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Wendy A Suzuki

Publications and source records attributed to Wendy A Suzuki.

16 recordsLinked to original sources
PubMed2006

Bayesian analysis of interleaved learning and response bias in behavioral experiments.

Accurate characterizations of behavior during learning experiments are essential for understanding the neural bases of learning. Whereas learning experiments often give subjects multiple tasks to learn simultaneously, most analyze subject performance separately on each individual task. This analysis strategy ignores the true interleaved presentation order of the tasks and cannot distinguish learning behavior from response preferences that may represent a subject's biases or strategies. We present a Bayesian analysis of a state-space model for characterizing simultaneous learning of multiple tasks and for assessing behavioral biases in learning experiments with interleaved task presentations. Under the Bayesian analysis the posterior probability densities of the model parameters and the learning state are computed using Monte Carlo Markov Chain methods. Measures of learning, including the learning curve, the ideal observer curve, and the learning trial translate directly from our previous likelihood-based state-space model analyses. We compare the Bayesian and current likelihood-based approaches in the analysis of a simulated conditioned T-maze task and of an actual object-place association task. Modeling the interleaved learning feature of the experiments along with the animal's response sequences allows us to disambiguate actual learning from response biases. The implementation of the Bayesian analysis using the WinBUGS software provides an efficient way to test different models without developing a new algorithm for each model. The new state-space model and the Bayesian estimation procedure suggest an improved, computationally efficient approach for accurately characterizing learning in behavioral experiments.

Bayes Theorem↗
PubMed2006

Encoding new episodes and making them stick.

How do we encode, store, and retrieve new episodic memories, and what are the computations performed by the hippocampus during this process? One system that has been used to model the brain basis of episodic memory in humans is the study of spatial navigation by path integration in rodents. Here I discuss three exciting new findings focused on encoding or replay of spatial sequences in the rat hippocampus. These findings not only provide important new insight into the computations associated with encoding and consolidation of spatial trajectories, but may also have implications for understanding key aspects of human episodic memory.

Animals↗
PubMed2005

Functional magnetic resonance imaging activity during the gradual acquisition and expression of paired-associate memory.

Recent neurophysiological findings from the monkey hippocampus showed dramatic changes in the firing rate of individual hippocampal cells as a function of learning new associations. To extend these findings to humans, we used blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to examine the patterns of brain activity during learning of an analogous associative task. We observed bilateral, monotonic increases in activity during learning not only in the hippocampus but also in the parahippocampal and right perirhinal cortices. In addition, activity related to simple novelty signals was observed throughout the medial temporal lobe (MTL) memory system and in several frontal regions. A contrasting pattern was observed in a frontoparietal network in which a high level of activity was sustained until the association was well learned, at which point the activity decreased to baseline. Thus, we found that associative learning in humans is accompanied by striking increases in BOLD fMRI activity throughout the MTL as well as in the cingulate cortex and frontal lobe, consistent with neurophysiological findings in the monkey hippocampus. The finding that both the hippocampus and surrounding MTL cortex exhibited similar associative learning and novelty signals argues strongly against the view that there is a clear division of labor in the MTL in which the hippocampus is essential for forming associations and the cortex is involved in novelty detection. A second experiment addressed a striking aspect of the data from the first experiment by demonstrating a substantial effect of baseline task difficulty on MTL activity capable of rendering mnemonic activity as either "positive" or "negative."

Adult↗
PubMed2005

Behavioral and neurophysiological analyses of dynamic learning processes.

In this article, the authors address two topics relevant to the study of the brain basis of associative learning. In Part 1, they compare and contrast the patterns and time course of dynamic learning-related neural activity that have been reported in the medial temporal lobe, premotor cortex, prefrontal cortex, and striatum during various associative learning tasks. In Part 2, they examine the statistical methodologies that have been used to analyze both behavioral learning and learning-related neural activity. They describe a state-space model of behavioral learning that provides accurate estimates of dynamic learning processes and a point-process filter algorithm that tracks the dynamic changes in neural activity on a millisecond time scale. Future challenges for these statistical methodologies and their application to the study of the brain basis of associative learning are discussed.

Association Learning↗
PubMed2004

Entorhinal cortex lesions disrupt the relational organization of memory in monkeys.

Recent accounts suggest that the hippocampal system critically supports two central characteristics of episodic memory: the ability to establish and maintain representations for the salient relationships between experienced events (relational representation) and the capacity to flexibly manipulate memory (flexible memory expression). To test this proposal in monkeys, intact controls and subjects with bilateral aspiration lesions of the entorhinal cortex were trained postoperatively on two standard memory tasks, delayed nonmatchingto-sample (DNMS) and two-choice object discrimination (OD) learning, and three procedures intended to emphasize relational representation and flexible memory expression: a paired associate (PA) task, a transitive inference (TI) test of learning and memory for hierarchical stimulus relationships, and a spatial delayed recognition span (SDRS) procedure. The latter assessments each included critical "probe" tests that asked monkeys to evaluate the relationships among previously learned stimuli presented in novel combinations. Subjects with entorhinal cortex lesions scored as accurately as controls on all phases of DNMS and OD, procedures that can be solved on the basis of memory for individual stimuli. In contrast, experimental monkeys displayed deficits relative to controls on all phases of the PA, TI, and SDRS tasks that emphasized the flexible manipulation of memory for the relationships between familiar items. Together, the findings support the conclusion that the primate hippocampal system critically enables the relational organization of declarative memory.

Animals↗
PubMed2004

Representation of well-learned information in the monkey hippocampus.

In the neocortex, extensive training results in enhanced neuronal selectivity for learned stimuli relative to novel stimuli. This enhanced selectivity has been taken as evidence for learning-related plasticity. Much less is known, in contrast, about the representation of well-learned information in the hippocampus. In this study, we examined the responses of individual hippocampal neurons to well-learned and novel stimuli presented in the context of an associative learning task. There was no difference in the response magnitude or visual response latency of hippocampal neurons to the well-learned and novel stimuli. In contrast, hippocampal neurons responded significantly more selectively to the well-learned stimuli relative to the novel stimuli. These findings show that hippocampal cells, like neocortical cells, show greater selectivity to well-learned stimuli compared to novel stimuli.

Animals↗
PubMed2004

Perirhinal and parahippocampal cortices of the macaque monkey: Intrinsic projections and interconnections.

We investigated the topographic and laminar organization of the intrinsic projections and interconnections of the macaque monkey perirhinal and parahippocampal cortices. Discrete anterograde tracer injections placed at various rostrocaudal and mediolateral levels in these cortices revealed extensive associational connections both within and between the perirhinal and parahippocampal cortices. Areas 35, 36rm, 36rl, 36cm, and 36cl are highly interconnected, whereas area 36d (encompassing the dorsal portion of the medial temporal pole) shares only modest connections with the rest of the perirhinal cortex. Areas TH, TFm, and TFl of the parahippocampal cortex also share an extensive network of associational connections that tend to be heaviest within a given subdivision. Area 36c of the perirhinal cortex is the main interface between the perirhinal and parahippocampal cortices. Its heaviest connections are with area 36r and the anterior aspect of area TF. The laminar organization of all these connections is typical of associational projections. Anterograde tracer experiments revealed that these projections are distributed through both deep and superficial layers, although heavier projections are directed toward the superficial layers. Results of retrograde tracer experiments suggested that the projections from caudal areas (36c or TF) to area 36r are of the feedforward type, whereas the projections from areas 36r and 36c to area TF are of the feedback type. These findings suggest that the perirhinal cortex is at a higher level than the parahippocampal cortex in the hierarchy of associational cortices. We discuss the functional implications of the organization of these extensive networks of intrinsic, associational projections.

Animals↗
PubMed2004

Dynamic analysis of learning in behavioral experiments.

Understanding how an animal's ability to learn relates to neural activity or is altered by lesions, different attentional states, pharmacological interventions, or genetic manipulations are central questions in neuroscience. Although learning is a dynamic process, current analyses do not use dynamic estimation methods, require many trials across many animals to establish the occurrence of learning, and provide no consensus as how best to identify when learning has occurred. We develop a state-space model paradigm to characterize learning as the probability of a correct response as a function of trial number (learning curve). We compute the learning curve and its confidence intervals using a state-space smoothing algorithm and define the learning trial as the first trial on which there is reasonable certainty (>0.95) that a subject performs better than chance for the balance of the experiment. For a range of simulated learning experiments, the smoothing algorithm estimated learning curves with smaller mean integrated squared error and identified the learning trials with greater reliability than commonly used methods. The smoothing algorithm tracked easily the rapid learning of a monkey during a single session of an association learning experiment and identified learning 2 to 4 d earlier than accepted criteria for a rat in a 47 d procedural learning experiment. Our state-space paradigm estimates learning curves for single animals, gives a precise definition of learning, and suggests a coherent statistical framework for the design and analysis of learning experiments that could reduce the number of animals and trials per animal that these studies require.

Algorithms↗
PubMed2003

Episodic memory signals in the rat hippocampus.

How does the hippocampus signal memory for episodes? In this issue of Neuron, Ferbinteanu and Shapiro show that classic place cell activity in the rat hippocampus together with robust retrospective and prospective memory signals reflects the sequence of past, present, and future events that make up an episode.

Animals↗
PubMed2003

Perirhinal and parahippocampal cortices of the macaque monkey: cytoarchitectonic and chemoarchitectonic organization.

Findings from recent tract-tracing studies examining the cortical and subcortical connectivity of the medial temporal lobe showed that the pattern of connections of areas TH and TF of the parahippocampal cortex were consistent with previous boundary demarcations of this region. In contrast, the connections of the perirhinal cortex (areas 35 and 36) indicated that the border of area 36 should be placed several millimeters more lateral than in earlier descriptions in the literature. The connections of this region also suggested that the perirhinal cortex extends rostrodorsally to include the medial portion of what is typically referred to as the temporal pole (areas TG, 38, or Pro). To determine if cyto- and chemoarchitectonic characteristics are consistent with the boundary scheme suggested by our tract-tracing studies, we carried out a detailed analysis of Nissl- and SMI-32-stained material throughout the perirhinal and parahippocampal cortices of the macaque monkey. The staining patterns seen in both these preparations are in excellent agreement with the boundaries defined by earlier connectional studies. Based on these studies, we recognize areas 35 and area 36 of the perirhinal cortex and area 36 contains five subdivisions. The parahippocampal cortex is composed of areas TH and TF and area TF contains two subdivisions. For both the perirhinal and parahippocampal cortices, we provide descriptions of the cytoarchitectonic and chemoarchitectonic features that are most useful for defining each cortical subdivision, as well as the features most useful for defining the boundaries with adjacent cortical regions. We discuss these findings in the context of the results of previous tract-tracing studies.

Animals↗
PubMed2003

Single neurons in the monkey hippocampus and learning of new associations.

The medial temporal lobe is crucial for the ability to learn and retain new declarative memories. This form of memory includes the ability to quickly establish novel associations between unrelated items. To better understand the patterns of neural activity during associative memory formation, we recorded the activity of hippocampal neurons of macaque monkeys as they learned new associations. Hippocampal neurons signaled learning by changing their stimulus-selective response properties. This change in the pattern of selective neural activity occurred before, at the same time as, or after learning, which suggests that these neurons are involved in the initial formation of new associative memories.

Action Potentials↗
PubMed2003

Declarative versus episodic: two theories put to the test.

The question of whether the hippocampus plays a selective role in episodic memory or a more general role in both episodic and semantic memory (together termed declarative memory) is an unresolved and much-debated topic in the current literature. In two back-to-back articles in this issue of Neuron, Squire and his colleagues describe findings from a group of six patients with damage thought to be limited to the hippocampus. The reported findings provide new evidence toward resolving this much-debated controversy.

Hippocampus↗
PubMed2002

Distribution of calbindin D-28k in the entorhinal, perirhinal, and parahippocampal cortices of the macaque monkey.

We examined the distribution of calbindin D-28k-immunoreactive (CB-IR) neurons, fibers, and neuropil in the entorhinal (area 28), perirhinal (areas 35 and 36), and parahippocampal (areas TH and TF) cortices in the macaque monkey. Two main findings are reported. First, except for CB-IR neurogliaform cells that are only observed in the parahippocampal cortex, the morphology of CB-stained pyramidal and nonpyramidal cells were similar across the three cortical areas examined. Second, we find that the topography of CB staining differed between the three areas. The entorhinal cortex exhibits the most striking gradient of CB staining such that the most anterior and medial portions are most strongly labeled, whereas posterior and lateral areas exhibit only weak labeling. The labeling throughout the perirhinal and parahippocampal cortices is more homogeneous. Area 35 contains only lightly stained neuropil and few CB-IR cells. Area 36 and areas TH and TF of the parahippocampal cortex contain a moderate to high density of CB-IR cells and fibers throughout their full rostrocaudal extents, although each area exhibits unique laminar patterns of staining. In all areas examined, the highest density of CB-positive cells and fibers is observed in superficial layers with lower densities of CB-positive cells and fibers present in deep layers. These findings, taken together with our current understanding of the connections of these areas may have implications for understanding the circuit properties of the entorhinal, perirhinal, and parahippocampal cortices areas in both normal and disease states.

Animals↗
PubMed2002

Perirhinal and parahippocampal cortices of the macaque monkey: projections to the neocortex.

We investigated the topographic and laminar organization of the efferent cortical projections of the perirhinal and parahippocampal cortices. Area 36 of the perirhinal cortex projects preferentially to areas TE and TEO, whereas area TF of the parahippocampal cortex projects preferentially to the posterior parietal cortex and area V4. Area TF projects to many regions of the frontal lobe, whereas area 36 projects mainly to the orbital surface. The insular and cingulate cortices receive projections from areas 36 and TF, whereas only area TF projects to the retrosplenial cortex. Projections to the superior temporal gyrus, including the dorsal bank of the superior temporal sulcus, arise predominantly from area TF. Area 36 projects only to rostral levels of the superior temporal gyrus. Area TF has, in general, reciprocal connections with the neocortex, whereas area 36 has more asymmetric connections. Area 36, for example, projects to more restricted regions of the frontal cortex and superior temporal sulcus than it receives inputs from. In contrast, it projects to larger portions of areas TE and TEO than it receives inputs from. The efferent projections of areas 36 and TF are primarily directed to the superficial layers of the neocortex, a laminar organization consistent with connections of the feedback type. Projections to unimodal visual areas terminate in large expanses of the cortex, but predominantly in layer I. Projections to other sensory and polymodal areas, in contrast, terminate in a columnar manner predominantly in layers II and III. In all areas receiving heavy projections, the projections extend throughout most cortical layers, largely avoiding layer IV. We discuss these findings in relation to current theories of memory consolidation.

Amino Acids↗