Spike-Based Compared to Rate-Based Hebbian Learning

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Richard Kempter, Wulfram Gerstner, J. van Hemmen


A correlation-based learning rule at the spike level is formulated, mathematically analyzed, and compared to learning in a firing-rate description. A differential equation for the learning dynamics is derived under the assumption that the time scales of learning and spiking can be separated. For a linear Poissonian neuron model which receives time-dependent stochastic input we show that spike correlations on a millisecond time scale play indeed a role. Corre(cid:173) lations between input and output spikes tend to stabilize structure formation, provided that the form of the learning window is in accordance with Hebb's principle. Conditions for an intrinsic nor(cid:173) malization of the average synaptic weight are discussed.