We present a computational neural network model of recognition memory based on the biological structures of the hippocampus and medial temporal lobe cortex (MTLC), which perform complementary learning functions. The hippocampal component of the model contributes to recognition by recalling specific studied details. MTLC can not support recall, but it is possible to extract a scalar familiarity signal from MTLC that tracks how well the test item matches studied items. We present simulations that establish key qualitative differences in the operating characteristics of the hippocampal recall and MTLC familiarity signals, and we identify several manipulations (e.g., target-lure similarity, interference) that differentially affect the two signals. We also use the model to address the stochastic relationship between recall and familiarity (i.e., are they independent), and the effects of partial vs. complete hippocampal

Computational models in cognitive neuroscience should ideally use biological properties and powerful computational principles to produce behavior consistent with psychological findings. Error-driven backpropagation is computationally powerful, and has proven useful for modeling a range of psychological data, but is not biologically plausible. Several approaches to implementing backpropagation in a biologically plausible fashion converge on the idea of using bidirectional activation propagation in interactive networks to convey error signals. This paper demonstrates two main points about these error-driven interactive networks: (a) they generalize poorly due to attractor dynamics that interfere with the network's ability to systematically produce novel combinatorial representations in response to novel inputs; and (b) this generalization problem can be remedied by adding two widely used mechanistic principles, inhibitory competition and Hebbian learning, that can be independent...

The authors theorize that 2 neurocognitive sequence-learning systems can be distinguished in serial reaction time experiments, one dorsal (parietal and supplementary motor cortex) and the other ventral (temporal and lateral prefrontal cortex). Dorsal system learning is implicit and associates noncategorized stimuli within dimensional modules. Ventral system learning can be implicit or explicit. It also allows associating events across dimensions and therefore is the basis of cross-task integration or interference, depending on degree of cross-task correlation of signals. Accordingly, lack of correlation rather than limited capacity is responsible for dual-task effects on learning. The theory is relevant to issues of attentional effects on learning; the representational basis of complex, sequential skills; hippocampalversus basal ganglia-based learning; procedural versus declarative memory; and implicit versus explicit memory. The ability to produce and learn sequential actions is one of the hallmarks of human cognition. Indeed, this ability has been hypothesized to constitute a fundamental adaptation that characterizes

Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff formulated four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural ‘blackboard ’ architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. 2 Content

The medial temporal lobe (MTL) has been studied extensively at all levels of analysis, yet its function remains unclear. Theory regarding the cognitive function of the MTL has centered along 3 themes. Different authors have emphasized the role of the MTL in episodic recall, spatial navigation, or relational memory. Starting with the temporal context model (M.W. Howard and M. J. Kahana, 2002), a distributed memory model that has been applied to benchmark data from episodic recall tasks, the authors propose that the entorhinal cortex supports a gradually changing representation of temporal context and the hippocampus proper enables retrieval of these contextual states. Simulation studies show this hypothesis explains the firing of place cells in the entorhinal cortex and the behavioral effects of hippocampal lesion in relational memory tasks. These results constitute a first step towards a unified computational theory of MTL function that integrates neurophysiological, neuropsychological and cognitive findings.

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In the single-store model of memory, the enhanced recall for the last items in a free-recall task (i.e., the recency effect) is understood to reflect a general property of memory rather than a separate short-term store. This interpretation is supported by the finding of a long-term recency effect under conditions that eliminate the contribution from the short-term store. In this article, evidence is reviewed showing that recency effects in the short and long terms have different properties, and it is suggested that 2 memory components are needed to account for the recency effects: an episodic contextual system with changing context and an activation-based short-term memory buffer that drives the encoding of item–context associations. A neurocomputational model based on these 2 components is shown to account for previously observed dissociations and to make novel predictions, which are confirmed in a set of experiments.

We present an overview of our computational approach towards understanding the different contributions of the neocortex and hippocampus in learning and memory. The approach is based on a set of principles derived from converging biological, psychological, and computational constraints. The most central principles are that the neocortex employs a slow learning rate and overlapping distributed representations to extract the general statistical structure of the environment, while the hippocampus learns rapidly using separated representations to encode the details of specific events while suffering minimal interference. Additional principles concern the nature of learning (error-driven and Hebbian), and recall of information via pattern completion. We summarize the results of applying these principles to a wide range of phenomena in conditioning, habituation, contextual learning, recognition memory, recall, and retrograde amnesia, and point to directions of current development. 2 Computat...

The authors present their primary value learned value (PVLV) model for understanding the rewardpredictive firing properties of dopamine (DA) neurons as an alternative to the temporal-differences (TD) algorithm. PVLV is more directly related to underlying biology and is also more robust to variability in the environment. The primary value (PV) system controls performance and learning during primary rewards, whereas the learned value (LV) system learns about conditioned stimuli. The PV system is essentially the Rescorla–Wagner/delta-rule and comprises the neurons in the ventral striatum/nucleus accumbens that inhibit DA cells. The LV system comprises the neurons in the central nucleus of the amygdala that excite DA cells. The authors show that the PVLV model can account for critical aspects of the DA firing data, making a number of clear predictions about lesion effects, several of which are consistent with existing data. For example, first- and second-order conditioning can be anatomically dissociated, which is consistent with PVLV and not TD. Overall, the model provides a biologically plausible framework for understanding the neural basis of reward learning.

How have connectionist models informed the study of development? This paper considers three contributions from specific models. First, connectionist models have proven useful for exploring nonlinear dynamics and emergent properties, and their role in nonlinear developmental trajectories, critical periods and developmental disorders. Second, connectionist models have informed the study of the representations that lead to behavioral dissociations. Third, connectionist models have provided insight into neural mechanisms, and why different brain regions are specialized for different functions. Connectionist and dynamic systems approaches to development have differed, with connectionist approaches focused on learning processes and representations in cognitive tasks, and dynamic systems approaches focused on mathematical characterizations of physical elements of the system and their interactions with the environment. The two approaches also share much in common, such as their emphasis on continuous, nonlinear processes and their broad application to a range of behaviors.