A population-level approach to temperature robustness in neuromorphic systems

Abstract

We present a novel approach to achieving temperature-robust behavior in neuromorphic systems that operates at the population level, trading an increase in silicon-neuron count for robustness across temperature. Our silicon neurons' tuning curves were highly sensitive to temperature, which could be decoded from a 400-neuron population with a precision of 0.07° C. We overcame this temperature-sensitivity by combining methods from robust optimization theory with the Neural Engineering Framework. We developed two algorithms and compared their temperature-robustness across a range of 2° C by decoding one period of a sinusoid-like function from populations with 25 to 800 neurons. We find that 560 neurons are required to achieve the same precision across this temperature range as 35 neurons achieved at a single temperature.