Using this learning algorithm, we demonstrate how networks of spiking neurons with biologically plau- sible time-constants can perform complex non-linear ...

This paper investigates the performance of the parallel differential evolution algorithm, as a supervised training algorithm for spiking neural networks.

Abstract. For a network of spiking neurons with reasonable post- synaptic potentials, we derive a supervised learning rule akin to tradi-.

For a network of spiking neurons that encodes information in the timing of individual spike times, we derive a supervised learning rule, SpikeProp, ...

The paper presents a novel approach to the construction and learning of linear neural networks based on fast orthogonal transforms. The orthogonality of basic ...

For a network of spiking neurons that encodes information in the timing of individual spike times, we derive a supervised learning rule, SpikeProp, ...

By further extending SpikeProp, we propose a backpropagation learning algorithm, which adjusts all the parameters, synaptic weights, synaptic delays, ...

Error backpropagation in networks of spiking neurons (SpikeProp) shows promise for the supervised learning of temporal patterns. However, its widespread use ...

Jun 13, 2020 · We propose a new supervised learning rule for mul- tilayer spiking neural networks (SNNs) that use a form of temporal coding known as ...

Duration: 48:19
Posted: Sep 1, 2020