Spatiotemporal Spike-Pattern Selectivity in Single Mixed-Signal Neurons with Balanced Synapses

Mattias Nilsson, Foteini Liwicki, Fredrik Sandin

Realizing the potential of mixed-signal neuromorphic processors for ultra-low-power inference and learning requires efficient use of their inhomogeneous analog circuitry as well as sparse, time-based information encoding and processing. Here, we investigate spike-timing-based spatiotemporal receptive fields of output-neurons in the Spatiotemporal Correlator (STC) network, for which we used excitatory-inhibitory balanced disynaptic inputs instead of dedicated axonal or neuronal delays. We present hardware-in-the-loop experiments with a mixed-signal DYNAP-SE neuromorphic processor, in which five-dimensional receptive fields of hardware neurons were mapped by randomly sampling input spike-patterns from a uniform distribution. We find that, when the balanced disynaptic elements are randomly programmed, some of the neurons display distinct receptive fields. Furthermore, we demonstrate how a neuron was tuned to detect a particular spatiotemporal feature, to which it initially was non-selective, by activating a different subset of the inhomogeneous analog synaptic circuits. The energy dissipation of the balanced synaptic elements is one order of magnitude lower per lateral connection (0.65 nJ vs 9.3 nJ per spike) than former delay-based neuromorphic hardware implementations. Thus, we show how the inhomogeneous synaptic circuits could be utilized for resource-efficient implementation of STC network layers, in a way that enables synapse-address reprogramming as a discrete mechanism for feature tuning.

Knowledge Graph



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