Qualitative switches in single-neuron spike dynamics on neuromorphic hardware: implementation, impact on network synchronization and relevance for plasticity

Abstract

Abstract Most efforts on spike-based learning on neuromorphic hardware focus on synaptic plasticity and do not yet exploit the potential of altering the spike-generating dynamics themselves. Biological neurons show distinct mechanisms of spike generation, which affect single-neuron and network computations. Such a variety of spiking mechanisms can only be mimicked on chips with more advanced, nonlinear single-neuron dynamics than the commonly implemented leaky integrate-and-fire (LIF) neurons. Here, we demonstrate that neurons on the BrainScaleS-2 chip configured for exponential leaky integrate-and-fire (EIF) dynamics can be tuned to undergo a qualitative switch in spike generation via a modulation of the reset voltage. This switch is accompanied by altered synchronization properties of neurons in a network and thereby captures a main characteristic of the unfolding of the saddle-node loop (SNL) bifurcation - a qualitative transition that was recently demonstrated in biological neurons. Using this switch, cell-intrinsic properties alone provide a means to control whether small networks of all-to-all coupled neurons on the chip exhibit synchronized firing or splayed-out spiking patterns. We use an example from a central pattern generating circuit in the fruitfly to show that such dynamics can be induced and controlled on the chip. Our study thereby demonstrates the potential of neuromorphic chips with relatively complex and tunable single-neuron dynamics such as the BrainScaleS-2 chip, to generate computationally distinct single unit dynamics. We conclude with a discussion of the utility of versatile spike-generating mechanisms on neuromorphic chips.