An Integrated Neural Network-Event-Related Potentials Model of Temporal and Probability Context Effects on Event Categorization

We present a neural network that adapts and integrates several preexisting or new modules to categorize events in short term memory (STM), encode temporal order in working memory, evaluate timing and probability context in medium and long term memory. The model shows how processed contextual information modulates event recognition and categorization, focal attention and incentive motivation. The model is based on a compendium of Event Related Potentials (ERPs) and behavioral results either collected by the authors or compiled from the classical ERP literature. Its hallmark is, at the functional level, the interplay of memory registers endowed with widely different dynamical ranges, and at the structural level, the attempt to relate the different modules to known anatomical structures.INSERM; NATO; DGA/DRET (911470/A000/DRET/DS/DR

Learning Temporal Contexts and Priming-Preparation Modes for Pattern Recognition

The system presented here is based on neurophysiological and electrophysiological data. It computes three types of increasingly integrated temporal and probability contexts, in a bottom-up mode. To each of these contexts corresponds an increasingly specific top-down priming effect on lower processing stages, mostly pattern recognition and discrimination. Contextual learning of time intervals, events' temporal order or sequential dependencies and events' prior probability results from the delivery of large stimuli sequences. This learning gives rise to emergent properties which closely match the experimental data.Institut national de la santé et de la recherche médicale; Ministère de la Défense Nationale (DGA/DRET 911470/AOOO/DRET/DS/DR); Consejo Nacional de Ciencia y Tecnología (63462

Space and time-related firing in a model of hippocampo-cortical interactions

International audienceIn a previous model [3], a spectral timing neural network [4] was used to account for the role of the Hs in the acquisition of classical conditioning. The ability to estimate the timing between separate events was then used to learn and predict transitions between places in the environment. We propose a neural architecture based on this work and explaining the out-of-field activities in the Hs along with their temporal prediction capabilities. The model uses the hippocampo-cortical pathway as a means to spread reward signals to entorhinal neurons. Secondary predictions of the reward signal are then learned, based on transition learning, by pyramidal neurons of the CA region

Hippocampal place cells as spatial category codes for navigation: a network model as implemented by a mobile agent

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From reflex to planning: multimodal, versatile, complex systems in biorobotics

International audienceAs models of living beings acting in a real world biorobots undergo an accelerated “philogenic” complexification. The first efficient robots performed simple animal behaviours (e.g., those of ants, crickets) and later on isolated elementary behaviours of complex beings. The increasing complexity of the tasks robots are dedicated to is matched by an increasing complexity and versatility of the architectures now supporting conditioning or even elementary planning

A Hippocampal Model of Visually Guided Navigation as Implemented by a Mobile Agent

International audienceVisually guided landmark navigation is based on space coding by hippocampal place cells (Pc)[2]. A biologically realistic architecture of cooperative-competitive associative networks (implemented as a control system for mobile agents) emulates place cell activity during local navigation (self and goal -localization, and route finding) in exploration and goal-retrieval paradigms. The system builds and stores panoramic views from landmarks and compares these views with current inputs. Mismatch induced low levels of recognition activity during exploration trigger a vigilance burst (by a mechanism inspired from septal modulation) which favors either the recognition of an alternative place category, or the creation of a new category. The sole implementation of visual ¿What¿ and ¿Where¿ information does not restrain the generality of the model since several modalities (proprioceptive and vestibular in particular) could cooperate to give rise to more robust place field spatial categories [5]. Providing the system with real visual inputs automatically extracted from a natural environment demonstrates that interspecies differences [6] in Pc coding (e.g. Pcs in rats vs. view cells in monkeys) result more from characteristics of the visual systems than from differences in Hs processing. Conversely, differences in Pc multiple codes within the same system result according to the model from different levels of processing and/or different degrees of multimodality. Each of these codes could be used within different navigational strategies a control system directly derived from the model allows a mobile agent to learn a few places in an environment, and their associated actions to perform in order to reach a goal. Generalization property of the model provides the capacity to join the goal from any place in the learned environment

Cortico-Hippocampal Maps and Navigation Strategies in Robots and Rodents

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Place cells, maps, and navigation strategies: Processing steps of the cortico-hippocampal system

International audienceA biologically inspired integrated model of different hippocampal subsystems makes a distinction between place cells (PC) within the entorhinal cortex (EC) (diffuse) or dentate gyrus (segregated), and transition cells (TC) in CA3-CA1 that encode transitions between events. These two types of codes support two kinds of hippocampo-cortical cognitive maps: a context-independent map in the subiculum and EC essentially encodes the spatial layout of the environment thanks to a local dominance of ideothetic movement-related information over allothetic (visual) information, and a task- and temporal context-dependent map based on the TC in CA3-CA1 allows the encoding, in higher-order structures, of maps as graphs resulting from combinations of learned sequences of events. The dominantly spatial and the temporal-task-dependent maps are permanently stored in the parietal cortex and the pre-frontal cortex respectively. On the basis of these two maps, two distinct goal-oriented navigation strategies were designed in experimental robotic paradigms: one based on a (population) vector code of the location-actions pairs to learn and implement to reach the goal, and the other based on linking TC together as conditioning chains that are implemented under the top-down guidance of drives and motivation

Sequence Learning and Timing in Hippocampus, Prefrontal Cortex and Accumbens

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