Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Constrained Parameter Inference as a Principle for Learning   Learning in biological and artificial neural networks is often framed as a
problem in which targeted error signals guide parameter updating for more
optimal network behaviour. Backpropagation of error (BP) is an example of such
an approach and has proven to be a highly successful application of stochastic
gradient descent to deep neural networks. However, BP relies on the global
transmission of gradient information and has therefore been criticised for its
biological implausibility. We propose constrained parameter inference (COPI) as
a new principle for learning. COPI allows for the estimation of network
parameters under the constraints of decorrelated neural inputs and top-down
perturbations of neural states. We show that COPI is not only more biologically
plausible but also provides distinct advantages for fast learning, compared
with the backpropagation algorithm.

15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph