Paper Info
Title
Single neuron computation: from dynamical system to feature detector.
Abstract
White noise methods are a powerful tool for characterizing the computation performed by neural systems. These methods allow one to identify the feature or features that a neural system extracts from a complex input and to determine how these features are combined to drive the system's spiking response. These methods have also been applied to characterize the input-output relations of single neurons driven by synaptic inputs, simulated by direct current injection. To interpret the results of white noise analysis of single neurons, we would like to understand how the obtained feature space of a single neuron maps onto the biophysical properties of the membrane, in particular, the dynamics of ion channels. Here, through analysis of a simple dynamical model neuron, we draw explicit connections between the output of a white noise analysis and the underlying dynamical system. We find that under certain assumptions, the form of the relevant features is well defined by the parameters of the dynamical system. Further, we show that under some conditions, the feature space is spanned by the spike-triggered average and its successive order time derivatives.
Year
DOI
Venue
2007
10.1162/neco.2007.19.12.3133
Neural Computation
Keywords
Field
DocType
underlying dynamical system,dynamical system,single neuron map,simple dynamical model neuron,relevant feature,single neuron computation,single neuron,feature space,white noise analysis,neural system extract,neural system,data analysis,white noise,dynamic system,ion channel,input output
Feature vector,Biological system,Models of neural computation,White noise,Input/output,Artificial intelligence,Artificial neural network,Mathematics,Dynamical system,Machine learning,Computation
Journal
Volume
Issue
ISSN
19
12
0899-7667
Citations 
PageRank 
References 
15
1.37
7
Authors
3
Name
Order
Citations
PageRank
Sungho Hong118112.78
Blaise Agüera y Arcas2151.37
Adrienne L. Fairhall313316.10