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Behavioral/Systems/Cognitive Spike Count Reliability and the Poisson Hypothesis
 

Summary: Behavioral/Systems/Cognitive
Spike Count Reliability and the Poisson Hypothesis
Asohan Amarasingham,1 Ting-Li Chen,1 Stuart Geman,1 Matthew T. Harrison,1 and David L. Sheinberg2
1Division of Applied Mathematics, and 2Department of Neuroscience, Brown University, Providence, Rhode Island 02912
The variability of cortical activity in response to repeated presentations of a stimulus has been an area of controversy in the ongoing
debateregardingtheevidenceforfinetemporalstructureinnervoussystemactivity.Wepresentanewstatisticaltechniqueforassessing
the significance of observed variability in the neural spike counts with respect to a minimal Poisson hypothesis, which avoids the
conventionalbuttroublingassumptionthatthespikingprocessisidenticallydistributedacrosstrials.Weapplythemethodtorecordings
of inferotemporal cortical neurons of primates presented with complex visual stimuli. On this data, the minimal Poisson hypothesis is
rejected: the neuronal responses are too reliable to be fit by a typical firing-rate model, even allowing for sudden, time-varying, and
trial-dependent rate changes after stimulus onset. The statistical evidence favors a tightly regulated stimulus response in these neurons,
close to stimulus onset, although not further away.
Keywords:inferotemporalcortex;spiketrains;temporalcoding;spiketrainanalysis;trial-to-trialvariability;regularity;Poissonhypoth-
esis test; non-Poisson spiking; vision; object recognition
Introduction
The variability of spike trains bears on theories of neural coding.
A range of hypotheses have been offered. At one extreme, the
existence and precise temporal location of every spike is signifi-
cant. At another extreme, a spike train is a stochastic process
essentially characterized by a slowly changing rate. The latter hy-

  

Source: Andrzejak, Ralph Gregor - Departament de Tecnologia, Universitat Pompeu Fabra
Bienenstock, Elie - Department of Neuroscience, Brown University
Brown University, Division of Applied Mathematics, Pattern Theory Group

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences; Mathematics