Chapter 48
Learning and Memory
Brain Systems
Introduction
The topic of memory systems can be usefully framed by the question of whether a specific memory can be localized or whether it is distributed throughout the brain. Even early on there was evidence that the answer might lie somewhere between these extremes. In the 1920s, the psychologist Karl Lashley conducted a series of experiments in which he carefully damaged various areas in the cerebral cortex of rats that had learned a route through a simple maze. He found that the total amount of cortex damaged correlated with the severity of memory impairment, irrespective of the specific location of the brain damage. Lashley therefore concluded that memory was distributed throughout the brain. Yet a short time later, the neurosurgeon Wilder Penfield found that when he stimulated various areas of the brains of awake epileptic patients (in order to identify functional areas deemed too important to be removed during surgery to treat the epilepsy), stimulation in some brain regions led the patient to experience specific memories. He reasoned that, if focal stimulations bring specific memories to mind, then memories might be localized in specific brain regions. The psychologist Donald Hebb offered reconciliation by suggesting that thoughts and memories were supported by “cell assemblies,” or networks of neurons, and that experience resulted in changes in the connections between cells (Hebb, 1949). Thus, to the extent that a change at an individual juncture between cells (i.e., a synapse) recorded experience, memory was localized. Yet to the extent that full memories resulted from a reactivation of entire cell assemblies, memory was distributed.
The previous chapter discussed how synapses can be strengthened and weakened, and the current chapter considers how this synaptic plasticity supports behavior through memory systems, distributed cell assemblies involving a large number of interconnected neurons. A memory system is most usefully defined in both psychological and anatomical terms. That is, a memory system is most clearly identified by both a distinct neural circuitry and a unique set of operating characteristics. Memory systems are not separated according to stimulus modality (e.g., auditory vs. visual modality) or response modalities (e.g., manual vs. verbal responses). Instead, the critical distinctions involve how the biology of each system’s anatomy and physiology support a particular type of memory representation in computational, psychological, and behavioral terms.
The present chapter focuses on individual structures that play central roles in each of the different systems and includes separate sections on the hippocampus, striatum, cerebellum, amygdala, and cerebral cortex (Fig. 48.1). Each of these structures anchors a much larger network of distributed brain areas that work in concert to support particular forms of memory. For example, the hippocampal memory system involves not only the hippocampus but also areas in the adjacent parahippocampal region and is connected to many distributed unimodal and polymodal neocortical areas. These areas normally work together to support conscious recollection of facts and events, a capacity termed declarative memory. Even though declarative memory depends on very specialized information processing and plasticity within the hippocampus, a complicated and distributed circuitry outside the hippocampus is also required for these alterations to emerge as the experience of remembering. Importantly, the cerebral cortex plays a role in many types of memory, and the present chapter will discuss both its role as a part of multiple memory systems and the specific memory functions it supports outside of those systems. Finally, memory systems work in concert to support behavior in the course of one’s day to day activities, and this issue will be considered in the final section of the chapter.
Figure 48.1 Drawing of the human brain showing components of the major memory systems.
Hippocampus
Anatomy
The hippocampal memory system includes the hippocampus (defined here as the CA fields, dentate gyrus, and subiculum) and the entorhinal, perirhinal, and postrhinal cortices in the adjacent parahippocampal region (the postrhinal cortex is referred to as the parahippocampal cortex in primates; Fig. 48.2; Burwell, Witter, & Amaral, 1995; Suzuki, 1996). The anatomy and circuitry of these regions, especially the hippocampus, are largely conserved across mammalian species (Manns & Eichenbaum, 2007). The parahippocampal region serves as a convergence site for input from cortical association areas and mediates the distribution of cortical afferents to the hippocampus. These parahippocampal cortical areas are interconnected and send major efferents to multiple subdivisions of the hippocampus. Within the hippocampus, an intricate pattern of connectivity mediates a large network of associations (Fig. 48.2; Amaral & Witter, 2004), and these connections support forms of long term potentiation that could participate in the rapid coding of novel conjunctions of information (see Chapter 47). The outcomes of hippocampal processing are directed back to the adjacent cortical areas in the parahippocampal region, and the outputs of that region are directed in turn back to the same areas of the cerebral cortex that were the source of its inputs. Additional structures have also been included as components of this system, including midline diencephalic structures that are connected to the hippocampus via the fornix.
Figure 48.2 The anatomy of the hippocampal memory system in monkeys and rats. The hippocampal memory system includes the hippocampus (CA fields, dentate gyrus, and subiculum) and the parahippocampal region, which includes the entorhinal cortex, the perirhinal cortex, and the parahippocampal cortex. The hippocampal memory system also includes midline diencephalic nuclei. Multiple association areas in the cerebral cortex send outputs that converge on cortical areas in the parahippocampal region, which in turn sends its outputs to the hippocampus. The output path involves return projections from the hippocampus to the surrounding parahippocampal region, which in turn projects back to the same cortical association areas. The bottom image shows detailed entorhinal projections on a transverse section of the hippocampus (location of section indicated by dashed lines on the top image).
Top image from Eichenbaum (2001).
Patient H.M. and Amnesia
The study of the hippocampal memory system, and memory systems in general, began in earnest with a published case report about a man who underwent brain surgery to treat severe epilepsy and who became arguably the most famous patient in neuroscience (Scoville & Milner, 1957; Squire, 2009). This man, known as H.M., began experiencing seizures at the age of 10, and by his late twenties, he had developed a severe seizure disorder that interfered with his ability to live a normal life. Heavy doses of anticonvulsant medications did not stop the seizures from worsening, and the disorder eventually became debilitating. Acting on the idea that dysfunctional brain tissue in the temporal lobes might be causing the seizures, on September 1, 1953, the neurosurgeon William Scoville removed substantial tissue from the medial aspect of H.M.’s temporal lobes bilaterally, including the hippocampus and the parahippocampal cortical region (Fig. 48.3). In one sense, the surgery was a success. The frequency and severity of the seizures were reduced. In another sense, the outcome was tragic. On April 26, 1955, Brenda Milner, a colleague of both Penfield and Hebb, conducted a neuropsychological examination of patient H.M. His profound memory impairment was immediately obvious. H.M. gave the date as March 1953 and his age as 27 (two years younger than his actual age). He performed poorly on memory tests that involved short stories, word lists, pictures, and a wide range of other materials. Remarkably, it was unclear that he even remembered that he had undergone brain surgery. The severity of H.M.’s memory impairment was shocking: he showed almost no capacity for new learning. Yet the reason that H.M.’s case ushered in the modern era of research on memory stemmed from the appreciation that severe memory impairment could occur while other cognitive functions were intact.
Figure 48.3 Magnetic resonance images showing the brains of amnesic patients H.M. and E.P. The images show axial sections through the medial temporal lobes and reveal damaged tissue as a bright signal. H.M.’s damage resulted from surgery, and E.P.’s damage was caused by viral encephalitis. Nevertheless, the resulting lesion was similar for the two patients. Both patients sustained extensive damage to the medial temporal lobes and are profoundly amnesic (from Stefanacci, Buffalo, Schmolck, & Squire, 2000).
First, formal testing identified that H.M.’s IQ score was generally unaffected by the surgery, and a battery of tests found no deficits in perception, abstract thinking, or reasoning ability. These intact abilities indicated that memory could be separated from perception and intelligence. Second, H.M. could hold on to small amounts of information as long as he was actively rehearsing the information. This finding suggested that the ability to maintain information online (now usually referred to as working memory) was distinct from the ability to make a lasting record in the brain (see Chapter 50). Third, H.M.’s childhood memories were relatively intact. This finding suggested that, although the medial temporal lobes might be important for forming new memories, this region was unlikely to be the final storage site for memory. Fourth, H.M. had an intact ability to acquire new motor and perceptual skills (Milner, 1962). As an example, over several days of practice, H.M. gradually improved at tracing the outline of a star when viewing the paper only through a mirror (a task that is initially challenging even for healthy individuals) despite never forming a conscious memory for the testing experience. These results indicated that memories outside the scope of conscious recollection depended on structures outside the medial temporal lobe.
Patient H.M.’s memory impairment is referred to as amnesia, and it includes both an anterograde component—the impaired ability to acquire new information—and a temporally-graded retrograde component—the loss of information acquired soon before the onset of brain damage. Amnesic patients, including H.M., have contributed fundamentally to our current understanding of the hippocampal memory system (see Box 48.1 for a discussion of memory impairments in Alzheimer’s Disease). For example, one patient (R.B.) with damage largely restricted to one subfield of the hippocampus (CA1) showed that the hippocampus itself made essential contributions to memory (Zola-Morgan, Squire, & Amaral, 1986), although his memory impairment was more modest than H.M.’s. Perhaps most fundamental has been the repeated demonstration by numerous amnesic patients that the hippocampal memory system supports conscious recollection of facts and events (declarative memory), and that this type of memory is distinct from the type of memory supported by other brain systems, collectively referred to as nondeclarative memory. In most amnesic patients, the deficit in declarative memory encompasses all stimulus modalities and impacts nonverbal expression as well as verbal report whenever the memory task requires the explicit expression of memory, as in tests of free recall or recognition memory.
One profoundly amnesic patient, E.P., has a memory impairment and brain damage very similar to that of patient H.M. (Fig. 48.2). Patient E.P. has shown normal performance on a wide variety of nondeclarative memory tasks, despite the fact that his nearly complete anterograde amnesia has kept him from forming conscious memories of the testing situations themselves. For example, in one study, E.P. was shown eight pairs of objects one at a time, each pair containing a correct object and an incorrect object. He slowly learned to pick up the correct object in each pair and after 18 weeks was able to select the correct object almost every time (Bayley, Frascino, & Squire, 2005). When he was asked how he knew which object to pick up, he pointed to his head and replied, “It’s here somehow or another and the hand goes for it.” In addition to being unavailable to awareness and being very slowly acquired, E.P.’s memory for the objects appeared to lack the flexibility of that shown by healthy individuals, who had learned the task after only three sessions. When the task was changed by presenting all 16 objects together and by asking E.P. to sort them into correct and incorrect piles, his performance deteriorated precipitously. In contrast, healthy individuals performed nearly perfectly on the sorting task. In another study, over the course of 12 weeks, E.P. came to learn a list of 60 three-word nonsense sentences (e.g., “Speech caused laughter.”), such that he eventually was able to respond correctly some of the time when asked to fill in the missing third word (e.g., “Speech caused ???”; Bayley & Squire, 2002). Yet E.P. continually expressed surprise when informed that he had seen the sentences previously. On this task, his memory also appeared more rigid than that of control subjects. When the middle word of each sentence was replaced by a synonym, E.P. was able to answer correctly for only one sentence. By contrast, healthy individuals who learned the sentences after only two weeks had no trouble adapting to the new testing format. These studies with patient E.P. illustrate that memory supported by structures outside the hippocampal memory system is not limited to simple and reflexive behaviors, but can be complex and can include acquisition of new verbal information. These studies also highlight the characteristics of memory supported by the hippocampal memory system. Memories formed via the hippocampal system are typically rapidly acquired, flexible, and available to conscious recollection.
Box 48.1 Alzheimer’s Disease
Alzheimer’s Disease (AD) is the most common disorder of memory in humans, afflicting approximately one in eight adults over age 65 (Alzheimer’s Association, 2012). The only FDA-approved therapeutics for AD to date are acetylcholinesterase inhibitors and memantine, an NMDA-receptor antagonist, that provide palliative treatment. Since there is no disease-modifying treatment that slows or halts the progression of AD this represents one of the greatest healthcare crises to date. Two characteristic pathological hallmarks of AD are extracellular plaques of aggregated amyloid beta (Aβ) and intracellular neurofibrillary tangles (NFTs) composed of aggregated tau, a microtubule binding protein. Aβ is produced through the enzymatic cleavage (via secretases) of amyloid precursor protein (APP) and in some forms is toxic to neurons. Aβ can disrupt synaptic plasticity, including long-term potentiation. NFTs may compromise intracellular transport and—together with Aβ—kill neurons. Genetic studies from familial cases of AD have strongly pointed towards a causal role for amyloid in aggressive, early-onset forms of AD, with APP or secretase mutations sufficient to cause early-onset AD. However, it is important to realize that familial AD accounts for <1% of all AD cases, while >99% of AD cases are sporadic in nature. Finally, there are a variety of other pathophysiological processes also involved in AD including perturbations in energy metabolism, membrane traffic and signaling, oxidative stress, and inflammation. The precise sequence of events and their relative importance remain to be determined.
Neurodegeneration in AD progresses sequentially through certain brain structures and select subpopulations of vulnerable neurons (Holtzman, Morris, & Goate, 2011). AD almost invariably begins with an insidious, gradual decline in memory, specifically, with problems encoding new memories. Consistently impacted early are those neurons in layer II of the entorhinal cortex, which acts as a gatekeeper of information flowing to and from the hippocampus. Aβ and tau pathologies accumulate in a circuit-dependent manner, deafferenting the entorhinal cortical projections to the hippocampus, and disrupting the molecular mechanisms necessary to form new episodic memories. This results in patients becoming more forgetful and repeating conversations, losing their belongings, and often getting lost driving or otherwise spatially navigating. These initial disease symptoms reflect the increasing dysfunction of the medial temporal lobe memory system. Neuropsychological tests may be used to confirm memory loss and quantify problems of new learning, recall, and recognition memory for verbal and visual material. Additionally, functional MRI studies have demonstrated altered patterns of memory-related activation in the medial temporal lobe of individuals with prodromal AD. While the coding of nascent episodic memories is compromised in early AD, older semantic memories that have previously been consolidated in neocortex are relatively preserved.
As Aβ and tau pathologies become increasingly widespread with disease progression and lead to synaptic failure and neurodegeneration, early episodic memory impairment gives way to progressive deficits in other higher brain functions (Holtzman, Morris, & Goate, 2011). The number of affected neuronal circuits increases dramatically to impact frontal, cingulate, and parietal cortices such that executive function, language, perception, praxis, and visuospatial abilities become impaired. With advanced disease, damage to higher cortices can also disrupt skilled motor actions (praxis) and compromise long-term semantic memory. Beyond widespread cortical pathology, cholinergic basal forebrain (CBF) neurons along with neurons of the locus coeruleus (LC) and raphe nuclei are vulnerable. CBF degeneration contributes to AD symptoms including attention deficits, increased spatial memory decline and further impairment in the coding of new episodic memories. Loss of serotonergic raphe projections likely contributes to behavioral symptoms such as irritability, apathy, depression, mood lability, and aggression. Degeneration of LC neurons translates to noradrenergic transmission loss, which disrupts arousal, vigilance, sleep-wakefulness, working memory, and properly interpreting and responding to autonomic stressors. Additionally, LC degeneration can also perturb microglial function, yielding neurotoxic inflammation.
The pathophysiology of AD allows for several putative therapeutic avenues. At present, our systems-level understanding of AD has yielded pro-cholinergic treatment strategies including acetylcholinesterase inhibitors and NMDA antagonists for transmitter-based symptomatic treatments. Drug candidates are being advanced in clinical trials so that we might obtain the desperately needed therapeutics to slow or halt AD progression. A cellular and molecular understanding of AD has led to amyloid-based approaches including secretase inhibitors, disrupting Aβ aggregation and promoting amyloid clearance. Also under investigation are neurotrophin-based therapies to prevent the neurodegeneration of vulnerable neurons, anti-inflammatories, and other approaches. While much progress has been made in the mechanistic understanding of AD progression, progress in our circuit- and cellular-level understanding of functional deficits and therapeutic benefits in vivo has remained particularly challenging. Fully resolving this therapeutic impasse will likely require merging current histology and medicinal chemistry with increased systems-level understanding—through electrophysiology and imaging approaches—of how relevant neural circuits are being modulated pathologically and therapeutically in vivo so that our forward progress might be better informed.
Evan P. Lebois and Allan I. Levey
References
1. Alzheimer’s Association. 2012 Alzheimer’s disease facts and figures Alzheimer’s and Dementia. The Journal of the Alzheimer’s Association. 2012;8:131–168.
2. Holtzman DM, Morris JC, Goate AM. Alzheimer’s disease: The challenge of the second century. Science Translational Medicine. 2011;3(77):1–17.
Models of Anterograde Amnesia in Experimental Animals
The memory impairment in human amnesic patients has been closely modeled by the performance of monkeys with similar brain damage (Zola-Morgan & Squire, 1985). One key to establishing a model of amnesia in experimental animals was developing tasks suitable for monkeys that would test declarative memory. Figure 48.4 shows two of these memory tasks. Using one of these tasks, researchers found that monkeys with brain damage similar to H.M.’s performed poorly when they had to retain information about objects for more than a few seconds (Mishkin, 1978). Thus, monkeys with medial temporal damage, like humans with amnesia, have intact immediate memory but perform poorly when the memory demand increases over time. In H.M., the damage included the amygdala, the hippocampus, and the surrounding parahippocampal region. Studies in monkeys with restricted lesions to different structures in the medial temporal lobe have shown that the amygdala is not necessary for declarative memory. In addition, the severity of memory impairment depends on the extent and locus of damage within the medial temporal lobe. Damage limited to the hippocampus, or to its major connections through the fornix, produces only a modest impairment. By contrast, damage that includes the parahippocampal cortices produces severe amnesia. Thus, while the hippocampus is a critical component of the system (Zola et al., 2000), the perirhinal and parahippocampal cortical regions also make major contributions to memory. A focus of current research is to better understand the unique contributions of each of the structures within the hippocampal memory system.
Figure 48.4 Recognition memory tasks used for studies of memory in nonhuman primates. Both tasks are easily adapted for human participants as well. (A) The delayed nonmatching-to-sample task using unique objects as stimuli. The subject is initially presented with a single novel object as the sample and must displace the object. This is followed by a variable delay during which the subject cannot see any objects. In the subsequent recognition test, two objects are presented, one of which is the same as the sample and the other of which is novel. Correct performance requires the subject to recognize and avoid the sample object and instead choose the novel one to receive a food reward. (B) Visual paired comparison task. During the sample phase the monkey looks at two identical pictures. In the test phase, one of the sample pictures is represented along with a novel picture. Memory for the repeated picture is inferred by measuring the subject’s tendency to look away from the repeated picture and towards the new picture.
Studies in rodents with damage to the hippocampal memory system have also increased our understanding of the functions of the hippocampal memory system. Rats with damage to the hippocampus are typically impaired on maze-based tasks when the task depends on memory for space that is capable of being flexibly expressed. One example is place learning in the Morris water maze task (Morris, Garrud, Rawlins, & O’Keefe, 1982; Fig. 48.5). In this task, rats are trained to find a hidden escape platform submerged just below the surface in a pool of cloudy water. The rats are unable to see the platform and must learn the location of the escape site on the basis of cues that are visible on the walls of the room around the pool. Rats with hippocampal damage can learn to locate the escape platform when they start from the same location on every trial. However, their performance is poor when they are asked to demonstrate flexible memory for those spatial relationships by swimming to the remembered location of the submerged platform from a different start point. Consistent with the studies with patient E.P., this pattern of performance reflects a more rigid memory in the absence of a hippocampus.
Figure 48.5 The Morris water maze task. Early in training, rats search for the submerged platform for extended periods. After training the rat swims directly to the platform.
Other experimental evidence in rats indicates that the hippocampus is also critically involved in the organization and flexible expression of nonspatial memories. For example, in one experiment rats were trained to dig through scented sand to obtain a cereal reward (Bunsey & Eichenbaum, 1996; Fig. 48.6). Initially, both healthy rats and rats with hippocampal lesions learned a set of simple associations between pairs of odors. For example, rats learned to dig in odor B when presented with odor A and to dig in odor C when presented with odor B. Subsequently, the rats were given probe tests to determine the extent to which learned representations supported two forms of flexible memory expression. One of these tests, a test for transitivity, measured the ability to infer an association between two odors that shared a common associate. For example, having learned that odor A is associated with odor B and that odor B is associated with odor C, could they infer that A is associated indirectly with C? The other test, a test for symmetry, measured the ability to recognize associated odors when they were presented in the reverse of their training order. For example, if B is associated with C, is C associated with B? Intact rats showed strong transitivity and symmetry but rats with damage to the hippocampus showed no evidence of transitivity or symmetry. These findings, along with the findings from the water maze and from monkeys, suggest that the hippocampus contributes to rapidly acquired and flexibly expressed spatial and nonspatial memories just as it does in humans.
Figure 48.6 Associative transitivity and symmetry in paired associate learning. (A) On each training trial, one of two odors is presented as the sample. On the subsequent choice trial the animal must select the assigned associate, indicated by a “+”. (B) Outline of odor pairings used in training on two sequential sets of paired associates, plus stimuli used in tests for transitivity (C for A; Z for X) and symmetry (B for C; Y for Z).
Neural Activity that Supports Declarative Memory
Studies with humans and experimental animals with damage to the hippocampal memory system have identified the types of memory that depend on the integrity of the hippocampus and parahippocampal region. Complementary studies involving neurophysiological recording and imaging of neural activity in intact medial temporal lobe structures have demonstrated how the hippocampal memory system ordinarily functions in support of declarative memory. In particular, noninvasive functional brain imaging, including functional magnetic resonance imaging (fMRI), has become a key tool for understanding how the hippocampal memory system operates in the intact human brain. In one of the earliest studies, increased activity was observed in the hippocampus and parahippocampal region when participants viewed previously unseen photographs as compared to when participants viewed photographs that they had already seen on multiple occasions (Stern et al., 1996). Subsequent studies found that activity in the hippocampal memory system is often related to the success of memory encoding or retrieval. For example, in one study, participants saw a list of words one at a time while being scanned (Wagner et al., 1998). After the scanning session, participants were given a test to assess which words were remembered and which words were forgotten. The results indicated that activity in the hippocampal memory system during encoding (in this case, in the parahippocampal region) was greater for words that were subsequently remembered compared to words that were forgotten. More recent studies suggest that, in humans, the different cortical areas of the parahippocampal region may play distinct roles in memory, with the perirhinal cortex showing increased activity during the encoding of specific objects and the parahippocampal cortex showing increased activity during the encoding of locations (Buffalo, Bellgowan, & Martin, 2006; Litman, Awipi, & Davachi, 2009; Pihlajamaki et al., 2004). These findings suggest that potential functional differences among medial temporal lobe regions may be linked to distinct neuroanatomical connectivity from nonspatial and spatial processing streams to these regions (Suzuki & Amaral, 1994; Witter et al., 2000).
While neuroimaging studies allow for an identification of neural activity averaged across a few millimeters of brain tissue, intracranial neurophysiological recording techniques allow for an identification of neural activity at the level of individual neurons. Studies in which electrodes were surgically implanted in the brains of monkeys, rats, and even humans (in cases of patients preparing for neurosurgery) have shown that the spiking activity of individual hippocampal neurons corresponds with memory performance on numerous tasks. For example, evidence of novelty and familiarity signals was found in a study of monkeys performing a simple visual recognition memory task (Jutras & Buffalo, 2010), similar to the paired comparison task described above (Fig. 48.4B). Across trials, many hippocampal neurons showed stronger responses to novel stimuli than to repeated stimuli (Fig. 48.7). Further, the magnitude of these firing rate modulations was positively correlated with memory performance. Larger changes in firing rate between novel and repeated stimuli were significantly correlated with better recognition memory performance. Results from human recordings studies also suggest that hippocampal neurons can represent stimulus novelty or familiarity, independent of the specific stimulus (Rutishauser, Mamelak, & Schuman, 2006). Importantly, these results provide evidence linking hippocampal activity with recognition memory performance.
Figure 48.7 Recognition memory signals in the monkey hippocampus. (A) As monkeys performed a variant of the Visual Paired Comparison task, some hippocampal neurons showed a change in firing rate that reflected stimulus novelty. This neuron fired more when the pictures were novel (Left panel) compared to when they were repeated (Right panel). (B) Many neurons showed a greater modulation in firing rate by novelty for pictures that were better remembered (Left panel) compared to those that were not well remembered (Right panel).
Other studies have shown that hippocampal neurons can demonstrate stimulus-specificity. For example, neurons in the human hippocampus have been shown to fire preferentially for specific categories of images—for example, faces versus natural scenes (Kreiman, Koch, Fried, 2000) and when the participant viewed or recalled specific video clips (Gelbard-Sagiv, Mukamel, Harel, Malach, & Fried, 2008). Hippocampal neurons in monkeys have been shown to fire preferentially when specific objects (Naya & Suzuki, 2011) or visual scenes were presented to the monkey, and the firing rates of these neurons showed modulations in activity that correlated with the monkeys’ ability to learn to move their eyes to a particular place in the scene to receive a reward (Wirth et al., 2003). Studies in rats have also shown that hippocampal neurons sometimes fire preferentially for specific olfactory stimuli as well as combinations of these stimuli and the places where they occur, suggesting that hippocampal neurons represent important stimuli in the context in which they are remembered (Wood, Dudchenko, & Eichenbaum, 1999).
In addition to this activity related to memory for items, neurons with spatial correlates have been identified in the hippocampal memory system of several species of mammals, including humans (O’Keefe & Dostrovsky, 1971; Derdikman & Moser, 2010; also see Chapter 45). In particular, many neurons in the rat hippocampus, referred to as “place cells,” have clearly defined spatial receptive fields, or “place fields” (Fig. 48.8A). Activity of these cells reflects the constellation of auditory, olfactory, tactile, and visual cues that define the external world as well as the self-motion cues that track the rat’s path through that space. Yet in addition to these clear spatial correlates, activity of these cells also reflects factors such as goals and expectations, and a common view is that the type of spatial memory reflected by place cells in rats shares many commonalities with examples of memory in humans such as recall. Further, cells with location or direction correlates have been found in several regions interconnected with the hippocampus, leading to an understanding of how mental maps might be constructed in the brain. An important advancement in that goal was the identification of “grid cells” in a region of the medial entorhinal cortex, an area reciprocally connected with the hippocampus (Hafting, Fyhn, Molden, Moser, & Moser, 2005). The activity of one of these cells tiles a recording chamber in a geometrically regular grid pattern, and the overlapping receptive fields of the population of grid cells in a rat’s brain are thought to be an essential component of the rat’s mental map (Fig. 48.8B). An important question is how the circuitry of the hippocampus and entorhinal cortex (and other connected areas) enables the transformation of spatial information from a grid field to a place field (and back again). A further question is how nonspatial information is incorporated into these spatial representations to support remembering, for example, which item was encountered in a given location. These questions highlight the distributed nature of memory representations, and the answers could be relevant to general information processing principles in the mammalian hippocampal memory system.
Figure 48.8 An example of a place cell (A) and a grid cell (B) recorded from a rat hippocampal and entorhinal neuron, respectively. The gray lines show the path of a rat as it explored a square recording chamber, and the red dots show the locations along that path at which an example hippocampal (A) or entorhinal (B) neuron fired action potentials. The right panel (C) illustrates activity from multiple grid cells, and one idea is that place fields might relate to points of overlap from a collection of grid fields.
In addition to recording activity of individual neurons, electrode recordings can measure the summed activity of many neurons in the local field potential at a particular recording site (measured either noninvasively from the scalp or with electrodes implanted on the surface or deep in the brain). Using this approach, studies have demonstrated memory-related changes in coordinated network activity that is reflected through oscillations, the synchronized and rhythmic activity of many neurons. Neuronal ensembles often synchronize their activity at a particular frequency band, and oscillatory activity during memory tasks has been observed at theta-band (4 to 12 Hz) and at gamma-band (30–90 Hz) frequencies.
Gamma-band oscillations have been identified in neural recordings taken from patients who volunteered to participate while they were awaiting neurosurgery. In one experiment, patients were asked to study a list of words and subsequently were asked to freely recall as many words as possible. Interestingly, the power of gamma-band oscillations was greater in the hippocampus and parahippocampal regions during the encoding of subsequently recalled words compared to unrecalled words (Sederberg et al., 2007). Using a similar task, others showed that interaction between medial temporal lobe structures might be important for memory formation. In this study, Fell et al. (2001) demonstrated that synchronized activity between the hippocampus and parahippocampal region in the gamma-frequency band was higher during the encoding of subsequently recalled words compared to unrecalled words. Recently, the relationship between gamma-band synchronization and memory formation was extended to single hippocampal neurons. The activity of hippocampal neurons was recorded while monkeys performed a preferential looking task, similar to the visual paired comparison task. Hippocampal neurons showed enhanced gamma-band synchronization during the encoding of pictures that the monkeys later recognized well (Jutras, Fries, & Buffalo, 2009). The time-course of this enhancement was extremely similar in monkeys and humans, using a different behavioral paradigm, which suggests that gamma-band synchronization may reflect a basic mechanism for the neuronal interactions that are critical for successful memory encoding (Fig. 48.9).
Figure 48.9 Gamma-band synchronization in the medial temporal lobe during memory encoding is associated with the degree of subsequent recognition. (A) Gamma-band phase synchronization (coherence) between the human hippocampus and the rhinal cortex during word study, as a function of time from stimulus onset. Coherence was significantly higher during the encoding of words that are subsequently recalled (black) than for words that were not later recalled (gray). Error bars indicate SEM. Adapted by permission from Macmillan Publishers Ltd. from Fell et al., 2001. (B) Gamma-band spike-field coherence in the monkey hippocampus during the encoding of pictures, as a function of time from stimulus onset. Coherence was significantly higher for stimuli to which monkeys subsequently showed a high degree of recognition (red) than for stimuli which were not well recognized (blue). Red and blue shaded areas represent SEM. Gray shaded area represents time points at which gamma-band coherence was significantly different for the two conditions (p < 0.01).
Modified from Jutras et al. (2009).
Slower, theta-band oscillations are a ubiquitous feature of hippocampal activity in rodents, especially when the animals are actively exploring an environment, and theta-band oscillations have also been described in cats, bats, and even humans. Modulations in theta-band oscillations have also been linked to memory formation (Fuentemilla, Penny, Cashdollar, Bunzeck, & Duzel, 2010; Rutishauser, Mamelak, & Schuman, 2010). For example, Rutishauser et al. (2010) demonstrated that an increase in the tendency of hippocampal neurons to emit action potentials at a consistent phase of the theta-band oscillation was associated with better memory, indicated by higher retrieval confidence in human patients.
Oscillations play an important role in synchronizing activity among neurons within a region or between regions, and recent studies suggest that theta-band and gamma-band oscillations interact to coordinate the flow of information in the hippocampal memory system. For example, gamma-band oscillations have been observed to ride on top of theta-band oscillations, with a single theta wave containing several gamma waves. A recent study in rats found that the troughs of theta waves in CA1 tended to carry a type of fast (around 65–150 Hz) gamma oscillation also prominent in entorhinal cortex, whereas the downward slope following the peaks of the theta waves tended to carry slow (25–50 Hz) gamma oscillation also prominent in CA3 (Colgin et al., 2009). These results are consistent with the idea that inputs to CA1 from CA3 and entorhinal cortex (Fig. 48.3) are precisely timed by oscillatory activity in the hippocampal memory system in order to optimize memory performance (Hasselmo, Bodelon, & Wyble, 2002; Manns, Zilli, Ong, Hasselmo, & Eichenbaum, 2007). Indeed, action potentials occurring within a single gamma wave that is nested in a theta wave will occur within about 10 ms of each other, a time window that is optimal for synaptic plasticity (Bi & Poo, 1998). Thus, oscillations likely provide the brain with the means to coordinate the activity of large cell assemblies while also optimizing the plasticity within individual junctures of that network.
Transgenic Animals and the Dissection of Hippocampal Circuitry
Experimental animals (typically mice) with modified genes have become another important avenue for exploring the functional circuitry of the hippocampal memory system. Researchers have combined techniques in cell biology with selective breeding to produce transgenic animals that produce too much or too little of targeted proteins in specific parts of the brain. In addition, the altered protein production can be brought under control of drugs that the animals ingest, allowing the timing of the expression of the genetic manipulation to be controlled in addition to the location in the brain. One very fruitful approach to understanding how the hippocampus contributes to declarative memory has been to test the memory of mice that lack a protein important for synaptic plasticity or synaptic transmission in a specific type of cell (e.g., pyramidal cells in hippocampal area CA3). In this way, one can ask how impairing memory at a particular juncture at the synapse level will relate to memory impairments at the systems level. For example, a recent study used sophisticated techniques to produce a strain of mice in which synaptic transmission between CA3 and CA1 hippocampal neurons could be temporarily blocked (Nakashiba, Young, McHugh, Buhl, & Tonegawa, 2008). The combination of memory testing (e.g., in the Morris Water Maze) and neural recording in downstream CA1 pyramidal cells in these mice suggested that the entorhinal cortex input to area CA1 was sufficient for normal acquisition of spatial memory over several trials but that the CA3 input to CA1 was important for single-trial acquisition of spatial memory.
The combination of genetic approaches with animal models of amnesia and neuronal recording has started to sketch some of the roles of the structures within the hippocampus. Two important aspects of memory that have been proposed to differentially involve the dentate gyrus, CA1, and CA3 subregions of the hippocampus are termed “pattern separation” and “pattern completion.” Pattern separation refers to the process of distinguishing between two similar inputs and storing them as separate representations. By comparison, pattern completion refers to the process of filling-in partial or incomplete sensory input based on stored representations. There is accumulating experimental evidence and computational theory to support the idea that the dentate gyrus input to CA3 is a particularly important pathway for pattern separation, while CA3 neurons combine input from the dentate gyrus, the entorhinal cortex, and their own recurrent collaterals and project to CA1 in the process of pattern completion (Rolls, 2010; Yassa & Stark, 2011).
Retrograde Amnesia and Consolidation
So far, this chapter has focused primarily on the structures and neural signals that support the formation of new memories. However, patients with amnesia due to medial temporal lobe damage suffer not only a deficit in learning new material (anterograde amnesia), but also loss of memories that were acquired before the brain damage (retrograde amnesia). Importantly, the retrograde deficit is time-limited, and material acquired shortly before the damage is affected most severely, whereas items learned earlier in life are relatively spared. For example, the profoundly amnesic patient E.P. (Fig. 48.2) has almost no capacity for new declarative learning but shows intact memories from his childhood. In one study, E.P. was asked to think back to his childhood neighborhood and describe routes he would take from one location to another (e.g., his grade school to the town movie theater; Teng & Squire, 1999). In some instances, he was asked to imagine that a main street was blocked and to describe an alternate route. E.P. performed as well as a group of healthy individuals who had lived in the neighborhood at the same time and, like E.P. had moved from the area as young adults, long before the study. These data indicated that his memory for the spatial layout of his childhood neighborhood had escaped the damage to his medial temporal lobes. Consistent with his severe anterograde amnesia described earlier in this chapter, E.P. has been unable to learn the layout of the neighborhood he moved to subsequent to the onset of amnesia.
Retrograde amnesia is another aspect of the amnesic syndrome that has been studied in experimental animals. For example, studies have shown that simple object discriminations that were learned by monkeys shortly before medial temporal lobe damage are poorly retained, but discriminations learned remotely are spared. This pattern of memory impairment has been replicated in a variety of tasks in humans, monkeys, rabbits, rats, and mice and is thought to reflect a process of memory consolidation (Squire, Clark, & Knowlton, 2001).
The term consolidation has been used to characterize two kinds of brain events that affect the stability of memory after learning. One event involves the fixation of plasticity within synapses over a period of minutes or hours through a sequence of protein synthesis and morphological changes at synapses (see Chapter 47). The other event involves a reorganization of memories, which occurs over weeks to years following new learning. This prolonged consolidation occurs in the hippocampal memory system and is thought to involve interactions between the hippocampus, parahippocampal region, and the cerebral cortex. Several models have been proposed to account for how the hippocampus might interact with other brain regions over a prolonged period in memory consolidation (Alvarez & Squire, 1994; McClelland, McNaughton, & O’reilly, 1995). These models assume that widespread areas of the neocortex contain the details of the information that is to be remembered and that areas in the hippocampal memory system support the capacity to retrieve the memory during the period shortly after learning. Over time, areas in the hippocampus are thought to reactivate the cortical representations through repetition, rehearsal, or spontaneous activity. The reactivation is then thought to induce plasticity in cortical-cortical connections, and these connections are viewed as a possible network structure for the permanent storage and organization of the memory. In support of this idea, it was recently demonstrated that activity in the neocortex could predict the recall of specific memories. Polyn, Natu, Cohen, and Norman (2005) asked human subjects to first study three lists of items including photographs of famous faces, famous locations, and common objects. Subsequently, the subjects performed a free-recall test where they were asked to recall as many items from the study lists as possible. Functional MRI was used to examine neural activity during both the study episode and the free-recall period, and the data revealed patterns of activity immediately prior to the recall of specific items that were similar to activity observed as the subjects studied that category of items (e.g., faces vs. locations or objects).
Recent studies using molecular markers of neuronal activity and synaptic change have also reported evidence consistent with this idea. For example, one study of maze learning in mice observed that these markers were found in high numbers in the hippocampus one day after learning but their numbers were much lower 30 days later (Maviel, Durkin, Menzaghi, & Bontempi, 2004). By contrast, the same markers were found at low levels in the cerebral cortex initially after learning but were high in various cortical areas 30 days after learning; cortical areas included the prefrontal, anterior cingulate, and retrosplenial cortices. These results suggest that plasticity in the hippocampus is initially important for memory formation, but over time, plasticity in neocortical areas becomes more important.
There has long been interest in the role of sleep and dreaming in memory, and it has been suggested that processes might occur during sleep that promote the consolidation of hippocampus-dependent memories. Although the benefit of sleep for nonconscious learning is generally accepted, the status of hippocampus-dependent memory is less certain (Stickgold, 2005). Nevertheless, there are several results that indicate that processes during sleep might contribute to consolidation of hippocampus-dependent memory. For example, specific patterns of activity observed in the hippocampus while rats were awake were observed to “replay” during both REM sleep and slow wave sleep (Sutherland & McNaughton, 2000). Later studies linked hippocampal replay to sharp wave ripples (SWRs). SWRs are prominent during sleep and are recorded in the CA3 and CA1 regions as a transient (~100 ms) high-frequency oscillation (100–250 Hz). During SWRs, activity propagates from CA3 to CA1, and then out to the neocortex. This hippocampal-cortical interaction may be important for consolidation, and studies suggest that reactivation of memories during sleep may participate in the process of consolidation. SWRs occur also during the waking state, and hippocampal replay has been recently observed during SWRs as rats explored a novel environment. Interestingly, awake replay is more prevalent and temporally precise when rats are learning a novel environment, compared to when they are placed in a familiar environment (Diba and Buzsaki, 2007; Foster and Wilson, 2006). SWRs occur most often during slow-wave sleep, and another study showed that induction of slow-wave-like oscillations in the brains of human participants while they slept led to better retention in that less forgetting occurred across the interval (Marshall, Helgadottir, Molle, & Born, 2006). Thus, there is accumulating evidence regarding the role of sleep in the consolidation of hippocampus-dependent memory, but further work is needed to fully understand this process.
Summary of Hippocampus
Amnesia associated with damage to the hippocampal memory system in humans is characterized by an inability to form and consciously recollect memories of facts and events. Studies with animal models of amnesia have furthered our understanding of the function of the hippocampus and the parahippocampal region. Neurophysiological and neuroimaging studies have provided insight into the neural activity that may underlie declarative memory formation. Taken together, data obtained from a variety of techniques have indicated the importance of interactions within the hippocampal memory system as well as between the hippocampal memory system and the cerebral cortex in the formation and consolidation of declarative memories.
Striatum
Anatomy
The striatum is an important component of the basal ganglia (see Chapter 30), and its position within a distributed cortico-basal ganglia network is central to its function as a hub of a memory system. Sensorimotor, associative, and limbic cortical areas project to specific regions of the striatum (dorsolateral, dorsomedial, and ventral regions, respectively), and outputs of these regions loop through the pallidum to gain access to brainstem structures and back to cortical areas via the thalamus (Fig. 48.10). In addition, the striatum receives modulatory inputs from dopaminergic cells in the midbrain. Thus, the striatum receives information relevant to actions, external stimuli, and internal states (including expectations) and can influence behavior via brainstem pathways or by updating processing in cortical areas. See Chapters 30 and 41 for additional consideration of the role of the striatum in motor control and in reward and motivation, respectively.
Figure 48.10 Anatomy of the striatum and the rest of the basal ganglia. STN, subthalamic nucleus; GPe, external globus pallidus; GPi, internal globus pallidus; SNr, substantia nigra pars reticulata; SNc, substantia nigra pars compacta; VTA, ventral tegmental area.
From Yin and Knowlton (2006).
Identifying Memory Supported by the Striatum
Early efforts to understand the role of the striatum in memory often sought to distinguish its contributions from those of the hippocampal memory system. For example, one study compared performance on a simple T-shaped maze between rats with pharmacological inactivation of the hippocampus or dorsal striatum (Packard & McGaugh, 1996). During training, neither structure was inactivated, and all rats learned to run to a consistent choice arm (e.g., left for some rats) for a food reward when started at the base of the T-maze. During testing, the T-maze was rotated 180 degrees, and the question was whether a rat would continue to make left turns at the choice point (if it had been trained to run to the left arm), indicating memory resembling a habitual response (“response” memory), or would now make right turns, indicating a more flexible memory for the location of the food reward relative to the surrounding room (“place” memory; Fig. 48.11). The finding was that, after a week of training, most rats showed evidence of using place memory. Furthermore, inactivating the striatum during the test had little effect on performance, but inactivating the hippocampus led some rats to shift to a strategy that suggested the rats were compensating with response memory. Following the initial test, the T-maze was rotated back to its original position, and all rats received another week of training during which neither structure was inactivated. After this second week of training, the T-maze was again rotated 180 degrees, and the results on the second test differed substantially from the first. Most rats now showed evidence of relying on the response memory, and inactivation of the hippocampus had little effect on performance. Instead, inactivation of the striatum led many rats to alter their performance and again resort to place memory. These results suggested that, after initial training, performance normally relied on a memory for the location of the food that was flexible enough to be accessed despite rotation of the maze and that this type of place memory depended on the hippocampus. However, after more extended training, performance appeared to come under influence of a habitual response memory that depended on the striatum.
Figure 48.11 “Place” versus “response” learning. The rat is trained initially to turn left in order to obtain a reward at a particular location. In a subsequent test, the maze is rotated and the rat is allowed to select whether it will perform the same left turning “response” or remember the “place” of the previous reward.
These early results with rats were paralleled by a study that compared memory between amnesic patients with damage to the extended hippocampal memory system and patients with Parkinson’s disease (Knowlton, Mangels, & Squire, 1996), a disease that disrupts striatal function through degeneration of dopaminergic inputs from the substantia nigra. Both groups of patients and a group of healthy individuals were tested on a task in which they tried to guess on each trial whether the stimulus cards presented to them foreshadowed rain or sunshine (Fig. 48.12). The cards predicted rain or sunshine only in a probabilistic manner, and healthy individuals improved their performance from 50% (guessing) to about 70% only after 50 trials. Thus, the predictive value of the cards did not appear to be easily memorized, and performance instead appeared to rely on slowly acquired trial-and-error learning. Patient’s with Parkinson’s disease showed little improvement over the 50 trials, but amnesic patients performed about as well as the healthy individuals. In contrast, when the participants were subsequently asked questions about the nature of the task and the kinds of materials they had encountered, the patients with Parkinson’s disease performed as well as controls, and the amnesic patients showed very poor memory. Thus, results with humans agreed with results from experimental animals in suggesting that the striatum was part of a memory system that was distinct from the hippocampal memory system.
Figure 48.12 Weather prediction task. View of the computer screen presented to subjects showing all four stimulus cards and the “sun” or “rain” response choices.
From Knowlton et al. (1996).
Memory for Actions, Outcomes, and Habits
These initial studies suggested that the striatal memory system accumulated trial-and-error experiences as inclinations to make certain responses in the presence of particular stimuli and that these inclinations differed from the record of individual experiences supported by the hippocampus. Nevertheless, these studies also suggested that the memory supported by the striatum was too complex to be described fully as simple stimulus-response learning. Indeed, more recent research has investigated how the striatal memory system incorporates outcomes of individual actions into the stimulus-response association and how its initial participation in evaluation and decision making might give way to a role in habit-like responding with extended training.
Studies in which the activity of midbrain dopamine neurons was recorded have provided some clues about how consequences of actions could be incorporated into memory supported by the striatum (Schultz, 2007). These studies have indicated that these midbrain dopamine neurons increase their firing in response not only to rewards but also to secondary cues that predict the delivery of those rewards. In addition, these neurons also appear to signal a mismatch between expected outcome and actual outcome by, for example, an exaggerated response when an unexpected reward was delivered. Thus, the projections into the striatum from these midbrain regions could help to update inclinations or disinclinations to engage a particular response in the presence of particular stimuli based on whether the previous response was rewarded with a favorable outcome.
In addition, studies that have recorded activity in the striatum in both humans and experimental animals have indicated that the striatum likely incorporates these anticipated outcome signals (Graybiel, 2008). For example, a study in which neurons were recorded from the striatum as rats performed a more elaborate version of the T-maze task found that both the dorsal and ventral striatum were engaged at moments when a turning response was to be made but that the contributions of these regions seemed to differ according to whether it was early or late in training (van der Meer, Johnson, Schmitzer-Torbert, & Redish, 2010). In particular, some neurons from the ventral striatum showed increased activity at one of two possible reward locations (i.e., left or right). Some of these reward-related neurons also showed activity early on in training when the rat paused at a point on the maze when the rat needed to choose to turn left or right. Thus, early on in training, the ventral striatum appeared to gain access to prior favorable outcomes in selecting an action to perform in the same scenario again. In comparison, neurons in dorsal striatum showed increased activity unrelated to reward at turns on the maze and did so with more spatial accuracy later on in sessions. These results are consistent with the idea that striatal learning typically progresses from a probing action selection to a habitual response and that this deemphasizing of the outcome parallels a reorganization from a network centered on the ventral striatum to a network more centered in dorsal striatum. This idea has been extended to habits of many kinds, including drug addiction and other compulsive behaviors (see Chapter 41).
Summary of Striatum
The striatum is a key structure in a distributed cortico-basal ganglia memory system that supports the acquisition of inclinations to make specific responses in particular situations. The striatum contributes to an initial evaluative action-outcome learning and to a habitual stimulus-response learning after extended training. Recent results suggest that the ventral striatum might be particularly important for the former and that the dorsal striatum might be particularly important for the latter.
Cerebellum
Anatomy
The cerebellum consists of the cerebellar cortex, which has been divided into lobes and further into lobules, and several deep cerebellar nuclei (see Chapter 31 for more detailed anatomy). The cerebellum receives direct input from the spinal cord and brainstem as well as indirect input from a variety of sensory and motor areas in the cerebral cortex via pontine nuclei in the brainstem. The cerebellum projects directly to the spinal cord, brainstem, hypothalamus, and thalamus, and the thalamic targets in turn project to various motor and nonmotor areas in the cortex, particularly in the frontal lobes (Middleton & Strick, 1998). The internal circuitry of the cerebellum has been described as modular, with a local wiring diagram that is repeated again and again. This modularity raises the possibility that, although different regions of the cerebellum receive different input, each region might perform a similar set of operations on that information before passing the results back out to the rest of the nervous system (Boyden, Katoh, & Raymond, 2004).
Eyeblink Classical Conditioning as a Prototype of Cerebellum-Dependent Memory
The cerebellum is important for a range of sensorimotor functions, including adaptations of the vestibulo-occular response and coordinated motor skill learning. The cerebellum has also been associated with nonmotor learning and other examples of cognition and behavior. The present section focuses on the best-understood example of cerebellar-dependent learning, eyeblink classical conditioning, as a framework for highlighting the role of the cerebellum in memory based on the idea that the modular circuitry of the cerebellum offers a generalized capacity for processing of specific inputs.
In standard “delay” eyeblink classical conditioning, a conditioned stimulus (CS; e.g., a brief tone) is initiated immediately (typically a second or less) prior to the delivery of an unconditioned stimulus (US; e.g., a brief puff of air to the eye), and both stimuli then terminate at the same time. The US triggers a reflexive, unconditioned eyeblink (the UR), and after many pairings of CS and US, the CS eventually elicits an eyeblink as a conditioned response timed such that the blink is most robust at the US onset. Remarkably, rabbits with no cerebral cortex, basal ganglia, limbic system, thalamus, or hypothalamus showed normal retention of the conditioned eyeblink response (Mauk & Thompson, 1987), indicating that no forebrain structures were an essential part of the neural circuitry supporting standard eyeblink conditioning.
Numerous studies by Thompson and colleagues have traced the paths to the cerebellum taken by incoming sensory information about the CS and the US and have found that these paths converge on one particular cerebellar nucleus, the interpositus nucleus, and that outputs of this nucleus have a clear route to producing conditioned eyeblink responses (Fig. 48.13; Thompson, 2005). These experiments were able to capitalize on the well-defined circuitry to show that the interpositus nucleus was a locus of plasticity essential for acquiring and retaining a CS-US association capable of supporting conditioned blinks in response to the CS in a manner that was dissociable from the ability to emit unconditioned blinks in response to the US. Additional studies have indicated that the cerebellar cortex is an important part of the circuitry for modulating the timing of the conditioned response to ensure that it is adaptive.
Figure 48.13 A schematic diagram of principal pathways involved in classical conditioning of the eyeblink reflex. The role of structures at points a–e has been studied using reversible inactivation with a local anesthetic. Inactivation at point “c” (shaded areas) prevents learning, whereas inactivation at “a,” “b,” “d,” or “e” prevents the behavioral response during inactivation, but does not block learning.
From Thompson and Kim (1996).
Delineation of the functional circuitry of eyeblink classical conditioning, along with results from other paradigms and from computer simulations, has provided a view of the cerebellum and as a key part of a circuit capable of finely recalibrating the sub-second timing of outputs based on the precise timing of inputs (Ohyama, Nores, Murphy, & Mauk, 2003). By this view, the “error” signal of a puff of air to an open eye is used to recalibrate the timing of an eyeblink with the help of the predictive value of the CS so that the next puff might be received by a closed eye. The capacity of the cerebellum and associated circuitry to recalibrate subsecond timing may contribute in a similar manner to other examples of learning, such as in the recalibration of the timing of contraction of opposing arm muscles in executing coordinated reaching movements such that a missed object can be successfully grasped.
Summary of cerebellum
The circuitry essential for eyeblink classical conditioning has been identified and provides an experimentally tractable opportunity to investigate what may be generalized learning processes supported by the cerebellum. These processes may contribute generally to recalibrating the timing of outputs in sensorimotor learning or even more generally to mental processing involved in precise timing.
Amygdala
Anatomy
Two major areas of the amygdala involved in emotional memory are the basolateral complex of the amygdala (BLA; includes the lateral, basolateral, and basomedial nuclei) and the central nucleus of the amygdala (CEA; Fig. 48.14B). The BLA receives input from widespread cortical areas as well as from sensory nuclei of the thalamus and thus has access to higher-level information from association areas as well as lower-level sensory information. In addition, the BLA has reciprocal connections with other brain systems, including the hippocampal and striatal memory systems. Areas within the BLA project to the central nucleus, which is the source of outputs to subcortical areas controlling a broad range of fear-related behaviors, including autonomic (e.g., changes in heart rate, blood pressure, sweating, or hormone release) and motor responses (e.g., fear-related freezing behavior).
Figure 48.14 Fear conditioning. (A) Prior to training, the tone produces a transient orienting response. During training the tone is followed by a brief foot shock. Following training, the rat is reintroduced into the chamber and freezes when the tone is presented. (B) Anatomical pathways that mediate fear conditioning. A hierarchy of sensory inputs converges on the lateral amygdala nucleus, which projects to other amygdala nuclei and then to the central nucleus, which send outputs to several effector systems for emotional responses. BNST, bed nucleus of the stria terminalis; DMV, dorsal nucleus of the vagus; NA, nucleus ambiguus; RPC, nucleus reticularis pontis oralis; RVL, rostral ventral nucleus of the medulla.
From LeDoux (1995).
The amygdala contributes to emotion in several ways, including mediating emotional influences on attention and perception and regulating emotional responses (Phelps & LeDoux, 2005). The topic of the present section is the two ways in which the amygdala contributes to emotional memory. First, the amygdala supports the acquisition of emotional dispositions towards stimuli. Second, the amygdala mediates the influence of emotion on the consolidation of memory in other memory systems.
Role of the Amygdala in Acquiring Emotional Dispositions to Stimuli
A particularly productive approach to studying the role of the amygdala in memory has been to focus on the circuitry that supports acquisition of fearful responses to simple auditory or visual stimuli (Fanselow & Gale, 2003; Lang & Davis, 2006; Paré, Quirk, & Ledoux, 2004). In one frequently used task (Fig. 48.14A), rats are initially habituated to a chamber and are then presented with multiple pairings of a tone (the CS) that terminates with a brief electric shock delivered through the floor of the chamber (the US), which typically results in an unconditioned jumping response (the UR). Subsequently, conditioned fear elicited by the tone is assessed by measuring autonomic responses or motor responses (the CR), such as freezing behavior. Rats with lesions in the BLA show markedly reduced conditioned fear responses to the tone but still show normal unconditioned fear responses to the shock itself. Rats with lesions to the central nucleus show reduced fear responses to both conditioned and unconditioned stimuli. Thus, the BLA appears to be important for acquiring an aversion to the tone, whereas the central nucleus appears to be additionally important for producing fearful responses in general.
A study in which fear conditioning was adapted for use in patients with damage to the amygdala or the hippocampus verified that the amygdala is crucial for acquiring conditioned fear responses and found further that these learned aversions are independent of declarative memory for the learning event (Bechera et al., 1995). In addition, functional imaging studies in healthy individuals have found increased activity in the amygdala for both positive and negative stimuli as compared to neutral stimuli (Vytal & Hamann, 2010), consistent with the view from experimental animals that, although its involvement in fear learning may be especially robust, the amygdala is important for acquiring both aversions and preferences to previously neutral stimuli (Morrison & Salzman, 2010).
Similar to the example of eyeblink classical conditioning and the cerebellum, identifying the confluence of CS and US pathways in the amygdala has provided opportunities for exploring the neurobiological details of emotional memory at a key locus of plasticity. These opportunities are also relevant to mental health issues including anxiety, phobias, and post-traumatic stress disorder, and a recent area of interest in emotional memory has been how the brain supports relearning that a stimulus no longer predicts a fear-inducing outcome (Jovanovic & Ressler, 2010). These studies suggest that the medial prefrontal cortex may play an important role by inhibiting activity in the amygdala in order to suppress previously learned fear responses and by mediating plasticity so that a new non-fearful disposition can be associated with the stimulus (Quirk & Mueller, 2008; Quirk et al., 2010).
Role of the Amygdala in the Modulation of Memory
Emotion can enhance memory, and evidence from a large number of studies in experimental animals has indicated that the amygdala, in particular the BLA, mediates this memory enhancing effect (McGaugh, 2004). In particular, emotionally arousing events result in the release of epinephrine and glucocorticoids by the adrenal glands, which in turn result in the release of norepinephrine in the amygdala (Fig. 48.15). The release of norepinephrine leads to an increase in activity in the amygdala, which is thought to influence consolidation of memory in the other parts of the brain, either directly, through connections of the amygdala with the striatum, hippocampus, and cortex, or indirectly, through connections with nucleus basalis (which innervates much of the cortex). Data in support of this model include the findings that damage to the BLA or infusions of drugs in the BLA that interfere with norepinephrine block the memory enhancing effect of emotional arousal. In addition, infusion of norepinephrine directly into the BLA following a learning session can enhance subsequent memory, even in instances such as object recognition memory in which emotional arousal ordinarily does not make an essential contribution to the memory (Roozendaal, Castello, Vedana, Barsegyan, & McGaugh, 2008). Furthermore, the modulatory influence of the BLA seems to be limited to the time of the learning event as well as a short period of time after. Manipulations of the amygdala during subsequent testing of retention typically do not influence memory performance. Thus, the amygdala is important in modulating storage in emotionally stressful situations, but not in the maintenance or retrieval of the memory that has been modulated.
Figure 48.15 A schematic representation of how hormonal systems and the amygdala complex can modulate the storage of memory for emotionally arousing events through influences on other brain systems. See text for details.
From McGaugh, Introini-Collison, Cahill, Kim, and Liang (1992).
The amygdala has been strongly implicated in the enhancement of memory for emotional material in humans as well as in experimental animals. In one study, a patient with damage to the amygdala was tested for memory of an emotional story along with a group of healthy individuals (Adolphs, Cahill, Schul, & Babinsky, 1997). The participants watched a series of slides and listened to an accompanying narrative that told a story about a mother and son. One portion of the story involved a traumatic accident, and all participants rated this portion as being strongly emotional. Compared to the healthy individuals, the patient with damage to the amygdala failed to show enhancement of memory for the emotional part of the story. However, the patient performed as well as others on neutral story material. In brain imaging studies with healthy individuals, the amygdala was activated during the viewing of emotional material that elicited either positive or negative emotional reactions, and this activation was related to the likelihood that participants were able to subsequently remember the emotional material (Cahill et al., 1996; Canli, Zhao, Brewer, Gabrieli, & Cahill, 2000; Hamann, Ely, Grafton, & Kilts, 1999). In these studies, there was no correlation between activation of the amygdala and subsequent memory performance for neutral material.
Summary of Amygdala
The amygdala is well situated anatomically to attach emotional (positive and negative) dispositions to a broad range of stimuli and to mediate the acquisition of such dispositions in the absence of conscious recollection of the circumstances of the emotional experience. In addition, components of the amygdala also mediate the modulation of memory storage during and after emotional events. These two roles in memory could be brought together in situations in which, for example, a conditioned fear response to a stimulus leads to emotional arousal and to modulation of declarative memory for the event.
Cerebral Cortex
Anatomy
The posterior half of the cerebral cortex contains many functionally distinct areas that are organized into hierarchies of serial and parallel processing for each sensory modality (see Chapter 2). The anterior cerebral cortex contains a similar hierarchy of motor areas, as well as association areas in the prefrontal cortex involved with motor planning, higher order cognition, and working memory (Fuster, 2001; see Chapter 50). Additionally, information from different modalities converges on “association” areas in the parietal and temporal lobes.
Some of the examples provided so far, such as standard eyeblink classical conditioning, illustrate that some simple forms of learning can circumvent the cerebral cortex. Nevertheless, for all the memory systems discussed in this chapter, including the cerebellum, the cerebral cortex provides important access to incoming information as well as output routes to influence behavior. Yet plasticity in the cerebral cortex also directly supports types of memory that do not require components of the other memory systems. For each area of the cortex, the modification of its information processing circuitry through alterations in synaptic connectivity and membrane excitability underlies its direct participation in memory. These alterations can be dramatic, such as in examples of experience-dependent reorganization that occurs in perceptual or motor learning, but can also be more subtle, such as in the phenomenon of repetition priming.
Perceptual and Motor Learning
Extended experience with a particular set of stimuli or particular set of movements can result in the modification of the cortical areas important for perceiving those stimuli or executing those movements. In particular, studies in a variety of experimental animals have used single-neuron recordings to identify the stimulus-selectivity or movement-selectivity of individual neurons and to map their location in the cortex. A common finding in these studies is that training on a particular perceptual or motor task, such as a fine-grained auditory or tactile discrimination or a skilled reaching task, leads to an increase in the area of the cortex containing neurons that are selective for the stimuli or movements used in the task (Buonomano & Merzenich, 1998; Monfils, Plautz, & Kleim, 2005; Weinberger, 2007). In some of these studies, cortical changes were correlated with task performance, suggesting that these cortical alterations were an important part of the acquired memory.
Functional imaging studies in humans have also suggested that the cortical areas important for perceiving the stimuli or executing the actions are also important areas of plasticity. For example, in one study participants were scanned while viewing pictures of nonsense objects both before and after performing a perceptual discrimination task that included half of the objects (Op de Beeck, Baker, DiCarlo, & Kanwisher, 2006). Following the perceptual discrimination task, the pattern of brain activity in object-responsive areas in visual cortex had changed for the objects included in the discrimination task but did not differ for the objects not included in the discrimination task. Thus, it appears that many of the areas in the cerebral cortex that are specialized for specific perceptual or information processing functions—from areas involved in perceiving simple tones to areas involved in perceiving complex objects—are capable of supporting perceptual learning for those stimuli.
Repetition Priming
Another example of memory thought to be supported directly by the cerebral cortex is the phenomenon known as repetition priming (or priming). Priming involves repeating previously presented items, either unaltered or as fragments of the original (e.g., word fragments or briefly flashed pictures), and measuring the increased fluency with which those items are identified as compared to items not repeated. For example, in one task, repeated and unrepeated words are flashed on a screen so briefly that many of the words are unreadable, and the measure of priming is the increased likelihood of identifying repeated words as compared with unrepeated words. Studies with amnesic patients and healthy individuals have found that successful priming is typically independent of the ability to recognize which items were repeated (Stark & Squire, 2000). Further, functional imaging studies in healthy individuals suggest that priming often results in reduced activity in cortical areas involved in initially processing the stimuli (Schacter, Wig, & Stevens, 2007), suggesting that less neural activity is required to identify words recently processed. These findings suggest that the neuronal basis of priming is an increase in the efficiency and bias in the direction of cortical sensory processing associated with perceptual identification.
Summary of Cerebral Cortex
The cerebral cortex provides a major route of information flow into and out of all of the memory systems discussed in this chapter. However, the cortex is more than just a way station. It is highly plastic, and distinct cortical areas are reorganized or biased by specific training experiences to meet demands for specific types of perceptual or motor representations.
Behavior Supported by Multiple Memory Systems
The discussion of memory systems so far has focused on dissociations between systems in illustrating the unique contributions of the central structure in each system. However, behavior is a product of the entire nervous system and is typically guided by contributions of more than one form of memory. For example, training in a sport normally results in declarative memory of instructions from one’s coach but also leads to motor skill learning from repetitive practice. The memory for the instructions would depend on the hippocampus and various aspects of the skill would depend on the striatum, cerebellum, and motor cortex. The amygdala would likely also contribute during moments of emotional arousal. The following section considers emerging data regarding how the memory systems discussed so far might interact and illustrates some progress in understanding how the distribution of memory throughout the brain ordinarily contributes to our day to day activities.
One example of a direct collaboration between two memory systems is the influence of the amygdala on hippocampus-dependent memory for emotion-inducing material. The section earlier in the chapter on the amygdala discussed how emotional arousal leads to the amygdala engaging in modulation of declarative memory, such that stimuli that elicit either positive or negative emotional responses tend to be remembered better than neutral stimuli. This process is thought to result from amygdalar projections modulating synaptic plasticity either in the hippocampus and parahippocampal region or in cortical areas fundamental to the hippocampal memory system. It has been suggested that the amygdala also modulates other memory systems, such as the striatal memory system, and that the amygdala may play a generalized role in memory modulation for emotion inducing stimuli in addition to its role in attaching positive or negative dispositions to stimuli (McGaugh, 2004).
Another example comes from an indirect collaboration between two very dissimilar memory systems, the hippocampal memory system and the cerebellar memory system. The section earlier in the chapter on the cerebellum detailed that the essential circuitry for standard eyeblink classical conditioning involved no forebrain structures and instead centered on deep cerebellar nuclei. Yet the situation changes if the parameters for standard eyeblink classical conditioning, referred to as delay eyeblink conditioning, are altered slightly such that the tone that normally overlaps with the airpuff is moved earlier in time such that it terminates as little as a half second prior to the onset of the airpuff, a version called trace conditioning (Christian & Thompson, 2003). Trace eyeblink conditioning depends on the same cerebellar and hindbrain circuitry as delay eyeblink conditioning, but trace conditioning depends additionally on forebrain structures including the hippocampus and prefrontal cortex. In studies with healthy human participants, trace (but not delay) eyeblink conditioning was found to relate closely to participants’ knowledge about the relationship between the tone and the airpuff (Clark, Manns, & Squire, 2002). A possible explanation of these findings is that the silent interval that separates the tone and airpuff may surpass the learning abilities of the cerebellum and may cause it to require inputs from additional forebrain structures, such as the hippocampal system via the prefrontal cortex (Kalmbach, Ohyama, Kreider, Riusech, & Mauk, 2009), to bridge that brief interval. The growing understanding of the relationship between hippocampus and cerebellum illustrates the tendency to collaborate between even the most dissimilar memory systems, and exemplifies the progress being made towards understanding how memory systems interact in general.
In other examples, memory systems may not directly collaborate but may operate in parallel. For example, the previously discussed studies contrasting the role of the striatum and hippocampus in supporting “response” learning and “place” learning, respectively, illustrate how the memory supported by different brain systems may not converge. Yet in the example of practicing a sport, the hippocampus-dependent memory for a coach’s instructions and the striatal “response” learning would likely support a common behavior. In these instances, it is thought that the striatum and hippocampus contribute to behavior in parallel rather than directly interacting.
Conclusion
Several brain systems support distinct forms of memory characterized by unique operating features and ways in which memory is expressed. The hippocampus supports rapidly-acquired memory that is experienced as conscious recollection and that can be expressed flexibly. The striatum supports acquisition of inclinations to make situational responses. The cerebellum supports learning related to adaptive timing and motor reflexes. The amygdala supports acquisition of emotional dispositions towards stimuli and also mediates the role of emotion arousal in modulating other forms of memory. The cerebral cortex participates in each of these types of memory, and mediates perceptual learning, motor learning, and the phenomenon of priming. These systems also often operate in concert to support the various ways that behavior and mental life can be modified by experience.
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Suggested Readings
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