20 research outputs found
Dynamical model of sequential spatial memory: winnerless competition of patterns
We introduce a new biologically-motivated model of sequential spatial memory
which is based on the principle of winnerless competition (WLC). We implement
this mechanism in a two-layer neural network structure and present the learning
dynamics which leads to the formation of a WLC network. After learning, the
system is capable of associative retrieval of pre-recorded sequences of spatial
patterns.Comment: 4 pages, submitted to PR
Spatial Representation and Navigation in a Bio-inspired Robot
A biologically inspired computational model of rodent repre-sentation?based (locale) navigation is presented. The model combines visual input in the form of realistic two dimensional grey-scale images and odometer signals to drive the firing of simulated place and head direction cells via Hebbian synapses. The space representation is built incrementally and on-line without any prior information about the environment and consists of a large population of location-sensitive units (place cells) with overlapping receptive fields. Goal navigation is performed using reinforcement learning in continuous state and action spaces, where the state space is represented by population activity of the place cells. The model is able to reproduce a number of behavioral and neuro-physiological data on rodents. Performance of the model was tested on both simulated and real mobile Khepera robots in a set of behavioral tasks and is comparable to the performance of animals in similar tasks
Independence of firing correlates of anatomically proximate hippocampal pyramidal cells
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201426.pdf (publisher's version ) (Open Access