Thermodynamically Equivalent Silicon Models of Voltage-Dependent Ion Channels

We model ion channels in silicon by exploiting similarities between the thermodynamic principles that govern ion channels and those that govern transistors. Using just eight transistors, we replicatefor the first time in siliconthe sigmoidal voltage dependence of activation (or inactivation) and the bell-shaped voltage-dependence of its time constant. We derive equations describing the dynamics of our silicon analog and explore its flexibility by varying various parameters. In addition, we validate the design by implementing a channel with a single activation variable. The design's compactness allows tens of thousands of copies to be built on a single chip, facilitating the study of biologically realistic models of neural computation at the network level in silicon.

[1]  Kwabena Boahen,et al.  Optic nerve signals in a neuromorphic chip II: testing and results , 2004, IEEE Transactions on Biomedical Engineering.

[2]  J. Rinzel,et al.  Enhancement of Signal-to-Noise Ratio and Phase Locking for Small Inputs by a Low-Threshold Outward Current in Auditory Neurons , 2002, The Journal of Neuroscience.

[3]  T. L. Hill,et al.  On the theory of ion transport across the nerve membrane. VI. Free energy and activation free energies of conformational change. , 1972, Proceedings of the National Academy of Sciences of the United States of America.

[4]  H. Sullivan Ionic Channels of Excitable Membranes, 2nd Ed. , 1992, Neurology.

[5]  Stephen P. DeWeerth,et al.  A multiconductance silicon neuron with biologically matched dynamics , 2004, IEEE Transactions on Biomedical Engineering.

[6]  R. Shapley,et al.  An egalitarian network model for the emergence of simple and complex cells in visual cortex , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[7]  R. Keynes The ionic channels in excitable membranes. , 1975, Ciba Foundation symposium.

[8]  Alain Destexhe,et al.  Nonlinear Thermodynamic Models of Voltage-Dependent Currents , 2000, Journal of Computational Neuroscience.

[9]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[10]  T. L. Hill,et al.  On the theory of ion transport across the nerve membrane. IV. Noise from the open-close kinetics of K + channels. , 1972, Biophysical journal.

[11]  M. Häusser,et al.  Determinants of Action Potential Propagation in Cerebellar Purkinje Cell Axons , 2005, The Journal of Neuroscience.

[12]  R. Douglas,et al.  A silicon neuron , 1991, Nature.

[13]  Kai M. Hynna,et al.  T channel dynamics in a silicon LGN , 2005 .

[14]  Kwabena Boahen,et al.  Space-rate coding in an adaptive silicon neuron , 2001, Neural Networks.

[15]  C F Stevens,et al.  Interactions between intrinsic membrane protein and electric field. An approach to studying nerve excitability. , 1978, Biophysical journal.

[16]  T. Delbruck 'Bump' circuits for computing similarity and dissimilarity of analog voltages , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[17]  J Rinzel,et al.  Current clamp and modeling studies of low-threshold calcium spikes in cells of the cat's lateral geniculate nucleus. , 1999, Journal of neurophysiology.

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

[19]  D. McCormick,et al.  Properties of a hyperpolarization‐activated cation current and its role in rhythmic oscillation in thalamic relay neurones. , 1990, The Journal of physiology.

[20]  Andreas G. Andreou,et al.  Characterization of subthreshold MOS mismatch in transistors for VLSI systems , 1994, J. VLSI Signal Process..

[21]  R. Llinás The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. , 1988, Science.

[22]  John Guckenheimer,et al.  An Improved Parameter Estimation Method for Hodgkin-Huxley Models , 1999, Journal of Computational Neuroscience.