# Show Reference: "A theory of cortical responses"

A theory of cortical responses Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, Vol. 360, No. 1456. (29 April 2005), pp. 815-836, doi:10.1098/rstb.2005.1622 by Karl Friston
@article{friston-2005,
address = {The Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK. k.friston@fil.ion.ucl.ac.uk},
author = {Friston, Karl},
day = {29},
doi = {10.1098/rstb.2005.1622},
issn = {0962-8436},
journal = {Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences},
keywords = {neural-coding},
month = apr,
number = {1456},
pages = {815--836},
pmcid = {PMC1569488},
pmid = {15937014},
posted-at = {2013-02-26 09:17:13},
priority = {2},
title = {A theory of cortical responses},
url = {http://dx.doi.org/10.1098/rstb.2005.1622},
volume = {360},
year = {2005}
}


Sensation refers to the change of state of the nervous system induced purely by a stimulus. Perception integrates sensation with experience and training.

According to Friston, percepts are the products of recognizing the causes of sensory input and sensation'.

Predictive coding can implement the EM algorithm.

Empirical Bayes methods estimate the prior from the data.

More formally, they choose some parametric form for the prior, and estimate an optimal set of parameters $\theta_{opt}$ by optimizaton: $$\theta_{opt} = \mathrm{arg\;max}_\theta\prod_n\int P_\theta(x)P(m_n\mid x)\;dx,$$ for measurements $m_n$ and possible latent variable values $x$.

In predictive coding, a model iterates the following steps:

• assume values for latent variables,
• predict sensory input (through a generative model),
• observe prediction error,
• adapt assumptions to minimize the error.

The EM algorithm is an iterative algorithm that solves a simplified version of Empirical Bayes.

Friston's predictive coding model predicts a hierarchical cortical system.

Functional segregation and integration are complementary principles of organization of the brain.

Backward connections in the visual cortex show less topographical organization (show abundant axonal bifurcation'), are more abundant than forward connections.

The visual cortex is hierarchically organized.

Feedforward connections in the visual cortex seem to be driving while feedback connections seem to be modulatory.

In order to recognize ie. to identify the causes underlying a sensation (according to Friston), one has to mentally undo the transformation from causes to sensations.

These transformations may not be invertible—for example if different causes interact in non-linear ways.

Given a generative model, it can be possible to find the most likely cause (or causes) of a sensation even if the causes interact in complex ways.

Some authors see the lower stages of visual processing as implementing an inverse model of optics—a model deriving causes from sensations and higher stages as implementing a forward model—a model generating expected sensations from assumed causes.

In Friston's architecture, competitive learning serves to de-correlate error units.

Friston states that models that do not show conditional independence (e.g. those used by connectionist and infomax schemes) depend on prior constraints for unique inference and do not invoke a hierarchical cortical organization;'

What does models that do not show conditional independence' mean? Does it include SOMs?

If what Friston means by `models that do not show conditional independence' includes SOM, then that would explain why I can't find an error signal. Maybe the prior constraint invoked by SOMs is similarity between stimuli?

Possibly, this is a point for future work: model cortico-collicular connections as prediction. But, in Friston's framework, there would have to be ascending connections, too.

Grossberg's ART and Friston's theory of cortical responses appeal to the anatomical interpretation of 'top-down' and 'bottom-up' processing and stress feedback as well as feedforward connections.