Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Input correlations impede suppression of chaos and learning in balanced
  rate networks   Neural circuits exhibit complex activity patterns, both spontaneously and
evoked by external stimuli. Information encoding and learning in neural
circuits depend on how well time-varying stimuli can control spontaneous
network activity. We show that in firing-rate networks in the balanced state,
external control of recurrent dynamics, i.e., the suppression of
internally-generated chaotic variability, strongly depends on correlations in
the input. A unique feature of balanced networks is that, because common
external input is dynamically canceled by recurrent feedback, it is far easier
to suppress chaos with independent inputs into each neuron than through common
input. To study this phenomenon we develop a non-stationary dynamic mean-field
theory that determines how the activity statistics and largest Lyapunov
exponent depend on frequency and amplitude of the input, recurrent coupling
strength, and network size, for both common and independent input. We also show
that uncorrelated inputs facilitate learning in balanced networks.

26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc