Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network

  Advanced Search  

J Comput Neurosci manuscript No. (will be inserted by the editor)

Summary: J Comput Neurosci manuscript No.
(will be inserted by the editor)
Capacity Analysis in Multi-state Synaptic Models: a Retrieval
Probability Perspective.
Yibi Huang Yali Amit
Received: date / Accepted: date
Abstract We define the memory capacity of networks of bi-
nary 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 net-
work 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, 2007) and meta-plasticity models (Fusi et al,
2005; Leibold and Kempter, 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


Source: Amit, Yali - Departments of Computer Science & Statistics, University of Chicago


Collections: Computer Technologies and Information Sciences