First-spike based visual categorization using reward-modulated STDP

Understand

Reinforcement learning (RL) has recently regained popularity, with major achievements such as beating the European game of Go champion.

• Here, for the first time, we show that RL can be used efficiently to train a spiking neural network (SNN) to perform object recognition in natural images without using an external classifier.
• We used a feedforward convolutional SNN and a temporal coding scheme where the most strongly activated neurons fire first, while less activated ones fire later, or not at all.
• 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.