Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits
Buch in deiner Nähe kaufen
Beschreibung
This work is motivated by the ongoing open question of how information in the outside world is represented and processed by the brain. Consequently, several novel methods are developed.
A new mathematical formulation is proposed for the encoding and decoding of analog signals using integrate-and-fire neuron models. Based on this formulation, a novel algorithm, significantly faster than the state-of-the-art method, is proposed for reconstructing the input of the neuron.
Two new identification methods are proposed for neural circuits comprising a filter in series with a spiking neuron model. These methods reduce the number of assumptions made by the state-of-the-art identification framework, allowing for a wider range of models of sensory processing circuits to be inferred directly from input-output observations.
A third contribution is an algorithm that computes the spike time sequence generated by an integrate-and-fire neuron model in response to the output of alinear filter, given the input of the filter encoded with the same neuron model.This work is motivated by the ongoing open question of how information in the outside world is represented and processed by the brain. Consequently, several novel methods are developed.
A new mathematical formulation is proposed for the encoding and decoding of analog signals using integrate-and-fire neuron models. Based on this formulation, a novel algorithm, significantly faster than the state-of-the-art method, is proposed for reconstructing the input of the neuron.
Two new identification methods are proposed for neural circuits comprising a filter in series with a spiking neuron model. These methods reduce the number of assumptions made by the state-of-the-art identification framework, allowing for a wider range of models of sensory processing circuits to be inferred directly from input-output observations.
A third contribution is an algorithm that computes the spike time sequence generated by an integrate-and-fire neuron model in response to the output of alinear filter, given the input of the filter encoded with the same neuron model.Nominated as an outstanding Ph.D. thesis by the University of Sheffield Reformulates, for the first time, the encoding performed by the integrate-and-fire neuron model as a problem of uniform sampling of an auxiliary function on a set of input independent time points Proposes two methodologies for identifying [Nonlinear Filter]-[Ideal IF] and [Linear Filter]-[Leaky IF] circuits Developes for the first time, a direct representation between the input and output of a linear filter, both encoded with an integrate-and-fire neuron model Includes supplementary material: sn.pub/extras Includes supplementary material: sn.pub/extras
Autor*in
Dorian Florescu
Themen in »Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits«
System Identification Spiking Neural Circuits Time Encoding Machines Time Decoding Machines Integrate-and-fire Neurons Uniform Sampling