20100420

disorganized notes

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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