Persistent activation blobs in spiking neural networks with Mexican hat connectivity

Authors:
Filip Piekniewski
Affiliations:
Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Torun, Poland
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Year:
2010

Citing 5
Cited 0

Polychronization: Computation with Spikes

Neural Computation
Spontaneous scale-free structure of spike flow graphs in recurrent neural networks

Neural Networks
Robustness of plaws in degree distributions for spiking neural networks

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Simple model of spiking neurons

IEEE Transactions on Neural Networks
Which model to use for cortical spiking neurons?

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Short range excitation, long range inhibition sometimes referred to as mexican hat connectivity seems to play important role in organization of the cortex, leading to fairly well delineated sites of activation. In this paper we study a computational model of a grid filled with rather simple spiking neurons with mexican hat connectivity. The simulation shows, that when stimulated with small amount of random noise, the model results in a stable activated state in which the spikes are organized into persistent blobs of activity. Furthermore, these blobs exhibit significant lifetime, and stable movement across the domain. We analyze lifetimes and trajectories of the spots, arguing that they can be interpreted as basic computational charge units of the so called spike flow model introduced in earlier work.