Paper Info
Title
Analysis of neural response for excitation-inhibition balanced networks with reversal potentials for large numbers of inputs
Abstract
The observed variability in the spike rate of cortical neurons has been hypothesized to result from a balance in the excitatory and inhibitory synaptic inputs that the neurons receive. The coefficient of variation of the spike rate is calculated in the limit of a large number of inputs using the integrated-input technique, which is extended here to include the effect of reversal potentials. The output spike rate is found to increase monotonically over two orders of magnitude, thereby solving the dynamic range (or gain control) problem. The coefficient of variation is approximately 1.0 for low input rates and increases to around 1.6 at high input rates, well within the range observed in the response of cortical neurons
Year
DOI
Venue
1999
10.1109/IJCNN.1999.831507
IJCNN
Keywords
Field
DocType
neural nets,neurophysiology,physiological models,cortical neurons,excitation-inhibition balanced networks,excitatory synaptic inputs,inhibitory synaptic inputs,integrated-input technique,neural response analysis,reversal potentials,spike rate variability,dynamic range,predictive models,gain control,fluctuations,coefficient of variation,numerical simulation
Coefficient of variation,Dynamic range,Neurophysiology,Computer science,Excitatory postsynaptic potential,Inhibitory postsynaptic potential,Artificial intelligence,Automatic gain control,Artificial neural network,Order of magnitude,Machine learning
Conference
Volume
ISSN
ISBN
1
1098-7576
0-7803-5529-6
Citations 
PageRank 
References 
0
0.34
3
Authors
1
Name
Order
Citations
PageRank
Anthony N. Burkitt148746.71