Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficiency of local learning rules in threshold-linear associative
  networks   We derive the Gardner storage capacity for associative networks of threshold
linear units, and show that with Hebbian learning they can operate closer to
such Gardner bound than binary networks, and even surpass it. This is largely
achieved through a sparsification of the retrieved patterns, which we analyze
for theoretical and empirical distributions of activity. As reaching the
optimal capacity via non-local learning rules like backpropagation requires
slow and neurally implausible training procedures, our results indicate that
one-shot self-organized Hebbian learning can be just as efficient.

5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg