Paper Info
Title
Predictive Coding, Variational Autoencoders, and Biological Connections
Abstract
We present a review of predictive coding, from theoretical neuroscience, and variational autoencoders, from machine learning, identifying the common origin and mathematical framework underlying both areas. As each area is prominent within its respective field, more firmly connecting these areas could prove useful in the dialogue between neuroscience and machine learning. After reviewing each area, we discuss two possible correspondences implied by this perspective: cortical pyramidal dendrites as analogous to (nonlinear) deep networks and lateral inhibition as analogous to normalizing flows. These connections may provide new directions for further investigations in each field.
Year
DOI
Venue
2022
10.1162/neco_a_01458
Neural Computation
DocType
Volume
Issue
Journal
34
1
ISSN
Citations 
PageRank 
0899-7667
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Joseph Marino17011.35