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- W1572257634 abstract "A large body of experimental evidence points tosophisticated signal processing taking place at the level ofdendritic trees and dendritic branches of neurons. This evidencesuggests that, in addition to inferring the connectivity betweenneurons, identifying analog dendritic processing in individualcells is fundamentally important to understanding the underlyingprinciples of neural computation. In this thesis, we develop anovel theoretical framework for the identification of dendriticprocessing directly from spike times produced by spiking neurons.The problem setting of spiking neurons is necessary since suchneurons make up the majority of electrically excitable cells inmost nervous systems and it is often hard or even impossible todirectly monitor the activity within dendrites. Thus, actionpotentials produced by neurons often constitute the only causal andobservable correlate of dendritic processing. In order to remaintrue to the underlying biophysics of electrically excitable cells,we employ well-established mechanistic models of action potentialgeneration to describe the nonlinear mapping of the aggregatecurrent produced by the tree into an asynchronous sequence ofspikes. Specific models of spike generation considered includeconductance-based models such as Hodgkin-Huxley, Morris-Lecar,Fitzhugh-Nagumo, as well as simpler models of theintegrate-and-fire and threshold-and-fire type. The aggregatetime-varying current driving the spike generator is taken to beproduced by a dendritic stimulus processor, which is a nonlineardynamical system capable of describing arbitrary linear andnonlinear transformations performed on one or more input stimuli.In the case of multiple stimuli, it can also describe thecross-coupling, or interaction, between various stimulus features.The behavior of the dendritic stimulus processor is fully capturedby one or more kernels, which provide a characterization of thesignal processing that is consistent with the broader cable theorydescription of dendritic trees. We prove that the neuralidentification problem, stated in terms of identifying the kernelsof the dendritic stimulus processor, is mathematically dual to theneural population encoding problem. Specifically, we show that thecollection of spikes produced by a single neuron in multipleexperimental trials can be treated as a single multidimensionalspike train of a population of neurons encoding the parameters ofthe dendritic stimulus processor. Using the theory of sampling inreproducing kernel Hilbert spaces, we then derive precise resultsdemonstrating that, during any experiment, the entire neuralcircuit is projected onto the space of input stimuli and parametersof this projection are faithfully encoded in the spike train. Spiketimes are shown to correspond to generalized samples, ormeasurements, of this projection in a system of coordinates that isnot fixed but is both neuron- and stimulus-dependent. We examinethe theoretical conditions under which it may be possible toreconstruct the dendritic…" @default.
- W1572257634 created "2016-06-24" @default.
- W1572257634 creator A5016276103 @default.
- W1572257634 date "2013-01-01" @default.
- W1572257634 modified "2023-09-23" @default.
- W1572257634 title "Identification of Dendritic Processing in Spiking Neural Circuits" @default.
- W1572257634 doi "https://doi.org/10.7916/d8mw2qj6" @default.
- W1572257634 hasPublicationYear "2013" @default.
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