Diverse theories of animal navigation aim at explaining how to determine and maintain a course from one place to another in the environment, although each presents a particular perspective with its own terminologies. These vocabularies sometimes overlap, but unfortunately with different meanings. This paper attempts to precisely define the existing concepts and terminologies, so as to comprehensively describe the different theories and models within the same unifying framework. We present navigation strategies within a 4 level hierarchical framework based upon levels of complexity of required processing (Guidance, Place recognition-triggered Response, Topological navigation, Metric navigation). This classification is based upon what information is perceived, represented and processed. It contrasts with common distinctions based upon availability of certain sensors or cues and rather stresses the information structure and content of central processors. We then review computat...

: In this work we discuss Hebb's old ideas about cell assemblies in the light of recent results concerning temporal structure and correlations in neural signals. We want to give a conceptual, necessarily only rough picture, how ideas about `binding by synchronisation', `synfire chains', `local and global assemblies', `short and long term memory' and `behaviour' might be integrated into a coherent model of brain functioning based on neuronal assemblies. Keywords: cell assemblies, synchronization, gamma-oscillations, synfire chains, memory, behaviour 1 ASSEMBLIES AND ASSOCIATIVE MEMORIES 1.1 Cell Assemblies Cell assemblies have been introduced by Donald Hebb with the intention of providing a functional and at the same time structural model for cortical processes and neuronal representations of external events (Hebb, 1949). According to Hebb's ideas, stimuli, objects, things, but also more abstract entities like concepts, contextual relations, ideas, and so on are thought of being repre...

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We have developed a neural network architecture that implements a theory of attention, learning, and trans-cortical communication based on adaptive synchronization of 5-20 Hz and 30-80 Hz oscillations between cortical areas. It assigns functional significance to EEG, ERP, and neuroimaging data. Using dynamical systems theory, the architecture is constructed from recurrently interconnected oscillatory associative memory modules that model higher order sensory and motor areas of cortex. The modules learn connection weights between themselves which cause the system to evolve under a 5-20 Hz clocked sensory/motor processing cycle by a sequence of transitions of synchronized 30-80 Hz oscillatory attractors within the modules. The architecture employs selective"attentional" control of the synchronization of the 30-80 Hz oscillations between modules to direct the flow of communication and computation to recognize and generate sequences. The 30-80 Hz attractor amplitude patterns code the infor...

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

A computational model of the hippocampal function in spatial learning is presented. A spatial representation is incrementally acquired during exploration. Visual and self-motion information is fed into a network of rate-coded neurons. A consistent and stable place code emerges by unsupervised Hebbian learning between place- and head direction cells. Based on this representation, goal-oriented navigation is learnt by applying a reward-based learning mechanism between the hippocampus and nucleus accumbens. The model, validated on a real and simulated robot, successfully localises itself by recalibrating its path integrator using visual input. A navigation map is learnt after about 20 trials, comparable to rats in the water maze. In contrast to previous works, this system processes realistic visual input. No compass is needed for localisation and the reward-based learning mechanism extends discrete navigation models to continuous space. The model reproduces experimental findings and suggests several neurophysiological and behavioural predictions in the rat. 1

We present in this paper an extension of the new neural network architecture named OWE (Orthogonal Weight Estimator). The OWE architecture permits to implement the modelization of context dependent behavior by dynamically estimating the synaptic weights of a MLP with respect to external parameters named here 'context parameters. The principle of this extension, named ODWE (Orthogonal Delta Weight Estimator), is based not on an estimation but on a modulation of the synaptic efficiencies. The interests of this approach is firstly to view the context dependent behavior as a general behavior modulated by the context parameters, and secondly to bring closer the OWE principle to neurobiological knowledge. A illustration on the modelization of a mathematical function is shown at the end of the paper.

Full-length review EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis

ation on the first experiment, Steven Cole for programming the visual search experiment, and Dan Simons, Ranxiao Wang, John Sloboda, and Mike Posner for discussion. This work was supported by an NRSA predoctoral fellowship to L.H.-V. (1 F31 MH10607), by two Cornell Cognitive Studies summer fellowships to L.H.-V., and by a grant from NIH (HD23103) to E.S.S. Address correspondence and reprint requests to Elizabeth Spelke at the Department of Brain and Cognitive Sciences, MIT, E10-246, Cambridge, MA 02138. 3 0010-0285/99 $30.00 Copyright 1999 by Academic Press All rights of reproduction in any form reserved. the experiments suggest that humans' flexible spatial memory depends on the ability to combine diverse information sources rapidly into unitary representations and that this ability, in turn, depends on natural language. 1999 Academic Press Although the neural mechanisms subserving perception, action, memory, and problem solving are substantially conserved across mammals, the

There has been considerable interest in the importance of oscillations in the brain and in how these oscillations relate to the firing of single neurons. Recently a number of studies have shown that the spiking of individual neurons in the medial prefrontal cortex (mPFC) become entrained to the hippocampal (HPC) theta rhythm. We recently showed that theta-entrained mPFC cells lost theta-entrainment specifically on error trials even though the firing rates of these cells did not change (Hyman et al., 2010). This implied that the level of HPC theta-entrainment of mPFC units was more predictive of trial outcome than differences in firing rates and that there is more information encoded by the mPFC on working memory tasks than can be accounted for by a simple rate code. Nevertheless, the functional meaning of mPFC entrainment to HPC theta remains a mystery. It is also unclear as to whether there are any differences in the nature of the information encoded by theta-entrained and non-entrained mPFC cells. In this review we discuss mPFC entrainment to HPC theta within the context of previous results as well as provide a more detailed analysis of the Hyman et al. (2010) data set. This re-analysis revealed that thetaentrained mPFC cells selectively encoded a variety of task relevant behaviors and stimuli while never theta-entrained mPFC cells were most strongly attuned to errors or the lack of expected