Does the Brain Infer Invariance Transformations from Graph Symmetries?

11/11/2021
by   Helmut Linde, et al.
0

The invariance of natural objects under perceptual changes is possibly encoded in the brain by symmetries in the graph of synaptic connections. The graph can be established via unsupervised learning in a biologically plausible process across different perceptual modalities. This hypothetical encoding scheme is supported by the correlation structure of naturalistic audio and image data and it predicts a neural connectivity architecture which is consistent with many empirical observations about primary sensory cortex.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

10/23/2020

A biologically plausible neural network for Slow Feature Analysis

Learning latent features from time series data is an important problem i...
07/10/2020

Biological credit assignment through dynamic inversion of feedforward networks

Learning depends on changes in synaptic connections deep inside the brai...
12/08/2017

Transformational Sparse Coding

A fundamental problem faced by object recognition systems is that object...
05/24/2019

Inference of Dynamic Graph Changes for Functional Connectome

Dynamic functional connectivity is an effective measure to characterize ...
10/23/2012

A Self-Organized Neural Comparator

Learning algorithms need generally the possibility to compare several st...
03/22/2019

Axonal Conduction Velocity Impacts Neuronal Network Oscillations

Increasing experimental evidence suggests that axonal action potential c...
07/08/2018

Detecting Synapse Location and Connectivity by Signed Proximity Estimation and Pruning with Deep Nets

Synaptic connectivity detection is a critical task for neural reconstruc...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.