Parameter estimation of a spiking silicon neuron

Alexander Russell, Kevin Mazurek, Stefan Mihalas, Ernst Niebur, Ralph Etienne-Cummings

Research output: Contribution to journalArticlepeer-review

Abstract

Spiking neuron models are used in a multitude of tasks ranging from understanding neural behavior at its most basic level to neuroprosthetics. Parameter estimation of a single neuron model, such that the model's output matches that of a biological neuron is an extremely important task. Hand tuning of parameters to obtain such behaviors is a difficult and time consuming process. This is further complicated when the neuron is instantiated in silicon (an attractive medium in which to implement these models) as fabrication imperfections make the task of parameter configuration more complex. In this paper we show two methods to automate the configuration of a silicon (hardware) neuron's parameters. First, we show how a Maximum Likelihood method can be applied to a leaky integrate and fire silicon neuron with spike induced currents to fit the neuron's output to desired spike times. We then show how a distance based method which approximates the negative log likelihood of the lognormal distribution can also be used to tune the neuron's parameters. We conclude that the distance based method is better suited for parameter configuration of silicon neurons due to its superior optimization speed.

Original languageEnglish (US)
Article number2182650
Pages (from-to)133-141
Number of pages9
JournalIEEE Transactions on Biomedical Circuits and Systems
Volume6
Issue number2
DOIs
StatePublished - Apr 2012

Keywords

  • Neuromorphic
  • parameter estimation
  • silicon neuron

ASJC Scopus subject areas

  • Biomedical Engineering
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Parameter estimation of a spiking silicon neuron'. Together they form a unique fingerprint.

Cite this