Literature DB >> 17206867

Thermodynamically equivalent silicon models of voltage-dependent ion channels.

Kai M Hynna1, Kwabena Boahen.   

Abstract

We model ion channels in silicon by exploiting similarities between the thermodynamic principles that govern ion channels and those that govern transistors. Using just eight transistors, we replicate--for the first time in silicon--the sigmoidal voltage dependence of activation (or inactivation) and the bell-shaped voltage-dependence of its time constant. We derive equations describing the dynamics of our silicon analog and explore its flexibility by varying various parameters. In addition, we validate the design by implementing a channel with a single activation variable. The design's compactness allows tens of thousands of copies to be built on a single chip, facilitating the study of biologically realistic models of neural computation at the network level in silicon.

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Year:  2007        PMID: 17206867     DOI: 10.1162/neco.2007.19.2.327

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  8 in total

1.  A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity.

Authors:  Guy Rachmuth; Harel Z Shouval; Mark F Bear; Chi-Sang Poon
Journal:  Proc Natl Acad Sci U S A       Date:  2011-11-16       Impact factor: 11.205

Review 2.  Neurotech for neuroscience: unifying concepts, organizing principles, and emerging tools.

Authors:  Rae Silver; Kwabena Boahen; Sten Grillner; Nancy Kopell; Kathie L Olsen
Journal:  J Neurosci       Date:  2007-10-31       Impact factor: 6.167

3.  Transistor analogs of emergent iono-neuronal dynamics.

Authors:  Guy Rachmuth; Chi-Sang Poon
Journal:  HFSP J       Date:  2008-04-18

4.  Silicon-Neuron Design: A Dynamical Systems Approach.

Authors:  John V Arthur; Kwabena Boahen
Journal:  IEEE Trans Circuits Syst I Regul Pap       Date:  2011       Impact factor: 3.605

5.  Neuromorphic silicon neuron circuits.

Authors:  Giacomo Indiveri; Bernabé Linares-Barranco; Tara Julia Hamilton; André van Schaik; Ralph Etienne-Cummings; Tobi Delbruck; Shih-Chii Liu; Piotr Dudek; Philipp Häfliger; Sylvie Renaud; Johannes Schemmel; Gert Cauwenberghs; John Arthur; Kai Hynna; Fopefolu Folowosele; Sylvain Saighi; Teresa Serrano-Gotarredona; Jayawan Wijekoon; Yingxue Wang; Kwabena Boahen
Journal:  Front Neurosci       Date:  2011-05-31       Impact factor: 4.677

6.  A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses.

Authors:  Ning Qiao; Hesham Mostafa; Federico Corradi; Marc Osswald; Fabio Stefanini; Dora Sumislawska; Giacomo Indiveri
Journal:  Front Neurosci       Date:  2015-04-29       Impact factor: 4.677

7.  A 4-fJ/Spike Artificial Neuron in 65 nm CMOS Technology.

Authors:  Ilias Sourikopoulos; Sara Hedayat; Christophe Loyez; François Danneville; Virginie Hoel; Eric Mercier; Alain Cappy
Journal:  Front Neurosci       Date:  2017-03-15       Impact factor: 4.677

8.  Ultra-low-power switching circuits based on a binary pattern generator with spiking neurons.

Authors:  Takeaki Yajima
Journal:  Sci Rep       Date:  2022-01-21       Impact factor: 4.379

  8 in total

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