A programmable analog subthreshold biomimetic model for bi-directional communication with the brain.

In this paper, we present a hardware implementation of a second order Laguerre Expansion of Volterra Kernel (LEV) model with four basis functions. The model is versatile enough to be applied at different abstraction levels (synapse, neuron, or network of neurons) and is implemented with analog building blocks in a modular manner. These analog blocks, realized using low power subthreshold CMOS transistors, can serve as a basis for large-scale hardware systems that emulate multi-input multi-output (MIMO) spike transformations in populations of neurons. The normalized mean square error between the signals produced by the circuit LEV implementation and the ideal LEV model is 8.15%. The total power consumption of the analog circuitry is less than 33nW.

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.