Characterizing the Firing Properties of an Adaptive Analog VLSI Neuron

We describe the response properties of a compact, low power, analog circuit that implements a model of a leaky–Integrate & Fire (I&F) neuron, with spike-frequency adaptation, refractory period and voltage threshold modulation properties. We investigate the statistics of the circuit’s output response by modulating its operating parameters, like refractory period and adaptation level and by changing the statistics of the input current. The results show a clear match with theoretical prediction and neurophysiological data in a given range of the parameter space. This analysis defines the chip’s parameter working range and predicts its behavior in case of integration into large massively parallel very–large–scale–integration (VLSI) networks.

[1]  Xiao-Jing Wang,et al.  Spike-Frequency Adaptation of a Generalized Leaky Integrate-and-Fire Model Neuron , 2004, Journal of Computational Neuroscience.

[2]  Henry C. Tuckwell,et al.  Introduction to theoretical neurobiology , 1988 .

[3]  E. Culurciello,et al.  Arbitrated address event representation digital image sensor , 2001, 2001 IEEE International Solid-State Circuits Conference. Digest of Technical Papers. ISSCC (Cat. No.01CH37177).

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

[5]  Giacomo Indiveri,et al.  An adaptive silicon synapse , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..

[6]  X J Wang,et al.  Calcium coding and adaptive temporal computation in cortical pyramidal neurons. , 1998, Journal of neurophysiology.

[7]  Giacomo Indiveri,et al.  Neuromorphic selective attention systems , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..

[8]  William R. Softky,et al.  The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[9]  D'Andreagiovanni,et al.  EMERGENT ASYNCHRONOUS , IRREGULAR FIRING IN A DETERMINISTIC ANALOG VLSI RECURRENT NETWORK , 2001 .

[10]  Vittorio Dante,et al.  A VLSI recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory , 2003, IEEE Trans. Neural Networks.

[11]  Shih-Chii Liu,et al.  Analog VLSI: Circuits and Principles , 2002 .

[12]  Carver Mead,et al.  Analog VLSI and neural systems , 1989 .

[13]  Maurizio Mattia,et al.  Collective Behavior of Networks with Linear (VLSI) Integrate-and-Fire Neurons , 1999, Neural Computation.

[14]  J. C. Anderson,et al.  Estimates of the net excitatory currents evoked by visual stimulation of identified neurons in cat visual cortex. , 1998, Cerebral cortex.

[15]  A. van Schaik.  Building blocks for electronic spiking neural networks. , 2001, Neural networks : the official journal of the International Neural Network Society.

[16]  Tor Sverre Lande,et al.  Neuromorphic Systems Engineering , 1998 .

[17]  Simon R. Schultz,et al.  Analogue VLSI 'integrate-and-fire' neuron with frequency adaptation , 1995 .

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

[19]  H. Tuckwell.  Introduction to Theoretical Neurobiology: Linear Cable Theory and Dendritic Structure , 1988 .

[20]  Bard Ermentrout,et al.  Linearization of F-I Curves by Adaptation , 1998, Neural Computation.

[21]  W. Senn,et al.  Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents. , 2003, Journal of neurophysiology.

[22]  Kwabena Boahen,et al.  Communicating neuronal ensembles between neuromorphic chips , 1998 .