Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic learning in a neural network with adapting synapses   We consider a neural network with adapting synapses whose dynamics can be
analitically computed. The model is made of $N$ neurons and each of them is
connected to $K$ input neurons chosen at random in the network. The synapses
are $n$-states variables which evolve in time according to Stochastic Learning
rules; a parallel stochastic dynamics is assumed for neurons. Since the network
maintains the same dynamics whether it is engaged in computation or in learning
new memories, a very low probability of synaptic transitions is assumed. In the
limit $N\to\infty$ with $K$ large and finite, the correlations of neurons and
synapses can be neglected and the dynamics can be analitically calculated by
flow equations for the macroscopic parameters of the system.

5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
1
The categories of this paper are: cs
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg