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Network 2 (1991) 245-258. Printed in the UK Realistic synaptic inputs for modelneuralnetworks

Summary: Network 2 (1991) 245-258. Printed in the UK
Realistic synaptic inputs for modelneuralnetworks
L F Abbott
PlvricsDepartmentandCulterfor ComplexSystems,BrandeisUniversrty,Waltham,
MA 02254, USA
Received 27 March 1991
Abstract. An expressionisderived relatingthe input current for a single neuron in
a neuralnetwork to the firing rates of exutatory andithbrtory mputr synapsingon
lite dendrrtic tree of the neuron. Any dendritic geometry and any pattem of synap
tic connections c m be treated using the tecldques presented. The input aments
calctdllated. combmned with known k n g rate functrons, dlow the eRects of synaptic
conductancedmnges along dendritic cables to be rncluded in a meamfielddeswrp
tmn of network beliaviour. The shuntingeffectsof mhibitorysynaptic conductances
provide a solution to tlie ltigh firingrate problem in neutralnetwork models
1. Introdnction
Neural network models are often based on a mean-field approach [l]that uses known
properties of single neurons to predict the behaviour of large neuronal populations
In such models, the average firing rates of excitatory and inhibitory neurons are used
to describe the activity of the population. The basic equations of mean-field theory
relate these average rates to the rate of firing of a single neuron expressed as a function


Source: Abbott, Laurence - Center for Neurobiology and Behavior & Department of Physiology and Cellular Biophysics, Columbia University


Collections: Biology and Medicine