Literature DB >> 21617741

Silicon-Neuron Design: A Dynamical Systems Approach.

John V Arthur1, Kwabena Boahen.   

Abstract

We present an approach to design spiking silicon neurons based on dynamical systems theory. Dynamical systems theory aids in choosing the appropriate level of abstraction, prescribing a neuron model with the desired dynamics while maintaining simplicity. Further, we provide a procedure to transform the prescribed equations into subthreshold current-mode circuits. We present a circuit design example, a positive-feedback integrate-and-fire neuron, fabricated in 0.25 μm CMOS. We analyze and characterize the circuit, and demonstrate that it can be configured to exhibit desired behaviors, including spike-frequency adaptation and two forms of bursting.

Entities:  

Year:  2011        PMID: 21617741      PMCID: PMC3100558          DOI: 10.1109/TCSI.2010.2089556

Source DB:  PubMed          Journal:  IEEE Trans Circuits Syst I Regul Pap        ISSN: 1549-8328            Impact factor:   3.605


  19 in total

1.  Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks.

Authors:  Alfonso Renart; Pengcheng Song; Xiao-Jing Wang
Journal:  Neuron       Date:  2003-05-08       Impact factor: 17.173

2.  Adaptive exponential integrate-and-fire model as an effective description of neuronal activity.

Authors:  Romain Brette; Wulfram Gerstner
Journal:  J Neurophysiol       Date:  2005-07-13       Impact factor: 2.714

3.  A silicon neuron.

Authors:  M Mahowald; R Douglas
Journal:  Nature       Date:  1991 Dec 19-26       Impact factor: 49.962

4.  Thermodynamically equivalent silicon models of voltage-dependent ion channels.

Authors:  Kai M Hynna; Kwabena Boahen
Journal:  Neural Comput       Date:  2007-02       Impact factor: 2.026

5.  Synchrony in silicon: the gamma rhythm.

Authors:  John V Arthur; Kwabena A Boahen
Journal:  IEEE Trans Neural Netw       Date:  2007-11

6.  A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity.

Authors:  Giacomo Indiveri; Elisabetta Chicca; Rodney Douglas
Journal:  IEEE Trans Neural Netw       Date:  2006-01

7.  Dynamics and bifurcations of the adaptive exponential integrate-and-fire model.

Authors:  Jonathan Touboul; Romain Brette
Journal:  Biol Cybern       Date:  2008-11-15       Impact factor: 2.086

8.  Intensity versus identity coding in an olfactory system.

Authors:  Mark Stopfer; Vivek Jayaraman; Gilles Laurent
Journal:  Neuron       Date:  2003-09-11       Impact factor: 17.173

9.  Low-amplitude oscillations in the inferior olive: a model based on electrical coupling of neurons with heterogeneous channel densities.

Authors:  Y Manor; J Rinzel; I Segev; Y Yarom
Journal:  J Neurophysiol       Date:  1997-05       Impact factor: 2.714

Review 10.  Neuromorphic analogue VLSI.

Authors:  R Douglas; M Mahowald; C Mead
Journal:  Annu Rev Neurosci       Date:  1995       Impact factor: 12.449

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  10 in total

1.  A recurrent neural network for closed-loop intracortical brain-machine interface decoders.

Authors:  David Sussillo; Paul Nuyujukian; Joline M Fan; Jonathan C Kao; Sergey D Stavisky; Stephen Ryu; Krishna Shenoy
Journal:  J Neural Eng       Date:  2012-03-19       Impact factor: 5.379

2.  Spiking Neural Network Decoder for Brain-Machine Interfaces.

Authors:  Julie Dethier; Vikash Gilja; Paul Nuyujukian; Shauki A Elassaad; Krishna V Shenoy; Kwabena Boahen
Journal:  Int IEEE EMBS Conf Neural Eng       Date:  2011

3.  A scalable neuristor built with Mott memristors.

Authors:  Matthew D Pickett; Gilberto Medeiros-Ribeiro; R Stanley Williams
Journal:  Nat Mater       Date:  2012-12-16       Impact factor: 43.841

4.  A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm.

Authors:  Julie Dethier; Paul Nuyujukian; Chris Eliasmith; Terry Stewart; Shauki A Elassaad; Krishna V Shenoy; Kwabena Boahen
Journal:  Adv Neural Inf Process Syst       Date:  2011

5.  Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces.

Authors:  Julie Dethier; Paul Nuyujukian; Stephen I Ryu; Krishna V Shenoy; Kwabena Boahen
Journal:  J Neural Eng       Date:  2013-04-10       Impact factor: 5.379

6.  Neuromorphic log-domain silicon synapse circuits obey bernoulli dynamics: a unifying tutorial analysis.

Authors:  Konstantinos I Papadimitriou; Shih-Chii Liu; Giacomo Indiveri; Emmanuel M Drakakis
Journal:  Front Neurosci       Date:  2015-01-20       Impact factor: 4.677

7.  Neuromorphic Implementation of Attractor Dynamics in a Two-Variable Winner-Take-All Circuit with NMDARs: A Simulation Study.

Authors:  Hongzhi You; Da-Hui Wang
Journal:  Front Neurosci       Date:  2017-02-07       Impact factor: 4.677

8.  BrainFreeze: Expanding the Capabilities of Neuromorphic Systems Using Mixed-Signal Superconducting Electronics.

Authors:  Paul Tschirhart; Ken Segall
Journal:  Front Neurosci       Date:  2021-12-21       Impact factor: 4.677

Review 9.  Qualitative-Modeling-Based Silicon Neurons and Their Networks.

Authors:  Takashi Kohno; Munehisa Sekikawa; Jing Li; Takuya Nanami; Kazuyuki Aihara
Journal:  Front Neurosci       Date:  2016-06-15       Impact factor: 4.677

10.  Generalized reconfigurable memristive dynamical system (MDS) for neuromorphic applications.

Authors:  Mohammad Bavandpour; Hamid Soleimani; Bernabé Linares-Barranco; Derek Abbott; Leon O Chua
Journal:  Front Neurosci       Date:  2015-11-03       Impact factor: 4.677

  10 in total

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