<div dir="ltr">Is there anything that can't be represented as a single equation or a really long run on sentence aka model?<div><br></div><div>With regards to whether new models such as *poesis are accepted by the field, I think this really boils down to identity politics. Most researchers are doing a mix of wanting to understand how their brain works and wanting to help humanity by grokking the brain to solve problems like epilepsy etc. The part of them that just wants to understand how their brain works will obviously tend to prefer their own personal rotation of the space. The part that wants to help humanity sees the need to integrate new theories, but this conflicts with the ego and these new theories are more likely to be changed beyond recognition to fit into a given researchers existing framework than to be supported and encouraged.</div>
<div><br></div><div>Every once in a while a researcher will stumble upon a description so short and so elegant that it easily transcends the usefulness of all existing theories. However, the brain isn't like previous objects of study. It's essentially the most sophisticated thing that science has ever turned its gaze on. Whether it lends itself to simple "single equation" descriptions is an open question, but I personally doubt it. All models are wrong, and this applies to every model of the brain. Some models are useful, and this also applies to every model of the brain. For the purposes of understanding how your own brain works, an arbitrary rotation of a sophisticated theory seems quite sufficient. For solving actual problems, like epilepsy, some models will be more useful than others. That said, a model isn't always even needed to solve a problem: the latest epilepsy drugs are the most effective and they are found by shotgun approaches which result in drugs that work and for reasons that nobody understands.</div>
<div><br></div><div>Zooming out, I like to ask myself whether there's a reason things are the way they are. This is obviously an unanswerable question, but it does shine a light on the fact that we have egos, and that this process of ego-scaffolding leads to many researchers focusing on different perspectives at different levels of analysis. The academic publishing system then broadcasts these perspectives, and whether or not we give truly fair credit assignment versus implicitly mashing their theory into our own preferred framework, everything does in the end get all mixed up together, resulting in a better set of theories overall. One wonders if this apparently fortuitous coincidence isn't a coincidence after all. I personally suspect the usefulness of what we're doing is that we are all contributing to the building of something great. While this somewhat justifies the existing system, given that it actually appears to be working (in a way that none of us understands), I would also advocate a more egalitarian approach where we open our minds to as many theories as possible and cheer on perspectives different from our own. And from this angle, I really like the promise of more informal mailing list conversations for the spread of ideas and hope that you guys keep it up, because I love reading your ideas. And I wish more folks would jump in too and help us all out, rather than just hiding out in the darkness of the literature!</div>
<div><br></div><div>Brian</div><div><br></div><div><a href="https://www.linkedin.com/profile/view?id=74878589">https://www.linkedin.com/profile/view?id=74878589</a><br></div></div><div class="gmail_extra"><br><br><div class="gmail_quote">
On Fri, May 23, 2014 at 8:43 AM, Hans du Buf <span dir="ltr"><<a href="mailto:dubuf@ualg.pt" target="_blank">dubuf@ualg.pt</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
I was out for some time (mailbox overflooded) and now saw again discussions<br>
about vision and motor control and single equations and and and...<br>
Why don't you start with the archaic part of our brain? (NOT the frontal lobe<br>
and rich club - the massive communication hubs; white matter - only these<br>
distinguish us and great apes from rodents)<br>
I've been reading a few recent reviews, and the idea comes up that the<br>
enormous complexity is the astonishing result of merely replicating very<br>
few structures over and over (single equation :-)<br>
We know the laminar structure of the neocortex and the connections and<br>
processing (FF input from a lower level, horizontal processing, FB input from<br>
a higher level), the hierarchies V1 V2 etc and M1 M2 etc and A1 A2 etc.<br>
Oops, FF = feedforward.<br>
These hierarchies are reciprocally connected: V2 groups features from V1,<br>
V4 groups features from V2, until IT cortex with population coding<br>
of (parts of) meaningful objects, but in each step up with less localisation<br>
(IT knows what the handle and shank of screwdriver are, and about where,<br>
and that they belong together; but don't ask IT to put the tip into the slot<br>
in the head of a screw - for that you need V1, but V1 has absolutely no clue<br>
what a screwdriver is). FF+FB is likely predictive coding with a generative<br>
grouping model. If you have V1 and V2, you also have V4 and IT. You can<br>
also assume that the processing in the V and M and A hierarchies is the<br>
same: one equation.<br>
All neocortical areas are reciprocally connected to pulvinar (LGN in case of<br>
vision) and higher-order thalamic areas in a laminar way, and then to basal<br>
ganglia via layers for arms, face and legs. The BG take decisions, most important<br>
keyword: DISinhibition. One equation.<br>
All visual areas are still connected to motor areas (archaic brain, rodents,<br>
screwdriver).<br>
All motor areas are connected to sensory areas: corollary discharge signals<br>
were first introduced because of saccadic eye movements, but they are<br>
ubiquitous for distinguishing external from self-induced percepts, and at<br>
all levels: from reflex inhibition, sensory filtration, stability analysis up to<br>
sensorimotor learning and planning. I boldly assume: one equation.<br>
<br>
Once you understand this, you could assume the same principles for other<br>
cortices, like the anterior and posterior cingulate: ACC for arousal and<br>
attention, error, conflict, reward, learning; PCC for more internal attention<br>
and salience. The PCC is connected to thalamus and striatum (basal ganglia,<br>
decisions!). ACC+PCC balance internal and external attention, both between<br>
narrow and broad attention. Internal: daydreaming, freewheeling, autism.<br>
Fronto-parietal network: short-term flexible allocation of selective attention.<br>
Cingulo-opercular network: longer-term, maintain task-related goals.<br>
They interact via cerebellar and thalamic nodes, work in parallel.<br>
They are part of the default mode network: external goal-oriented action<br>
vs. self-regulation. Homeostasis. Small imbalance between endogenous and<br>
exogenous processes: ADHD.<br>
Anterior insula and dorso-lateral PCC: balance between excessive control and<br>
lack of control. Imbalance: obsessive-compulsive disorder or schizophrenia.<br>
<br>
Keep in mind that our brain is always testing hypotheses and predicting errors,<br>
always at the brink of failure. Metastability: shift through multiple, short-lived<br>
yet stable states. You tweak a parameter and the brain freaks out. It is amazing<br>
that (in my view) a very few principles could be applied to understand how our<br>
brain works - and that most brains seem to work quite well.<br>
<br>
Finally (I need to get some work done), the brain is not a bunch of artificial neural<br>
networks which are trained once. It constantly re-trains itself like a babbling baby,<br>
although this is rarely noticed.<br>
Am I too bold?<br>
Hans<div class=""><br>
<br>
<br>
<br>
<br>
On 05/23/2014 03:27 AM, Janet Wiles wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Will a single equation be a good model of the brain as a whole? Unlikely!<br>
Will a set of equation-sized-chunks of knowledge suffice? It works for physics, but remains an empirical question for neuroscience.<br>
<br>
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.<br>
<br>
Janet<br>
<br>
<br>
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<a href="http://Prof.dr.ir" target="_blank">Prof.dr.ir</a>. J.M.H. du Buf mailto:<a href="mailto:dubuf@ualg.pt" target="_blank">dubuf@ualg.pt</a><br>
Dept. of Electronics and Computer Science - FCT,<br>
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