Journal
NEURAL NETWORKS
Volume 18, Issue 10, Pages 1301-1308Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2005.05.004
Keywords
neural microcircuit; spiking neurons; information theoretic methods; neural coding; computational power; dynamic synapses; linear regression; Bayesian classifier
Ask authors/readers for more resources
Numerous methods have already been developed to estimate the information contained in single spike trains. In this article we explore efficient methods for estimating the information contained in the simultaneous firing activity of hundreds of neurons. Obviously such methods are needed to analyze data from multi-unit recordings. We test these methods on generic neural microcircuit models consisting of 800 neurons, and analyze the temporal dynamics of information about preceding spike inputs in such circuits. It turns out that information spreads with high speed in Such generic neural microcircuit models, thereby supporting-without the postulation of any additional neural or synaptic mechanisms-the possibility of ultra-rapid computations on the first input spikes. (c) 2005 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available