 
Summary: J Comput Neurosci manuscript No.
(will be inserted by the editor)
Capacity Analysis in Multistate 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 finitestate 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 nonselective 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 metaplasticity 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
