poggio and seung : connections between hypercolumns

lateral connectivity and receptive fields in V1, automatic organization

mesoscale cortical organization

systems level organization of cortext and how this constrains what information "space" is likely to be represented in a given region

understanding the coding "space" of specific brain areas

brodmann areas : different, and different for a reason ?

understanding the communication between different patches of cortex

understand the information transformations that a given patch of cortex can effect

understanding what the variations in cortical archetecture indicate about computational capabilities

define the cortical surface automorphism ( an N vector on a 2D manifold embedded in 3space )

invert neural code to generate stimuli that can reproduce give neural state

make a better model of hallucinations and the flicker geometric hallucinations

dynamical systems model of basal ganglia / motor control system

idea :

patch

computational entity

computational function

not linear per-se, some sort of information mapping

maybe a "rotation" like function that respects the local 2D organization

in addition to some computing nonlinearities

each patch of cortex represents some psychological space

which is strongly related to classical notions of space

that is, co-ordinate information relative to various degrees of freedom of the body and the environment

so, for all behaviorally relevant spaces, you have an embedding of that space somewhere in cortex

connected to other regions that provide information about that space

and require information about that space

and perhaps you align these representations based on prior experience to fill in missing data

its like, I want to perform a particular operation like convolution

but instread of brute forcing

I take the FFT

use a simpler transformation

then invert the FFT again to get the convolved result

there is some computation that cortex is good at doing

and mappings between regions transform spaces into representations that are natural for this computation

mar's theory of cortex :

(un?)supervisd learning of statistical structure of its inputs.

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