Analog electronic system for simulating biological neurons

Douence, Vincent; Laflaquiere, A.; Masson, Sylvie Le; Bal, Thierry; Masson, Gwendal Le

doi:10.1007/bfb0100485

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Currently, VLSI is the only physical system with which it is feasible to model a neural circuit. Several such implementations of neurons and synapses have been reported [1] [2]. In these cases the motivation was not primarily the speed but the continuoustime behavior.…”

Section: Introductionmentioning

confidence: 99%

“…If the membrane potential reaches a threshold voltage, a spike generation process will be triggered. To reproduce near-threshold behavior observed Johannes Schemmel, Andreas Grübl, Karlheinz Meier and Eilif Mueller are with the Kirchhoff Institute for Physics, University of Heidelberg, Im Neuenheimer Feld 227, 69120 Heidelberg, Germany (email: schemmel@kip.uni-heidelberg.de) 1 very large scale integration experimentally [6], an integrate-and-fire model is not adequate. Therefore the neuron circuit was designed in a way that it depends not only on the membrane voltage, but on its derivative as well.…”

Section: Introductionmentioning

confidence: 99%

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Implementing Synaptic Plasticity in a VLSI Spiking Neural Network Model

Schemmel

Grübl

Meier

et al. 2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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Abstract-This paper describes an area-efficient mixed-signal implementation of synapse-based long term plasticity realized in a VLSI 1 model of a spiking neural network. The artificial synapses are based on an implementation of spike time dependent plasticity (STDP). In the biological specimen, STDP is a mechanism acting locally in each synapse. The presented electronic implementation succeeds in maintaining this high level of parallelism and simultaneously achieves a synapse density of more than 9k synapses per mm 2 in a 180 nm technology. This allows the construction of neural micro-circuits close to the biological specimen while maintaining a speed several orders of magnitude faster than biological real time. The large acceleration factor enhances the possibilities to investigate key aspects of plasticity, e.g. by performing extensive parameter searches.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implementing Synaptic Plasticity in a VLSI Spiking Neural Network Model

Schemmel

Grübl

Meier

et al. 2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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“…One innovation in those systems is the fact that artificial neurons are integrated on custom analog VLSI circuits (ASICs). These neuromimetic ASICs compute in real-time the electrical activity of neurons, represented by the voltage difference across their membrane (V mem ) [4].…”

Section: Introductionmentioning

confidence: 99%

Design of a modular and mixed neuromimetic ASIC

Tomas

Bornat

Saïghi

et al. 2006

2006 13th IEEE International Conference on Electronics, Circuits and Systems

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This paper presents a new specific integrated circuit (ASIC) that emulates neurons electrical activity using a biophysical model (neuromimetic ASIC). Such ASICs form the computation core of a complete simulation system dedicated to the investigation of the dynamics of biomimetic neural networks. The circuits were designed using a modular approach. Simulations were realized by mixing the behavioral and the transistor-level description of modules. We present in this paper the ASICs specifications and architecture, together with simulation results.

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