Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fitting of dynamic recurrent neural network models to sensory
  stimulus-response data   We present a theoretical study aiming at model fitting for sensory neurons.
Conventional neural network training approaches are not applicable to this
problem due to lack of continuous data. Although the stimulus can be considered
as a smooth time dependent variable, the associated response will be a set of
neural spike timings (roughly the instants of successive action potential
peaks) which have no amplitude information. A recurrent neural network model
can be fitted to such a stimulus-response data pair by using maximum likelihood
estimation method where the likelihood function is derived from Poisson
statistics of neural spiking. The universal approximation feature of the
recurrent dynamical neuron network models allow us to describe
excitatory-inhibitory characteristics of an actual sensory neural network with
any desired number of neurons. The stimulus data is generated by a Phased
Cosine Fourier series having fixed amplitude and frequency but a randomly shot
phase. Various values of amplitude, stimulus component size and sample size are
applied in order to examine the effect of stimulus to the identification
process. Results are presented in tabular form at the end of this text.

31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph