Connectionists: How the brain works

[Hans du Buf]

I was out for some time (mailbox overflooded) and now saw again discussions
about vision and motor control and single equations and and and...
Why don't you start with the archaic part of our brain? (NOT the frontal 
lobe
and rich club - the massive communication hubs; white matter - only these
distinguish us and great apes from rodents)
I've been reading a few recent reviews, and the idea comes up that the
enormous complexity is the astonishing result of merely replicating very
few structures over and over (single equation :-)
We know the laminar structure of the neocortex and the connections and
processing (FF input from a lower level, horizontal processing, FB input 
from
a higher level), the hierarchies V1 V2 etc and M1 M2 etc and A1 A2 etc.
Oops, FF = feedforward.
These hierarchies are reciprocally connected: V2 groups features from V1,
V4 groups features from V2, until IT cortex with population coding
of (parts of) meaningful objects, but in each step up with less localisation
(IT knows what the handle and shank of screwdriver are, and about where,
and that they belong together; but don't ask IT to put the tip into the slot
in the head of a screw - for that you need V1, but V1 has absolutely no clue
what a screwdriver is). FF+FB is likely predictive coding with a generative
grouping model. If you have V1 and V2, you also have V4 and IT. You can
also assume that the processing in the V and M and A hierarchies is the
same: one equation.
All neocortical areas are reciprocally connected to pulvinar (LGN in case of
vision) and higher-order thalamic areas in a laminar way, and then to basal
ganglia via layers for arms, face and legs. The BG take decisions, most 
important
keyword: DISinhibition. One equation.
All visual areas are still connected to motor areas (archaic brain, rodents,
screwdriver).
All motor areas are connected to sensory areas: corollary discharge signals
were first introduced because of saccadic eye movements, but they are
ubiquitous for distinguishing external from self-induced percepts, and at
all levels: from reflex inhibition, sensory filtration, stability 
analysis up to
sensorimotor learning and planning. I boldly assume: one equation.

Once you understand this, you could assume the same principles for other
cortices, like the anterior and posterior cingulate: ACC for arousal and
attention, error, conflict, reward, learning; PCC for more internal 
attention
and salience. The PCC is connected to thalamus and striatum (basal ganglia,
decisions!). ACC+PCC balance internal and external attention, both between
narrow and broad attention. Internal: daydreaming, freewheeling, autism.
Fronto-parietal network: short-term flexible allocation of selective 
attention.
Cingulo-opercular network: longer-term, maintain task-related goals.
They interact via cerebellar and thalamic nodes, work in parallel.
They are part of the default mode network: external goal-oriented action
vs. self-regulation. Homeostasis. Small imbalance between endogenous and
exogenous processes: ADHD.
Anterior insula and dorso-lateral PCC: balance between excessive control 
and
lack of control. Imbalance: obsessive-compulsive disorder or schizophrenia.

Keep in mind that our brain is always testing hypotheses and predicting 
errors,
always at the brink of failure. Metastability: shift through multiple, 
short-lived
yet stable states. You tweak a parameter and the brain freaks out. It is 
amazing
that (in my view) a very few principles could be applied to understand 
how our
brain works - and that most brains seem to work quite  well.

Finally (I need to get some work done), the brain is not a bunch of 
artificial neural
networks which are trained once. It constantly re-trains itself like a 
babbling baby,
although this is rarely noticed.
Am I too bold?
Hans




On 05/23/2014 03:27 AM, Janet Wiles wrote:
> Will a single equation be a good model of the brain as a whole? Unlikely!
> Will a set of equation-sized-chunks of knowledge suffice? It works for physics, but remains an empirical question for neuroscience.
>
> The point is not about what's the best way to model the brain, but rather, what models are adopted widely, while others remain the province of a single lab.  A model that can be expressed as a single equation seems to be a particularly effective meme for computational researchers.
>
> Janet
>
>    
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Prof.dr.ir. J.M.H. du Buf                          mailto:dubuf at ualg.pt
Dept. of Electronics and Computer Science - FCT,
University of Algarve,                            fax (+351) 289 818560
Campus de Gambelas, 8000 Faro, Portugal. tel (+351) 289 800900 ext 7761
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UALG Vision Laboratory:            http://w3.ualg.pt/~dubuf/vision.html
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