Spike-timing-dependent plasticity for neurons with recurrent connections
- 148 Downloads
The dynamics of the learning equation, which describes the evolution of the synaptic weights, is derived in the situation where the network contains recurrent connections. The derivation is carried out for the Poisson neuron model. The spiking-rates of the recurrently connected neurons and their cross-correlations are determined self- consistently as a function of the external synaptic inputs. The solution of the learning equation is illustrated by the analysis of the particular case in which there is no external synaptic input. The general learning equation and the fixed-point structure of its solutions is discussed.
KeywordsSynaptic Weight Recurrent Network Hebbian Learning Recurrent Connection Output Spike
Unable to display preview. Download preview PDF.
- Hebb DO (1949) The organization of behavior. Wiley, New YorkGoogle Scholar
- Hawkes AG (1971) Point spectra of some mutually exciting point processes. J Roy Stat Soc B 33:438–443Google Scholar
- Lamperti J (1966) Probability, 2nd edn. Benjamin, New York. (1996) Wiley, New YorkGoogle Scholar
- Sanders JA, Verhulst F (1985) Averaging methods in nonlinear dynamical systems. Springer, BerlinGoogle Scholar
- Van Hemmen JL (2001) Theory of synaptic plasticity. In: Moss F, Gielen S (eds) Handbook of biophysics, vol 4. Elsevier, Amsterdam, pp 771–823Google Scholar