Analog low-power hardware implementation of a Laguerre-Volterra model of intracellular subthreshold neuronal activity.
The right level of abstraction for a model mimicking a neural function is often difficult to determine. There are trade-offs between capturing biological complexities on one hand and the scalability and efficiency of the model on the other. In this work, we describe a nonlinear Laguerre-Volterra model of the synaptic temporal integration of input spikes to postsynaptic potentials. This model is then efficiently implemented using analog subthreshold circuits and can serve as a foundation for future large-scale hardware systems that can emulate multi-input multi-output (MIMO) spike transformations in populations of neurons. The normalized mean square error in estimating real data using the circuit implementation of this model is less than 15%. The model components are modular and its parameters are adjustable for modeling temporal integration by neurons in other brain regions. The total power consumption of this nonlinear Laguerre-Volterra system is less than 5nW.
Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA. vghaderi@usc.edu
Viviane S
VS
Ghaderi
Shane
S
Roach
Dong
D
Song
Vasilis Z
VZ
Marmarelis
John
J
Choma
Theodore W
TW
Berger
D000200:Action Potentials; D000818:Animals; D001921:Brain / Q000502:physiology; D056547:CA1 Region, Hippocampal / Q000502:physiology; D056654:CA3 Region, Hippocampal / Q000502:physiology; D055724:Electrophysiological Phenomena; D006801:Humans; D008959:Models, Neurological; D058117:Neural Prostheses; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted; D014173:Transistors, Electronic