Paper Info
Title
Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell
Abstract
To understand the interspike interval (ISI) variability displayed by visual cortical neurons (Softky & Koch, 1993), it is critical to examine the dynamics of their neuronal integration, as well as the variability in their synaptic input current. Most previous models have focused on the latter factor. We match a simple integrate-and-fire model to the experimentally measured integrative properties of cortical regular spiking cells (McCormick, Connors, Lighthall, & Prince, 1985). After setting RC parameters, the postspike voltage reset is set to match experimental measurements of neuronal gain (obtained from in vitro plots of firing frequency versus injected current). Examination of the resulting model leads to an intuitive picture of neuronal integration that unifies the seemingly contradictory and random walk pictures that have previously been proposed. When ISIs are dominated by postspike recovery, arguments hold and spiking is regular; after the “memory” of the last spike becomes negligible, spike threshold crossing is caused by input variance around a steady state and spiking is Poisson. In integrate-and-fire neurons matched to cortical cell physiology, steady-state behavior is predominant, and ISIs are highly variable at all physiological firing rates and for a wide range of inhibitory and excitatory inputs.
Year
DOI
Venue
1997
10.1162/neco.1997.9.5.971
Neural codes and distributed representations
Keywords
Field
DocType
steady state,random walk
Signal processing,Neuroscience,Visual cortex,Cortical neurons,Random walk,Information transmission,Artificial intelligence,Artificial neural network,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
9
5
0899-7667
ISBN
Citations 
PageRank 
0-262-51100-2
76
23.26
References 
Authors
3
2
Name
Order
Citations
PageRank
Todd W Troyer19027.69
Kenneth D. Miller224777.22