Covariant spatio-temporal receptive fields for spiking neural networks

Understand

Biological nervous systems constitute important sources of inspiration towards computers that are faster, cheaper, and more energy efficient.

• Neuromorphic disciplines view the brain as a coevolved system, simultaneously optimizing the hardware and the algorithms running on it.
• There are clear efficiency gains when bringing the computations into a physical substrate, but we presently lack theories to guide efficient implementations.
• Here, we present a principled computational model for neuromorphic systems in terms of spatio-temporal receptive fields, based on affine Gaussian kernels over space and leaky-integrator and leaky integrate-and-fire models over time.