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

We study a range of neural dynamics under variations in biophysical parameters underlying extended Morris-Lecar and Hodgkin-Huxley models in three gating variables. The extended models are implemented in NeuroDyn, a four neuron, twelve synapse continuous-time analog VLSI programmable neural emulation platform with generalized channel kinetics and biophysical membrane dynamics. The dynamics exhibit a wide range of time scales extending beyond 100 ms neglected in typical silicon models of tonic spiking neurons. Circuit simulations and measurements show transition from tonic spiking to tonic bursting dynamics through variation of a single conductance parameter governing calcium recovery. We similarly demonstrate transition from graded to all-or-none neural excitability in the onset of spiking dynamics through the variation of channel kinetic parameters governing the speed of potassium activation. Other combinations of variations in conductance and channel kinetic parameters give rise to phasic spiking and spike frequency adaptation dynamics. The NeuroDyn chip consumes 1.29 mW and occupies 3 mm × 3 mm in 0.5 μm CMOS, supporting emerging developments in neuromorphic silicon-neuron interfaces.

[1]  Terrence J. Sejnowski,et al.  Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism , 1994, Journal of Computational Neuroscience.

[2]  Yannis Tsividis,et al.  A reconfigurable VLSI neural network , 1992 .

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

[4]  Ralph Etienne-Cummings,et al.  A switched capacitor implementation of the generalized linear integrate-and-fire neuron , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[5]  S. Doi,et al.  Parameter estimation of various Hodgkin-Huxley-type neuronal models using a gradient-descent learning method , 2002, Proceedings of the 41st SICE Annual Conference. SICE 2002..

[6]  Arindam Basu,et al.  Dynamics and Bifurcations in a Silicon Neuron , 2010, IEEE Transactions on Biomedical Circuits and Systems.

[7]  Teresa H. Y. Meng,et al.  Adaptive Resolution ADC Array for an Implantable Neural Sensor , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[8]  Gert Cauwenberghs,et al.  Analog VLSI Biophysical Neurons and Synapses With Programmable Membrane Channel Kinetics , 2010, IEEE Transactions on Biomedical Circuits and Systems.

[9]  Gert Cauwenberghs,et al.  A floating-gate programmable array of silicon neurons for central pattern generating networks , 2006, 2006 IEEE International Symposium on Circuits and Systems.

[10]  Yannick Bornat,et al.  Neuromimetic ICs and system for parameters extraction in biological neuron models , 2006, 2006 IEEE International Symposium on Circuits and Systems.

[11]  C. Stevens,et al.  Prediction of repetitive firing behaviour from voltage clamp data on an isolated neurone soma , 1971, The Journal of physiology.

[12]  Mohsen Mollazadeh,et al.  A VLSI Neural Monitoring System With Ultra-Wideband Telemetry for Awake Behaving Subjects , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[13]  T. Sejnowski,et al.  Pyramidal neurons switch from integrators in vitro to resonators under in vivo-like conditions. , 2008, Journal of neurophysiology.

[14]  Mohamad Sawan,et al.  IEEE Transactions on Biomedical Circuits and Systems , 2018, IEEE Transactions on Biomedical Circuits and Systems.

[15]  Teresa Ree Chay,et al.  Modeling Slowly Bursting Neurons via Calcium Store and Voltage-Independent Calcium Current , 1996, Neural Computation.

[16]  Ernst Niebur,et al.  A Generalized Linear Integrate-and-Fire Neural Model Produces Diverse Spiking Behaviors , 2009, Neural Computation.

[17]  G. Lawton Why do we sleep? , 2000, Nature Neuroscience.

[18]  Terrence J. Sejnowski,et al.  Interactive reportWhy do we sleep?1 , 2000 .

[19]  R. Harris-Warrick In: Dynamic Biological Networks: The Stomatogastric Nervous System , 1992 .

[20]  Hillel J. Chiel,et al.  Ultra-Low-Power and Robust Digital-Signal-Processing Hardware for Implantable Neural Interface Microsystems , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[21]  Eugene M. Izhikevich,et al.  Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting , 2006 .

[22]  Gert Cauwenberghs,et al.  A subthreshold aVLSI implementation of the Izhikevich simple neuron model , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[23]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[24]  Gert Cauwenberghs,et al.  Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.

[25]  Giacomo Indiveri,et al.  A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity , 2006, IEEE Transactions on Neural Networks.

[26]  Arindam Basu,et al.  Neural Dynamics in Reconfigurable Silicon , 2010, IEEE Transactions on Biomedical Circuits and Systems.

[27]  Gert Cauwenberghs,et al.  Analog VLSI neuromorphic network with programmable membrane channel kinetics , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[28]  C. Morris,et al.  Voltage oscillations in the barnacle giant muscle fiber. , 1981, Biophysical journal.

[29]  Gert Cauwenberghs,et al.  Biophysical neural spiking and bursting dynamics in reconfigurable analog VLSI , 2010, 2010 Biomedical Circuits and Systems Conference (BioCAS).

[30]  Gert Cauwenberghs,et al.  Dynamically Reconfigurable Silicon Array of Spiking Neurons With Conductance-Based Synapses , 2007, IEEE Transactions on Neural Networks.

[31]  Gert Cauwenberghs,et al.  Biophysical synaptic dynamics in an analog VLSI network of hodgkin-huxley neurons , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  Yannick Bornat,et al.  A Library of Analog Operators Based on the Hodgkin-Huxley Formalism for the Design of Tunable, Real-Time, Silicon Neurons , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[33]  S.P. DeWeerth,et al.  Two-Dimensional Variation of Bursting Properties in a Silicon-Neuron Half-Center Oscillator , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[34]  N. Urban,et al.  Intrinsic biophysical diversity decorrelates neuronal firing while increasing information content , 2010, Nature Neuroscience.

[35]  Craig T. Jin,et al.  A log-domain implementation of the Izhikevich neuron model , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[36]  Christof Koch,et al.  Detecting and Estimating Signals in Noisy Cable Structures, I: Neuronal Noise Sources , 1999, Neural Computation.

[37]  Yi Dong,et al.  Optimization Methods for Spiking Neurons and Networks , 2010, IEEE Transactions on Neural Networks.

[38]  Massimiliano Giulioni,et al.  An aVLSI recurrent network of spiking neurons with reconfigurable and plastic synapses , 2006, 2006 IEEE International Symposium on Circuits and Systems.

[39]  Sylvie Renaud,et al.  Automated Parameter Estimation of the Hodgkin-Huxley Model Using the Differential Evolution Algorithm: Application to Neuromimetic Analog Integrated Circuits , 2011, Neural Computation.

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

[41]  Piotr Dudek,et al.  Spiking and Bursting Firing Patterns of a Compact VLSI Cortical Neuron Circuit , 2007, 2007 International Joint Conference on Neural Networks.

[42]  Giacomo Indiveri,et al.  A Systematic Method for Configuring VLSI Networks of Spiking Neurons , 2011, Neural Computation.