Introduction Neuronal activity due to recurrent excitations in the form of a spatially localized pulse or bump has been proposed as a mechanism for feature selectivity in models of the visual system (Somers, Nelson, & Sur, 1995; Hansel & Sompolinsky, 1998), the head direction system (Skaggs, Knieram, Kudrimoti, & McNaughton, 1995; Zhang, 1996; Redish, Elga, & Touretzky, 1996), and working memory (Wilson & Cowan, 1973; Amit & Brunel, 1997; Camperi & Wang, 1998). Many of the previous mathematical formulations of such structures have employedpopulation rate models (Wilson &Cowan, 1972, 1973; Amari, 1977; Kishimoto & Amari, 1979; Hansel & Sompolinsky, 1998). (See Ermentrout, 1998, for a recent review.) Here, we consider a network of spiking neurons that shows such structures and investigate their properties. In our network we #nd localized time-stationary states

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.

The authors model the neural mechanisms underlying spatial cognition, integrating neuronal systems and behavioral data, and address the relationships between long-term memory, short-term memory, and imagery, and between egocentric and allocentric and visual and ideothetic representations. Long-term spatial memory is modeled as attractor dynamics within medial–temporal allocentric representations, and short-term memory is modeled as egocentric parietal representations driven by perception, retrieval, and imagery and modulated by directed attention. Both encoding and retrieval/imagery require translation between egocentric and allocentric representations, which are mediated by posterior parietal and retrosplenial areas and the use of head direction representations in Papez’s circuit. Thus, the hippocampus effectively indexes information by real or imagined location, whereas Papez’s circuit translates to imagery or from perception according to the direction of view. Modulation of this translation by motor efference allows spatial updating of representations, whereas prefrontal simulated motor efference allows mental exploration. The alternating temporal–parietal flows of information are organized by the theta rhythm. Simulations demonstrate the retrieval and updating of familiar spatial scenes, hemispatial neglect in memory, and the effects on hippocampal place cell firing of lesioned head direction representations and of conflicting visual and ideothetic inputs.

We propose a computational model which is consistent with several neurophysiological findings concerning biological head-direction cells and hippocampal place cells. The model consists of two separate neural systems providing directional and place coding representations, respectively. These two modules are strongly coupled and interact with each other to form a unitary spatial learning system. We stress the importance of correlating idiothetic and allothetic signals to determine the dynamics of the system in order to stabilize head-direction and place representations over time. We give experimental results obtained by implementing the entire model on a real mobile robot. 1. Introduction Directional sense and place coding are crucial capabilities for solving spatial cognitive tasks. Neurophysiological findings suggest the existence of neural representations of direction and position as a basis for animal spatial behavior (O'Keefe & Nadel, 1978; Taube et al., 1990). Experi...

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. Based on head direction experiments in rats, the existence of localized bumps of thalamic activity has been proposed. We computationally demonstrate the existence of a novel class of localized bump solutions in a two-layer conductancebased thalamic network and analyze the mechanisms behind these stable patterns. In contrast to previous models of bump activity, here inhibition plays a crucial role in initially spreading neuronal ring and in subsequently sustaining it. In our model, we incorporate local strong, fast GABAA inhibition and diuse weak, slow GABAB inhibition, based on previous biophysical experiments. These forms of inhibition contribute in dierent, yet complementary, ways to the observed pattern formation. Keywords: localized activity, head direction cells, thalamus, conductance-based model, synaptic coupling Abbreviations: GABA { -aminobutyric acid; HD { head direction; PoS { postsubiculum; ATN { anterior thalamic nuclei; AD { anterior dorsal thalamic nucleus; TC { t...

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The position of a localized region of active neurons (a "bump") has been proposed to encode information for working memory, the head direction system, and feature selectivity in the visual system. Stationary bumps are ordinarily stable, but including spike frequency adaptation in the neural dynamics causes a stationary bump to become unstable to a moving bump through a supercritical pitchfork bifurcation in bump speed. Adding spatiotemporal noise to the network supporting the bump can cause the average speed of the bump to decrease to almost zero, reversing the effect of the adaptation and "restabilizing" the bump. This restabilizing occurs for noise levels lower than those required to break up the bump. The restabilizing can be understood by examining the effects of noise on the normal form of the pitchfork bifurcation where the variable involved in the bifurcation is bump speed. This noisy normal form can be further simplified to a persistent random walk in which the probability of changing direction is related to the noise level through an Arrhenius-type rate. The probability density function of position for the continuous-time version of this random walk satis es the telegrapher's equation, and the closed-form solution of this PDE allows us to find expressions for the mean and variance of the average speed of the particle (the bump) undergoing the random walk. This noise-induced stabilization is a novel example in which moderate amounts of noise have a beneficial effect on a system, specifically, stabilizing a spatiotemporal pattern.

The place fields of hippocampal cells in old animals sometimes change when an animal is removed from and then returned to an environment [ Barnes et al., 1997 ] . The ensemble correlation between two sequential visits to the same environment shows a strong bimodality for old animals (near 0, indicative of remapping, and greater than 0.7, indicative of a similar representation between experiences), but a strong unimodality for young animals (greater than 0.7, indicative of a similar representation between experiences). One explanation for this is the multi-map hypothesis in which multiple maps are encoded in the hippocampus: old animals may sometimes be returning to the wrong map. A theory proposed by Samsonovich and McNaughton (1997) suggests that the Barnes et al. experiment implies that the maps are pre-wired in the CA3 region of hippocampus. Here, we offer an alternative explanation in which orthogonalization properties in the dentate gyrus (DG) region of hippocampus interact with e...

Cells in several areas of the hippocampal formation show place specific firing patterns, and are thought to form a distributed representation of an animal’s current location in an environment. Experimental results suggest that this representation is continually updated even in complete darkness, indicating the presence of a path integration mechanism in the rat. Adopting the Neural Engineering Framework (NEF) presented by Eliasmith and Anderson (2003) we derive a novel attractor network model of path integration, using heterogeneous spiking neurons. The network we derive incorporates representation and updating of position into a single layer of neurons, eliminating the need for a large external control population, and without making use of multiplicative synapses. An efficient and biologically plausible control mechanism results directly from applying the principles of the NEF. We simulate the network for a variety of inputs, analyze its performance, and give three testable predictions of our model.