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Found 4 papers, 2 papers with code
Enhancing efficiency of object recognition in different categorization levels by reinforcement learning in modular spiking neural networks
The human visual system contains a hierarchical sequence of modules that take part in visual perception at superordinate, basic, and subordinate categorization levels.
SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks with at most one Spike per Neuron
Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and energy-efficient.
Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks
We trained it using a combination of spike-timing-dependent plasticity (STDP) for the lower layers and reward-modulated STDP (R-STDP) for the higher ones.
First-spike based visual categorization using reward-modulated STDP
In the highest layers, each neuron was assigned to an object category, and it was assumed that the stimulus category was the category of the first neuron to fire.
