The influence of prior experience on some forms of behavior and cognition is drastically affected by damage to the hippocampal system. However, if the hippocampal system is left intact both during the experience and for a period of time thereafter, subsequent damage can have much less or even no effect. Such findings suggest that memory traces change over time in a way that makes them less dependent on the hippocampal system. This process of change has often been called consolidation. Consolidation is a very gradual process; in humans, it appears to span up to 15 years. This article asks what consolidation is and why it occurs. We take as our point of departure the view that the initial memory trace that results from a relevant experience consists of changes to the strengths of the connections among neurons in the hippocampal system. Bidirectional connections between the neocortex and the hippocampus allow these initial traces to mediate the reinstatement of representations of events o...

The hippocampus and related structures are thought to be capable of 1) representing cortical activity in a way that minimizes overlap of the representations assigned t ~ different cortical patterns (pattern separation); and 2) modifying synaptic connections so that these representations can later be reinstated from partial or noisy versions of the cortical activity pattern that was present at the time of storage (pattern completion). We point out that there is a trade-off between pattern separation and completion and propose that the unique anatomical and physiological properties of the hippocampus might serve to minimize this trade-off. We use analytical methods to determine quantitative estimates of both separation and completion for specified parameterized models of the hippocampus. These estimates are then used to evaluate the role of various properties and of the hippocampus, such as the activity levels seen in different hippocampal regions, synaptic potentiation and depression, the multi-layer connectivity of the system, and the relatively focused and strong mossy fiber projections. This analysis is focused on the feedforward pathways from the entorhinal cortex (EC) to the dentate gyrus (DG) and region CA3. Among our results are the following: 1) Hebbian synaptic modification (LTP) facilitates completion but reduces separation, unless the

We are capable of drawing a variety of inferences effortlessly, spontaneously, and with remarkable efficiency — as though these inferences are a reflex response of our cognitive apparatus. This remarkable human ability poses a challenge for cognitive science and computational neuroscience: How can a network of slow neuron-like elements represent a large body of systematic knowledge and perform a wide range of inferences with such speed? The connectionist model Shruti attempts to address this challenge by demonstrating how a neurally plausible network can encode a large body of semantic and episodic facts, systematic rules, and knowledge about entities and types, and yet perform a wide range of explanatory and predictive inferences within a few hundred milliseconds. Relational structures (frames, schemas) are represented in Shruti by clusters of cells, and inference in Shruti corresponds to a transient propagation of rhythmic activity over such cell-clusters wherein dynamic bindings are represented by the synchronous firing of appropriate cells. Shruti encodes mappings across relational structures using high-efficacy links that enable the propagation of rhythmic activity, and it encodes items in long-term memory as coincidence and conincidence-error detector circuits that become active in response to the occurrence (or non-occurrence) of appropriate coincidences in the on going flux of rhythmic activity.

The concepts of declarative memory and procedural memory have been used to distinguish two basic types of learning. A neural network model suggests how such memory processes work together as recognition learning, reinforcement learning, and sensory-motor learning take place during adaptive behaviors. To coordinate these processes, the hippocampal formation and cerebellum each contain circuits that learn to adaptively time their outputs. Within the model, hippocampal timing helps to maintain attention on motivationally salient goal objects during variable task-related delays, and cerebellar timing controls the release of conditioned responses. This property is part of the model's description of how cognitive-emotional interactions focus attention on motivationally valued cues, and how this process breaks down due to hippocampal ablation. The model suggests that the hippocampal mechanisms that help to rapidly draw attention to salient cues could prematurely release motor commands were no...

Human episodic memory provides a seemingly unlimited storage for everyday experiences, and a retrieval system that allows us to access the experiences with partial activation of their components. The system is believed to consist of a fast, temporary storage in the hippocampus, and a slow, longterm storage within the neocortex. This paper presents a neural network model of the hippocampal episodic memory inspired by Damasio's idea of Convergence Zones. The model consists of a layer of perceptual feature maps and a binding layer. A perceptual feature pattern is coarse coded in the binding layer, and stored on the weights between layers. A partial activation of the stored features activates the binding pattern, which in turn reactivates the entire stored pattern. For many configurations of the model, a theoretical lower bound for the memory capacity can be derived, and it can be an order of magnitude or higher than the number of all units in the model, and several orders of magnitude higher than the number of binding-layer units. Computational simulations further indicate that the average capacity is an order of magnitude larger than the theoretical lower bound, and making the connectivity between layers sparser causes an even further increase in capacity. Simulations also show that if more descriptive binding patterns are used, the errors tend to be more plausible (patterns are confused with other similar patterns), with a slight cost in capacity. The convergence-zone episodic memory therefore accounts for the immediate storage and associative retrieval capability and large capacity of the hippocampal memory, and shows why the memory encoding areas can be much smaller than the perceptual maps, consist of rather coarse computational units, and be only sparsely connected t...

Brain imaging methods, such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), provide a unique opportunity to study the neurobiology of human memory. Since these methods can measure most of the brain, it is possible to examine the operations of large-scale neural systems and their relation to cognition. Two neuroimaging studies, one concerning working memory and the other episodic memory retrieval, serve as examples of application of two analytic methods that are optimized for the quantification of neural systems, structural equation modeling and partial least squares. Structural equation modeling was used to explore shifting prefrontal and limbic interactions from the right to the left hemisphere in a delayed match-to-sample task for faces. A feature of the functional network for short delays was strong right hemisphere interactions between hippocampus, inferior prefrontal, and anterior cingulate cortices. At longer delays, these same three areas were strongly linked, but in the left hemisphere, which was interpreted as reflecting change in task strategy from perceptual to elaborate encoding with increasing delay. The primary manipulation in the memory retrieval study was different levels of retrieval success. Partial least squares was used to determine whether the image-wide pattern of covariances of Brodmann areas 10 and 45/47 in right prefrontal cortex (RPFC) and the left hippocampus (LGH) could be mapped on to retrieval levels. Area 10 and LGH showed an opposite pattern of functional connectivity with a large expanse of bilateral limbic cortices that was equivalent for all levels of retrieval as well as the baseline task. However, only during high retrieval area 45/47 was included in this pattern. The results suggest that activ...

& How objects are represented and processed in the brain is a central topic in cognitive neuroscience. Previous studies have shown that knowledge of objects is represented in a featurebased distributed neural system primarily involving occipital and temporal cortical regions. Research with nonhuman primates suggest that these features are structured in a hierarchical system with posterior neurons in the inferior temporal cortex representing simple features and anterior neurons in the perirhinal cortex representing complex conjunctions of features (Bussey & Saksida, 2002; Murray & Bussey, 1999). On this account, the perirhinal cortex plays a crucial role in object identification by integrating information from different sensory systems into more complex polymodal feature conjunctions. We tested the implications of these claims for human object processing in an event-related fMRI study in which we presented colored pictures of common objects for 19 subjects to name at two levels of specificity—basic and domain. We reasoned that domain-level naming requires access to a coarsergrained representation of objects, thus involving only posterior regions of the inferior temporal cortex. In contrast, basic-level naming requires finer-grained discrimination to differentiate between similar objects, and thus should involve anterior temporal regions, including the perirhinal cortex. We found that object processing always activated the fusiform gyrus bilaterally, irrespective of the task, whereas the perirhinal cortex was only activated when the task required finer-grained discriminations. These results suggest that the same kind of hierarchical structure, which has been proposed for object processing in the monkey temporal cortex, functions in the human. &

Although experimental evidence for distributed cell assemblies is growing, theories of cell assemblies are still marginalized in theoretical neuroscience. We argue that this has to do with shortcomings of the currently best understood assembly theories, the ones based on formal associative memory models. These only insufficiently reflect anatomical and physiological properties of nervous tissue and their functionality is too restricted to provide a framework for cognitive modeling. We describe cell assembly models that integrate more neurobiological constraints and review results from simulations of a simple nonlocal associative network formed by a reciprocal topographic projection. Impacts of nonlocal associative projections in the brain are discussed

this paper, we consider several hypotheses which have been put forward to explain the role of temporal structure of neural activity for information processing. We describe neural networks that have been developed in support of these hypotheses and whose analysis reveals what kind of model neurons or neural assemblies are suitable and how their interaction should be organised to implement different types of information coding and processing. Neuronal Coding

Abstract — Brainstorming is the process of generating ideas in a specific task or problem context. We have previously presented a connectionist framework to study the dynamics of idea generation in individuals. In this paper, we develop this model further, and apply it to studying qualitatively the effects of priming on the process of ideation. Motivated by experimental data from a previous study, we explore the differential effects of relevant and irrelevant primes on productivity of idea generation in specific problem/task contexts. Simulations using our model suggest that even irrelevant primes can provide a modest productivity boost in contexts that are familiar or are similar to familiar contexts, but no benefit when the context is unfamiliar. We propose possible explanations for these results and make predictions for future experiments. I.