Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predictive Coding for Dynamic Vision : Development of Functional
  Hierarchy in a Multiple Spatio-Temporal Scales RNN Model   The current paper presents a novel recurrent neural network model, the
predictive multiple spatio-temporal scales RNN (P-MSTRNN), which can generate
as well as recognize dynamic visual patterns in the predictive coding
framework. The model is characterized by multiple spatio-temporal scales
imposed on neural unit dynamics through which an adequate spatio-temporal
hierarchy develops via learning from exemplars. The model was evaluated by
conducting an experiment of learning a set of whole body human movement
patterns which was generated by following a hierarchically defined movement
syntax. The analysis of the trained model clarifies what types of
spatio-temporal hierarchy develop in dynamic neural activity as well as how
robust generation and recognition of movement patterns can be achieved by using
the error minimization principle.

4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas