Methods for applying the Neural Engineering Framework to neuromorphic hardware
This work addresses the challenge of efficiently mapping neural models to neuromorphic hardware for researchers in neuromorphic computing, but it is incremental as it summarizes and extends prior methods.
The paper reviews software tools and theoretical methods for applying the Neural Engineering Framework to neuromorphic hardware, enabling implementation of linear and nonlinear dynamical systems with features like time-delays and nonideal synapses.
We review our current software tools and theoretical methods for applying the Neural Engineering Framework to state-of-the-art neuromorphic hardware. These methods can be used to implement linear and nonlinear dynamical systems that exploit axonal transmission time-delays, and to fully account for nonideal mixed-analog-digital synapses that exhibit higher-order dynamics with heterogeneous time-constants. This summarizes earlier versions of these methods that have been discussed in a more biological context (Voelker & Eliasmith, 2017) or regarding a specific neuromorphic architecture (Voelker et al., 2017).