Short term memory in recurrent networks of spiking neurons |
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Authors: | Daucé Emmanuel |
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Affiliation: | (1) UMR Movement and Perception, Faculty of Sport Sciences, University of the Mediterrannean, 163 avenue de Luminy, CP 910, 13288 Marseille cedex 9, France ( |
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Abstract: | We present in this paper a general model of recurrent networks of spiking neurons, composed of several populations, and whose
interaction pattern is set with a random draw. We use for simplicity discrete time neuron updating, and the emitted spikes
are transmitted through randomly delayed lines. In excitatory-inhibitory networks, we show that inhomogeneous delays may favour
synchronization provided that the inhibitory delays distribution is significantly stronger than the excitatory one. In that
case, slow waves of synchronous activity appear (this synchronous activity is stronger in inhibitory population). This synchrony
allows for a fast ada ptivity of the network to various input stimuli. In networks observing the constraint of short range
excitation and long range inhibition, we show that under some parameter settings, this model displays properties of –1– dynamic
retention –2– input normalization –3– target tracking. Those properties are of interest for modelling biological topologically
organized structures, and for robotic applications taking place in noisy environments where targets vary in size, speed and
duration.
This revised version was published online in June 2006 with corrections to the Cover Date. |
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Keywords: | chaotic dynamics delays neural field random recurrent neural networks short term memory sparsely connected networks spiking neurons synchronization |
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