A new VLSI model of neural microcircuits including spike time dependent plasticity

This work presents a new VLSI model for biological neural systems, a unified research tool for neuro- as well as computer science. It allows construction of neural microcircuits close to the biological specimen while maintaining a speed several orders faster than real time. The synapse model includes an implementation of spike time dependent plasticity (STDP). Therefore, the VLSI system allows the investigation of key aspects of plasticity without a speed penalty. Additionally, this system is a research tool for new concepts of information processing like liquid or any-time computing. The analog, continuous-time operation of the neuron is implemented in a contemporary deep-submicron process technology. Thereby it realizes a powerful computing system that is not based on the Turing paradigm.

[1]  Henry Markram,et al.  Spike frequency adaptation and neocortical rhythms. , 2002, Journal of neurophysiology.

[2]  D. McCormick,et al.  Neurotransmitter control of neocortical neuronal activity and excitability. , 1993, Cerebral cortex.

[3]  H. Markram,et al.  Organizing principles for a diversity of GABAergic interneurons and synapses in the neocortex. , 2000, Science.

[4]  Carsten Wolff,et al.  PCNN neurocomputers - Event driven and parallel architectures , 2002, ESANN.

[5]  DestexheAlain Conductance-based integrate-and-fire models , 1997 .

[6]  Alain Destexhe,et al.  Conductance-Based Integrate-and-Fire Models , 1997, Neural Computation.

[7]  A. Destexhe,et al.  The high-conductance state of neocortical neurons in vivo , 2003, Nature Reviews Neuroscience.

[8]  K. Meier,et al.  RUPRECHT-KARLS-UNIVERSITÄT HEIDELBERG , 2002 .

[9]  Jacques Gautrais,et al.  SpikeNET: A simulator for modeling large networks of integrate and fire neurons , 1999, Neurocomputing.

[10]  H. Markram,et al.  Differential signaling via the same axon of neocortical pyramidal neurons. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[12]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[13]  Johannes Schemmel,et al.  A VLSI Implementation of an Analog Neural Network Suited for Genetic Algorithms , 2001, ICES.

[14]  Misha Mahowald,et al.  A Spike Based Learning Neuron in Analog VLSI , 1996, NIPS.

[15]  G. Bi,et al.  Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.

[16]  A. Destexhe,et al.  The discharge variability of neocortical neurons during high-conductance states , 2003, Neuroscience.

[17]  Sylvie Renaud,et al.  Analog Electronic System for Simulating Biological Neurons , 1999, IWANN.

[18]  Tim Schönauer,et al.  Simulation of a digital neuro-chip for spiking neural networks , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[19]  L. Abbott,et al.  Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.

[20]  David McLaughlin,et al.  States of High Conductance in a Large-Scale Model of the Visual Cortex , 2002, Journal of Computational Neuroscience.

[21]  Henry Markram,et al.  A Model for Real-Time Computation in Generic Neural Microcircuits , 2002, NIPS.

[22]  Johannes Schemmel,et al.  A Mixed-Mode Analog Neural Network Using Current-Steering Synapses , 2004 .