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[0] Isoda M, Hikosaka O, Switching from automatic to controlled action by monkey medial frontal cortex.Nat Neurosci 10:2, 240-8 (2007 Feb)

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ref: -0 tags: ocaml application functional programming date: 10-11-2022 21:36 gmt revision:2 [1] [0] [head]


From this I learned that in ocaml you can return not just functions (e.g. currying) but appliations of yet-to-be named functions.

let sum f = f 0 ;;
let arg a b c = c ( b + a ) ;;
let z a = a ;;


sum (arg 1) ;; 

is well-typed as (int -> `a) -> `a = <fun> e.g. an application of a function that converts int to `a. Think of it as the application of Xa to argument ( 0 + 1 ), where Xa is the argument (per type signature). Zero is supplied by the definition of 'sum'.

 sum (arg 1) (arg 2);; 

can be parsed as

(sum (arg 1)) (arg 2) ;; 

'(arg 2)' outputs an application of an int & a yet-to be determined function to 'a,

E.g. it's typed as int -> (int -> `a) -> `a = <fun>. So, you can call it Xa passed to above.

Or, Xa = Xb( ( 0 + 1 ) + 2)

where, again, Xb is a yet-to-be defined function that is supplied as an argument.

Therefore, you can collapse the whole chain with the identity function z. But, of course, it could be anything else -- square root perhaps for MSE?

All very clever.

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ref: -2019 tags: Piantadosi cogntion combinators function logic date: 09-05-2022 01:57 gmt revision:0 [head]

  • The Computational Origin of Representation (2019)
  • from Piantandosi, talks a big game... reviews some seminal literature ...
    • But the argument reduces to the established idea that you can represent boolean logic and arbitrary algorithms with Church encoding through S and K (and some tortuous symbol manipulation..)
    • It seems the Piantadosi was perhaps excited by discovering and understanding combinators?
      • It is indeed super neat (and i didn't wade so deep to really understand it), but the backtracking search procedure embodied in pyChuriso is scarcely close to anything happening in our brains (and such backtracking search is common in CS..)
      • It is overwhelmingly more likely that we approximate other Turning-complete computations, by (evolutionary) luck and education.
      • The last parts of the paper, describing a continuum between combinators, logic, calculus, tensor approximations, and neuroscience is ... very hand-wavey, with no implementation.
        • If you allow me to be hyypercritical, this paper is an excellent literature review, but limited impact for ML practitioners.

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ref: -0 tags: synaptic plasticity LTP LTD synapses NMDA glutamate uncaging date: 08-11-2020 22:40 gmt revision:0 [head]

PMID-31780899 Single Synapse LTP: A matter of context?

  • Not a great name for a thorough and reasonably well-written review of glutamate uncaging studies as related to LTP (and to a lesser extent LTD).
  • Lots of refernces from many familiar names. Nice to have them all in one place!
  • I'm left wondering, between CaMKII, PKA, PKC, Ras, other GTP dependent molecules -- how much of the regulatory network in synapse is known? E.g. if you pull down all proteins in the synaptosome & their interacting partners, how many are unknown, or have an unknown function? I know something like this has been done for flies, but in mammals - ?

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ref: -2015 tags: conjugate light electron tomography mouse visual cortex fluorescent label UNC cryoembedding date: 03-11-2019 19:37 gmt revision:1 [0] [head]

PMID-25855189 Mapping Synapses by Conjugate Light-Electron Array Tomography

  • Use aligned interleaved immunofluorescence imaging follwed by array EM (FESEM). 70nm thick sections.
  • Of IHC, tissue must be dehydrated & embedded in a resin.
  • However, the dehydration disrupts cell membranes and ultrastructural details viewed via EM ...
  • Hence, EM microscopy uses osmium tetroxide to cross-link the lipids.
  • ... Yet that also disrupt / refolds the poteins, making IHC fail.
  • Solution is to dehydrate & embed at cryo temp, -70C, where the lipids do not dissolve. They used Lowicryl HM-20.
  • We show that cryoembedding provides markedly improved ultrastructure while still permitting multiplexed immunohistochemistry.

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ref: -0 tags: automatic programming inductive functional igor date: 07-29-2014 02:07 gmt revision:0 [head]

Inductive Rule Learning on the Knowledge Level.

  • 2011.
  • v2 of their IGOR inductive-synthesis program.
  • Quote: The general idea of learning domain specific problem solving strategies is that first some small sample problems are solved by means of some planning or problem solving algorithm and that then a set of generalized rules are learned from this sample experience. This set of rules represents the competence to solve arbitrary problems in this domain.
  • My take is that, rather than using heuristic search to discover programs by testing specifications, they use memories of the output to select programs directly (?)
    • This is allegedly a compromise between the generate-and-test and analytic strategies.
  • Description is couched in CS-lingo which I am inexperienced in, and is perhaps too high-level, a sin I too am at times guilty of.
  • It seems like a good idea, though the examples are rather unimpressive as compared to MagicHaskeller.

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ref: -0 tags: putamen functional organization basal ganglia date: 02-24-2012 21:01 gmt revision:0 [head]

PMID-6705861 Single cell studies of the primate putamen. I. Functional organization.

  • Cells in the striatum have very low levels of activity -- some are simply not spontaneously active.
  • Other cells are tonically active at 3-6Hz (cholinergic?)
  • ( Most cells related to the direction of movement, not necessarily force.
  • Two types of load reactions: short latency (presumably sensory) and long-latency (motor -- related to the active return movement of the arm.)
  • Timing suggests that the striatum does not play a role in the earliest phases of movement, consistent with cooling studies, kainic acid lesions, or microstimulation. Only 19% of neurons were active before movement.
  • Many neurons were reactive to both active and passive movements in the same joint / direction.
    • The BG receive afferents from joint and not muscle receptors.

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ref: Vitek-2008.03 tags: DBS function efferent STN date: 02-22-2012 18:39 gmt revision:2 [1] [0] [head]

PMID-18540149[0] Deep brain stimulation: how does it work?

  • MPTP monkey research suggests that activation of output and the resultant change in pattern of neuronal activity that permeates throughout the basal ganglia motor circuit is the mechanism responsible for symptom improvement.
    • Sensible network approach.
  • If pathological plasticity mechanisms are responsible for the symptoms, perhaps we should look for similarly slow treatments?


[0] Vitek JL, Deep brain stimulation: how does it work?Cleve Clin J Med 75 Suppl 2no Issue S59-65 (2008 Mar)

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ref: -0 tags: reinforcement learning basis function policy specialization date: 01-03-2012 02:37 gmt revision:1 [0] [head]

To read:

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ref: Douglas-1991.01 tags: functional microcircuit cat visual cortex microstimulation date: 12-29-2011 05:12 gmt revision:3 [2] [1] [0] [head]

PMID-1666655[0] A functional microcircuit for cat visual cortex

  • Using in vivo stim and record, They describe what may be a 'cannonical' circuit for the cortex.
  • Not dominated by excitation / inhibition, but rather cell dynamics.
  • Thalamus weaker than poysynaptic inupt from the cortex for excitation.
  • Focuses on Hubel and Wiesel style stuffs. Cats, SUA.
  • Stimulated the geniculate body & observed the response using intracellular electrodes from 102 neurons.
  • Their traces show lots of long-duration inhibition.
  • Probably not relevant to my purposes.


[0] Douglas RJ, Martin KA, A functional microcircuit for cat visual cortex.J Physiol 440no Issue 735-69 (1991)

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ref: work-0 tags: no free lunch wolpert coevolution date: 07-19-2010 12:54 gmt revision:2 [1] [0] [head]


  • Just discovered this. It makes perfect sense - bias free learning is 'futile'. Learning need be characterized by its biases, which enable faster or better results in particular problem domains.
  • Equivalently: any two algorithms are equivalent when their performance is averaged across all possible problems. (This is not as strong as it sounds, as most problems will never be encountered).
  • Wolper 1996 provides an excellent geometric interpretation of this: the quality of the search/optimization algorithm within a particular domain iis proporational to the inner product of its expected search stream with the actual (expected?) probability distribution of the data.
  • However! with coevolutionary algorithms, there can be a free lunch - "in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems." Wolpert 2005
    • claims that this does not (??) hold in biological evolution, where there is no champion. Yet biology seems all about co-evolution.
    • coevolution of a backgammon player details how it may be coevolution + the structure of the backgammon game, not reinforcement learning, which led Tesauro to his championship-level player. Specifically, coevolutionary algorithms tend to get stuck in local minima - where both contestants play mediocre and draw - but this is not possible in backgammon; there is only one winner, and the games must terminate eventually.
      • These authors introduce a very interesting twist to improve coevolutionary bootstrapping: Firstly, the games are played in pairs, with the order of play reversed and the same random seed used to generate the dice rolls for both games. This washes out some of the unfairness due to the dice rolls when the two networks are very close - in particular, if they were identical, the result would always be one win each.

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ref: work-0 tags: differential evolution function optimization date: 07-09-2010 14:46 gmt revision:3 [2] [1] [0] [head]

Differential evolution (DE) is an optimization method, somewhat like Neidler-Mead or simulated annealing (SA). Much like genetic algorithms, it utilizes a population of solutions and selection to explore and optimize the objective function. However, it instead of perturbing vectors randomly or greedily descending the objective function gradient, it uses the difference between individual population vectors to update hypothetical solutions. See below for an illustration.

At my rather cursory reading, this serves to adapt the distribution of hypothetical solutions (or population of solutions, to use the evolutionary term) to the structure of the underlying function to be optimized. Judging from images/821_1.pdf Price and Storn (the inventors), DE works in situations where simulated annealing (which I am using presently, in the robot vision system) fails, and is applicable to higher-dimensional problems than simplex methods or SA. The paper tests DE on 100 dimensional problems, and it is able to solve these with on the order of 50k function evaluations. Furthermore, they show that it finds function extrema quicker than stochastic differential equations (SDE, alas from 85) which uses the gradient of the function to be optimized.

I'm surprised that this method slipped under my radar for so long - why hasn't anyone mentioned this? Is it because it has no proofs of convergence? has it more recently been superseded? (the paper is from 1997). Yet, I'm pleased because it means that there are also many other algorithms equally clever and novel (and simple?), out their in the literature or waiting to be discovered.

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ref: Inzlicht-2009.03 tags: uncertainty religion conviction decision science date: 02-02-2010 20:39 gmt revision:3 [2] [1] [0] [head]

The Neural Markers of Religious Conviction PMID-19291205

Recently a friend pointed this article out to me, and while I found the scientific results interesting though slightly questionable - that religious people have less anterior cingulate cortex activation upon error - the introduction and discussion were stimulating. What follows are a few quotes and my interpretation and implications of the authors' viewpoint.

"The absence of a cognitive map providing clear standards and goals is uncomfortable and leads people to search for and assert belief systems that quell their anxiety by allowing for clearer goal pursuit (McGregor, Zanna, Holmes, & Spencer, 2001)." I would argue that uncertainty itself is highly uncomfortable - whether it is uncertainty as to how much food you will have in the future, or uncertainty as to the best behavior. In this sense, of course religion decreases anxiety - it provides a structured way to think about this disordered and highly undecidable world, a filter to remove or explain away many of the random parts of our lives. In my personal experience, conviction is usually easier than trying to hold accurate probabalistic models in your mind - conviction is pleasurable, even if it is wrong.

I find their short review of cognitive science in the introduction interesting - they claim that the septo-hippocampal system is concerned with the detection and correction of errors associated with concrete behaviors and goals, while in humans (and other primates?) the ACC allows error and feedback based operations on concepts and higher-order goals. The need for a higher-level error detection circuit makes sense in humans, as we are able to bootstrap our behavior to very complicated limits, but it also begs to question - what trains the ACC? To some degree, it must train itself in the via the typical loopy feedback-based brain way, but this only goes so far, as (at least in the modern world) the space of all possible behaviors, longterm and short term, given stochastic feedback is too large to be either decidable or fully parseable/generalizable into an accurate global model, even given a lifetime of experience. Religion, as this paper and many others posits, provides this global model against which behaviors and perceptions can be measured.

But why does a uncertainty challenge causes a compensatory increase in the strength of convictions, almost to the point of zealousness (how is this adaptive? just as a means of reducing anxiety?); I've seen it happen, but why. From a Bayesian point of view, increased uncertainty necessitates decreased certainty, or fewer convictions. From a pragmatic point of view, increased uncertainty requires increased convictions purely because the convictions have to make up for the lack of environmental information from which to make a decision. Any theory must include the cost of not making a decision, the cost of delaying a decision, and the principle of sunk costs.

There are other solutions to the 'undecidable' problems of life than religion - literary culture and science come to mind. The principle behind all may be that, while individual experience and intellect is possibly insufficient for generating global rules to guide behavior, the condensed experience of thousands/millions/billions of people is. This assumes that experience, as a random variable/signal, scales according to the laws of large numbers - noise decreases monotonically as sample size increases. This may not actually be true, it depends on the structure of the distributions, and the extent to which people's decisions/behaviors are orthogonal, and the fidelity of the communication / aggregation channels which operate on the data. I think the dimensionality increase afforded by larger sample size is slower than the concomitant noise decrease, hence (valid) global rules guiding behavior can be extracted from large populations of people. Regarding the communication channels, it seems there were always high fidelity channels of experience - e.g. Homer, Benjamin Franklin's transatlatic trips, the royal Society of London, (forgive my western pov) - and now, there are even more (the internet)! The latter invention should, at least within the framework here, allow larger groups of people to make 'harder' or 'more undecidable' decisions by virtue of greater information. Fairly standard rhetoric to the internet crowd (c.f. forums), I know.

I would argue that this is better than using convictions... but the result of communication / aggregation is convictions anyway, so eh. Getting back to the uncertainty issue, the authors point out that conservative cultures there is usually greater uncertainty (which way is the arrow of causality?), and increasing uncertainty bolsters support zealous action, e.g. war.

"For example, contemporary social psychological research indicates that uncertainty threats can cause people to become more extreme in their opinions, so that they exaggerate their religious convictions and become more willing to support a war to defend those convictions (McGregor, Haji, Nash, & Teper, 2008). In fact, even nonbelievers bolster their personal convictions to near-religious levels in order to reduce uncertainty-related distress (McGregor et al., 2001). Thus, in terms of feedback-loop models, the standards and predictions provided by religious convictions are strong enough that they can resist any discrepant feedback that might alert the comparator system."

This, I believe, is fairly accurate, and it implies several dramatic things: if a despot or leader wishes to engender support for a war, particularly a religious war, then he should make the lives of his constituents uncertain. If their lives are stable and certain sans ideology, then they will be less likely to have the convictions ('the other side is bad!') to fight certain wars. (It of course depends on who/what the other side is!). Take Europe vs. America as an example - America has far fewer social support systems and greater uncertainty in life than in Europe. The Economist frequently phrases American businesses' penchant for hiring and firing people quickly and seemingly at whim, as it encourages creative reuse, economic flexibility, and better allocation of capital, but it has a clear downside - increased anxiety, uncertainty. We (well, not me, but many Americans) deal with this via religion, the article would argue (that said, I should guess that there are a great many other reasons people are religious). Still, in western Europe has less uncertainty in life, is more secular, and less tolerant of ideological wars. Hence the antidote for war is to give people stable, significant lives. More common-sense rhetoric.

On to another suggestive point made by the article: "In terms of feedback-loop models, this explanation suggests that the standards and predictions provided by religion are inadequate and should, in fact, result in prediction errors; however, because religious beliefs are rigid, inconsistent information is reinterpreted in such a way that it becomes assimilated to preexisting convictions, further sustaining beliefs (Park, 2005)."

I would be interested in an actual test of this hypothesis - if it is possible without bias (perhaps another EEG study? perhaps it has been already done?) The authors actually prove the opposite point, that religions people are more likely to answer correctly on the Stroop test. They take more time, but seem to be more careful. This reminds me of Matteo Ricci, who allegedly used his Jesuit training in sustained concentration and memorization to master the Chinese language; clearly religion is far more than just a means of reducing perceived uncertainty about the world.

To loop the argument back on its tail - this is the 'meta' blog, afterall - one may question if the theory (looking at behavior in terms of the unpleasantness of uncertainty and the need for decidability) is a good way of looking at things, just as we questioned if religion is a good theory of the world. I think it generalizes; for example, Solaiman mentioned that the European children of the revolution of 1968 had parents who notably applied very little guidance to their lives; they were like the American hippies. These people grew up disliking their parents, and sought far more structure in their lives and in parenting their own children. One may imagine that they disliked the vast uncertainty their parents bluntly exposed them to, and paucity of guiding principles - something that the parents, after years of living in the world, probably had. Secondly, Solaiman recalled that all his favorite teachers were those that were strictest, strongest in their conviction, and most structured in their pedagogy. People seek to make decisions decidable whether through parents, teachers, religion, science or even art and literature.

To summarize, uncertainty engenders convictions by the pragmatic principle. Best thing we can do is to either reduce uncertainty or found those convictions on aggregate data(*)

(*) Google publication. The principle of data is our zeitgist, but history suggests that independent of what we think now it will not be the last.

comments? edit this, write below.

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ref: work-0 tags: functional programming compilation ocaml date: 08-24-2009 14:33 gmt revision:0 [head]

The implementation of functional programming languages - book!

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ref: work-0 tags: ocaml mysql programming functional date: 07-03-2009 19:16 gmt revision:2 [1] [0] [head]

Foe my work I store a lot of analyzed data in SQL databases. In one of these, I have stored the anatomical target that the data was recorded from - namely, STN or VIM thalamus. After updating the analysis programs, I needed to copy the anatomical target data over to the new SQL tables. Where perl may have been my previous go-to language for this task, I've had enuogh of its strange quiks, hence decided to try it in Ruby (worked, but was not so elegant, as I don't actually know Ruby!) and then Ocaml.

#use "topfind"
#require "mysql"

(* this function takes a query and a function that converts entries 
in a row to Ocaml tuples *)
let read_table db query rowfunc =
	let r = Mysql.exec db query in
	let col = Mysql.column r in
	let rec loop = function
		| None      -> []
		| Some x    -> rowfunc col x :: loop (Mysql.fetch r)
	loop (Mysql.fetch r)

let _ = 
	let db = Mysql.quick_connect ~host:"crispy" ~database:"turner" ~password:"" ~user:"" () in
	let nn = Mysql.not_null in
	(* this function builds a table of files (recording sessions) from a given target, then 
	uses the mysql UPDATE command to propagate to the new SQL database. *)
	let propagate targ = 
		let t = read_table db 
			("SELECT file, COUNT(file) FROM `xcor2` WHERE target='"^targ^"' GROUP BY file")
			(fun col row -> (
				nn Mysql.str2ml (col ~key:"file" ~row), 
				nn Mysql.int2ml (col ~key:"COUNT(file)" ~row) )
		List.iter (fun (fname,_) -> 
			let query = "UPDATE `xcor3` SET `target`='"^targ^
				"' WHERE STRCMP(`file`,'"^fname^"')=0" in
			print_endline query ;
			ignore( Mysql.exec db query )
		) t ;
	propagate "STN" ; 
	propagate "VIM" ; 
	propagate "CTX" ; 
	Mysql.disconnect db ;;

Interacting with MySQL is quite easy with Ocaml - though the type system adds a certain overhead, it's not too bad.

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ref: work-0 tags: covariance matrix adaptation learning evolution continuous function normal gaussian statistics date: 06-30-2009 15:07 gmt revision:0 [head]


  • Details a method of sampling + covariance matrix approximation to find the extrema of a continuous (but intractable) fitness function
  • HAs flavors of RLS / Kalman filtering. Indeed, i think that kalman filtering may be a more principled method for optimization?
  • Can be used in high-dimensional optimization problems like finding optimal weights for a neural network.
  • Optimum-seeking is provided by weighting the stochastic samples (generated ala a particle filter or unscented kalman filter) by their fitness.
  • Introductory material is quite good, actually...

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ref: Isoda-2007.02 tags: SMA saccade basal_forebrain executive function 2007 microstimulation SUA cortex sclin date: 10-03-2008 17:12 gmt revision:2 [1] [0] [head]

PMID-17237780[0] Switching from automatic to controlled action by monkey medial frontal cortex.

  • SCLIN's blog entry
  • task: two monkeys were trained to saccade to one of two targets, left/right pink/yellow. the choice was cued by the color of the central fixation target; when it changed, they should saccade to the same-colored target.
    • usually, the saccade direction remained the same; sometimes, it switched.
    • the switch could either occur to the same side as the SUA recording (ipsilateral) or to the opposite (contralateral).
  • found cells in the pre-SMA that would fire when the monkey had to change his adapted behavior
    • both cells that increased firing upon an ipsi-switch and contra-switch
  • microstimulated in SMA, and increased the number of correct trials!
    • 60ua, 0.2ms, cathodal only,
    • design: stimulation simulated adaptive-response related activity in a slightly advanced manner
    • don't actually have that many trials of this. humm?
  • they also did some go-nogo (no saccade) work, in which there were neurons responsive to inhibiting as well as facilitating saccades on both sides.
    • not a hell of a lot of neurons here nor trials, either - but i guess proper statistical design obviates the need for this.
  • I think if you recast this in tems of reward expectation it will make more sense and be less magical.
  • would like to do shadlen-similar type stuff in the STN
  1. how long did it take to train the monkeys to do this?
  2. what part of the nervous system looked at the planned action with visual context, and realized that the normal habitual basal-ganglia output would be wrong?
    1. probably the whole brain is involved in this.
    2. hypothetical path of error trials: visual system -> cortico-cortico projections + context activation -> preparatory motor activity -> basal ganglia + visual context (is there anatomical basis for this?) -> activation of some region that detects the motor plan is unlikely to result in reward -> SMA?


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ref: bookmark-0 tags: optimization function search matlab linear nonlinear programming date: 08-09-2007 02:21 gmt revision:0 [head]


very nice collection of links!!

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ref: GarciaRill-1991.01 tags: PPN pedunculopontine nucleus brainstem sleep locomotion consciousness 1991 date: 0-0-2007 0:0 revision:0 [head]

PMID-1887068 The Pedunculopontine nucleus

  • extensive review!

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ref: Nakanishi-2005.01 tags: schaal adaptive control function approximation error learning date: 0-0-2007 0:0 revision:0 [head]

PMID-15649663 Composite adaptive control with locally weighted statistical learning.

  • idea: want error-tracking plus locally-weighted peicewise linear function approximation (though , I didn't read it all that much in depth.. it is complicated)

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ref: Schaal-1998.11 tags: schaal local learning PLS partial least squares function approximation date: 0-0-2007 0:0 revision:0 [head]

PMID-9804671 Constructive incremental learning from only local information