Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unifying and generalizing models of neural dynamics during
  decision-making   An open question in systems and computational neuroscience is how neural
circuits accumulate evidence towards a decision. Fitting models of
decision-making theory to neural activity helps answer this question, but
current approaches limit the number of these models that we can fit to neural
data. Here we propose a unifying framework for modeling neural activity during
decision-making tasks. The framework includes the canonical drift-diffusion
model and enables extensions such as multi-dimensional accumulators, variable
and collapsing boundaries, and discrete jumps. Our framework is based on
constraining the parameters of recurrent state-space models, for which we
introduce a scalable variational Laplace-EM inference algorithm. We applied the
modeling approach to spiking responses recorded from monkey parietal cortex
during two decision-making tasks. We found that a two-dimensional accumulator
better captured the trial-averaged responses of a set of parietal neurons than
a single accumulator model. Next, we identified a variable lower boundary in
the responses of an LIP neuron during a random dot motion task.

18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con