Faramarz Faghihi

This paper introduces a deep spiking neural network for detecting auditory spatial attention from single-trial EEG, achieving 90% accuracy with 10% data.

This paper introduces a neuroscience-inspired spiking neural network with feedback inhibition that enhances pattern separation and demonstrates its application in cognitive robotics.

This paper introduces a spiking neural network for detecting auditory spatial attention from single-trial EEG data, achieving 90% accuracy with only 10% of the data.

This paper introduces a brain-inspired neural network with non-synaptic memory for handwritten digit classification, achieving 86% accuracy using full training data.

This paper develops a spiking neural network that learns hand-written digits from few examples, inspired by neuroscience mechanisms, and achieves efficient learning with minimal training data.