Spikes: exploring the neural code
Spikes: exploring the neural code
Pulsed Neural Networks
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning
Neural Computation
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
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In this paper, we attempt to implement classical conditioning with spiking neurons instead of connectionist neural networks. The neuron model used is a leaky linear integrate-and-fire model with a learning algorithm combining spike-time dependent Hebbian learning and spiketime dependent anti-Hebbian learning. Experimental results show that the major phenomena of classical conditioning, including Pavlovian conditioning, extinction, partial conditioning, blocking, inhibitory conditioning, overshadow and secondary conditioning, can be implemented by the spiking neuron model proposed here and further indicate that spiking neuron models are well suited to implementing classical conditioning.