Optimal information storage and the distribution of synaptic weights: perceptron versus Purkinje cell.
It is widely believed that synaptic modifications underlie learning and memory. However, few studies have examined what can be deduced about the learning process from the distribution of synaptic weights. We analyze the perceptron, a prototypical feedforward neural network, and obtain the optimal synaptic weight distribution for a perceptron with excitatory synapses. It contains more than 50% silent synapses, and this fraction increases with storage reliability: silent synapses are therefore a necessary byproduct of optimizing learning and reliability. Exploiting the classical analogy between the perceptron and the cerebellar Purkinje cell, we fitted the optimal weight distribution to that measured for granule cell-Purkinje cell synapses. The two distributions agreed well, suggesting that the Purkinje cell can learn up to 5 kilobytes of information, in the form of 40,000 input-output associations.
Brunel
Nicolas
N
Neurophysique et Physiologie, Université René Descartes, 45 rue des Saints Pères, 75270 Paris Cedex 06, France.
Hakim
Vincent
V
Isope
Philippe
P
Nadal
Jean-Pierre
JP
Barbour
Boris
B
D000344:Afferent Pathways / Q000166:cytology / Q000502:physiology*; D000818:Animals; D019706:Excitatory Postsynaptic Potentials / Q000502:physiology; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology; D016571:Neural Networks, Computer; D011689:Purkinje Cells / Q000166:cytology / Q000502:physiology*; D051381:Rats; D015203:Reproducibility of Results; D013569:Synapses / Q000502:physiology* / Q000648:ultrastructure; D009435:Synaptic Transmission / Q000502:physiology*