Literature DB >> 14765707

A multiconductance silicon neuron with biologically matched dynamics.

Mario F Simoni1, Gennady S Cymbalyuk, Michael E Sorensen, Ronald L Calabrese, Stephen P DeWeerth.   

Abstract

We have designed, fabricated, and tested an analog integrated-circuit architecture to implement the conductance-based dynamics that model the electrical activity of neurons. The dynamics of this architecture are in accordance with the Hodgkin-Huxley formalism, a widely exploited, biophysically plausible model of the dynamics of living neurons. Furthermore the architecture is modular and compact in size so that we can implement networks of silicon neurons, each of desired complexity, on a single integrated circuit. We present in this paper a six-conductance silicon-neuron implementation, and characterize it in relation to the Hodgkin-Huxley formalism. This silicon neuron incorporates both fast and slow ionic conductances, which are required to model complex oscillatory behaviors (spiking, bursting, subthreshold oscillations).

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Year:  2004        PMID: 14765707     DOI: 10.1109/TBME.2003.820390

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  12 in total

Review 1.  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

2.  Transistor analogs of emergent iono-neuronal dynamics.

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

3.  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

Review 4.  Spinal cord injury: present and future therapeutic devices and prostheses.

Authors:  Simon F Giszter
Journal:  Neurotherapeutics       Date:  2008-01       Impact factor: 7.620

5.  Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI.

Authors:  T Yu; T J Sejnowski; G Cauwenberghs
Journal:  IEEE Trans Biomed Circuits Syst       Date:  2011-10-13       Impact factor: 3.833

6.  Tunable neuromimetic integrated system for emulating cortical neuron models.

Authors:  Filippo Grassia; Laure Buhry; Timothée Lévi; Jean Tomas; Alain Destexhe; Sylvain Saïghi
Journal:  Front Neurosci       Date:  2011-12-07       Impact factor: 4.677

7.  Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments.

Authors:  Matthieu Ambroise; Timothée Levi; Sébastien Joucla; Blaise Yvert; Sylvain Saïghi
Journal:  Front Neurosci       Date:  2013-11-21       Impact factor: 4.677

8.  Optoelectronic system for brain neuronal network stimulation.

Authors:  Mikhail A Mishchenko; Svetlana A Gerasimova; Albina V Lebedeva; Lyubov S Lepekhina; Alexander N Pisarchik; Victor B Kazantsev
Journal:  PLoS One       Date:  2018-06-01       Impact factor: 3.240

9.  An FPGA-Based Silicon Neuronal Network with Selectable Excitability Silicon Neurons.

Authors:  Jing Li; Yuichi Katori; Takashi Kohno
Journal:  Front Neurosci       Date:  2012-12-24       Impact factor: 4.677

10.  A codimension-2 bifurcation controlling endogenous bursting activity and pulse-triggered responses of a neuron model.

Authors:  William H Barnett; Gennady S Cymbalyuk
Journal:  PLoS One       Date:  2014-01-31       Impact factor: 3.240

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