Capacity analysis in multi-state synaptic models: a retrieval probability perspective.
We define the memory capacity of networks of binary neurons with finite-state synapses in terms of retrieval probabilities of learned patterns under standard asynchronous dynamics with a predetermined threshold. The threshold is set to control the proportion of non-selective neurons that fire. An optimal inhibition level is chosen to stabilize network behavior. For any local learning rule we provide a computationally efficient and highly accurate approximation to the retrieval probability of a pattern as a function of its age. The method is applied to the sequential models (Fusi and Abbott, Nat Neurosci 10:485-493, 2007) and meta-plasticity models (Fusi et al., Neuron 45(4):599-611, 2005; Leibold and Kempter, Cereb Cortex 18:67-77, 2008). We show that as the number of synaptic states increases, the capacity, as defined here, either plateaus or decreases. In the few cases where multi-state models exceed the capacity of binary synapse models the improvement is small.
Department of Statistics, University of Chicago, 5734 S University Ave, Chicago, IL 60637, USA. yibih@uchicago.edu
Yibi
Y
Huang
Yali
Y
Amit
D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001921:Brain / Q000502:physiology; D006801:Humans; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*