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[0] Narayanan NS, Kimchi EY, Laubach M, Redundancy and synergy of neuronal ensembles in motor cortex.J Neurosci 25:17, 4207-16 (2005 Apr 27)

[0] Wood F, Fellows M, Donoghue J, Black M, Automatic spike sorting for neural decoding.Conf Proc IEEE Eng Med Biol Soc 6no Issue 4009-12 (2004)

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ref: -0 tags: coevolution fitness prediction schmidt genetic algorithm date: 09-14-2018 01:34 gmt revision:8 [7] [6] [5] [4] [3] [2] [head]

Coevolution of Fitness Predictors

  • Michael D. Schmidt and Hod Lipson, Member, IEEE
  • Fitness prediction is a technique to replace fitness evaluation in evolutionary algorithms with a light-weight approximation that adapts with the solution population.
    • Cannot approximate the full landscape, but shift focus during evolution.
    • Aka local caching.
    • Or adversarial techniques.
  • Instead use coevolution, with three populations:
    • 1) solutions to the original problem, evaluated using only fitness predictors;
    • 2) fitness predictors of the problem; and
    • 3) fitness trainers, whose exact fitness is used to train predictors.
      • Trainers are selected high variance solutions across the predictors, and predictors are trained on this subset.
  • Lightweight fitness predictors evolve faster than the solution population, so they cap the computational effort on that at 5% overall effort.
    • These fitness predictors are basically an array of integers which index the full training set -- very simple and linear. Maybe boring, but the simplest solution that works ...
    • They only sample 8 training examples for even complex 30-node solution functions (!!).
    • I guess, because the information introduced into the solution set is relatively small per generation, it makes little sense to over-sample or over-specify this; all that matters is that, on average, it's directionally correct and unbiased.
  • Used deterministic crowding selection as the evolutionary algorithm.
    • Similar individuals have to compete in tournaments for space.
  • Showed that the coevolution algorithm is capable of inferring even highly complex many-term functions
    • And, it uses function evaluations more efficiently than the 'exact' (each solution evaluated exactly) algorithm.
  • Coevolution algorithm seems to induce less 'bloat' in the complexity of the solutions.
  • See also {842}

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ref: -0 tags: dopamine reward prediction striatum error striatum orbitofrontal reward date: 02-24-2012 21:26 gmt revision:1 [0] [head]

PMID-11105648 Involvement of basal ganglia and orbitofrontal cortex in goal-directed behavior.

  • Many regions have a complex set of activations, but dopamine neurons appear more homogenous: they report the error in reward prediction.
    • "The homogeneity of responsiveness across the population of dopamine neurons indicates that this error signal is widely broadcast to dopamine terminal regions where it could provide a teaching signal for synaptic modifications underlying the learning of goal-directed appetitive behaviors."
    • Signals are not contingent on the type of behavior needed to obtain the reward, and hence represent a relatively 'pure' reward prediction error.
  • Unlike dopamine neurons, many striatal neurons respond to predicted rewards, although at least some may reflect the relative degree of predictability in the magnitude of the responses to reward.
  • Neuronal activations in the orbitofrontal cortex appear to involve less integration of behavioral and reward-related information, but rather incorporate another aspect of reward, the relative motivational significance of different rewards.
  • Processing is hierarchical (or supposed to be so):
    • Dopamine neurons provide a relatively pure signal of an error in reward prediction,
    • Striatal neurons signal not only reward, but also behavioral contingencies,
    • Orbitofrontal neurons signal reward and incorporate relative reward preference.

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ref: neuro notes-0 tags: clementine thesis electrophysiology fit predictions tlh24 date: 01-06-2012 03:07 gmt revision:4 [3] [2] [1] [0] [head]

ok, so i fit all timestamps from clem022007001 & timarm_log_070220_173947_k.mat to clementine's behavior, and got relatively low SNR for almost everything - despite the fact that I am most likely overfitting. (bin size = 7802 x 1491) the offset is calibrated @ 2587 ms + 50 to center the juice artifact in the first bin. There are 10 lags. There are 21 sorted units.

same thing, but with only the sorted units. juice prediction is, of course, worse.

now, for file clem022007002 & timarm_log_070220_175636_k.mat. first the unsorted:

and the sorted:

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ref: Kim-2007.08 tags: Hyun Kim muscle activation method BMI model prediction kinarm impedance control date: 01-06-2012 00:19 gmt revision:1 [0] [head]

PMID-17694874[0] The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm.

  • First BMI that successfully predicted interactions between the arm and a force field.
  • Previous BMIs are used to decode position, velocity, and acceleration, as each of these has been shown to be encoded in the motor cortex
  • Hyun talks about stiff tasks, like writing on paper vs . pliant tasks, like handling an egg; both require a mixture of force and position control.
  • Georgopoulous = velocity; Evarts = Force; Kalaska movement and force in an isometric task; [17-19] = joint dependence;
  • Todorov "On the role of primary motor cortex in arm movement control" [20] = muscle activation, which reproduces Georgouplous and Schwartz ("Direct cortical representation of drawing".
  • Kakei [19] "Muscle movement representations in the primary motor cortex" and Li [23] [1] show neurons correlate with both muscle activations and direction.
  • Argues that MAM is the best way to extract impedance information -- direct readout of impedance requires a supervised BMI to be trained on data where impedance is explicitly measured.
  • linear filter does not generalize to different force fields.
  • algorithm activity highly correlated with recorded EMG.
  • another interesting ref: [26] "Are complex control signals required for human arm movements?"

____References____

[0] Kim HK, Carmena JM, Biggs SJ, Hanson TL, Nicolelis MA, Srinivasan MA, The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm.IEEE Trans Biomed Eng 54:8, 1520-9 (2007 Aug)
[1] Li CS, Padoa-Schioppa C, Bizzi E, Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field.Neuron 30:2, 593-607 (2001 May)

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ref: -0 tags: the edge ideas future prediction date: 01-03-2011 19:26 gmt revision:2 [1] [0] [head]

Interesting ideas from __This Will Change Everything__

  • Daniel Dennett suggests that what is changing everything is the act of looking at what is changing everything: "When we look closely at looking closely, when we increase our investment in techniques for investing in techniques, this is what amplify uncertainties, what will change everything. We figure out how to game the system, and this initiates an arm race to control or prevent gaming of the system, which leads to new levels of gamesmanship, and so on."
    • Well said. I think this is an essential part of any creative economy.
  • The internet is humanity's growing global hindbrain: it attends itself with rote memory, managing commerce and markets, and doling out attention. This implies that eventually it will be a global forebrain
    • W. Danniel Hillis argues that it will do this through recursive hierarchical organization. But, that said, there is still no good way for making decisions with higher intelligence than each of the actors/voters. (really? are you sure this is not just an artifact of perception?)
  • Paul Saffo: "But there is one development that would fundamentally change everything: the discovert of nonhuman intelligences equal or superior to our own species. It would change everything because our crowded, quarreling species is lonely. Vastly, achingly, existentially lonely"
    • [If we do find someone/thing else:] "And despite the distance, of course we will try to talk to them. A third of us will try to conquer them, a third of us will seek to convert them, and the rest of us will try to sell them something". hah!
  • Mentioned: focus fusion technology and http://focusfusion.org/ -- looks excellent, the argument seems convincing. Why doesn't somebody throw some money at them, get it done and tested?
  • John Gottman paraphrases Peggy Sanday: "Military - or any hierarchical - social structure cannot last without external threat" Unfortunately, hierarchical structures (human and otherwise) also seem to be the best way for getting things done.

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ref: Gilbert-2009.03 tags: human prediction estimation social situation neighbor advice affective forecasting date: 06-10-2009 15:13 gmt revision:2 [1] [0] [head]

PMID-19299622[0] The Surprising Power of Neighborly Advice.

  • quote (I cannot say this any better!): "People make systematic errors when attempting to predict their affective reactions to future events, and these errors have social (1–3), economic (4–8), legal (9, 10), and medical (11–22) consequences. For example, people have been shown to overestimate how unhappy they will be after receiving bad test results (23), becoming disabled (14, 19–21), or being denied a promotion (24), and to overestimate how happy they will be after winning a prize (6), initiating a romantic relationship (24), or taking revenge against those who have harmed them (3). Research suggests that the main reason people mispredict their affective reactions to future events is that they imagine those events inaccurately (25). For example, people tend to imagine the essential features of future events but not the incidental features (26–28), the early moments of future events but not the later moments (17, 24), and so on. When mental simulations of events are inaccurate, the affective forecasts that are based on them tend to be inaccurate as well."
  • solution, ala François de La Rochefoucauld: "Before we set our hearts too much upon anything," he wrote, "let us first examine how happy those are who already possess it"
    • this is surrogation ; it relies not on mental simulation, hence is immune to the associated systematic errors.
    • problem is that people differ. paper agues that, in fact, they don't all that much - the valuations & affective reactions are produced by evolutionarily ancient physiological mechanisms. Furthermore, people's neighbors, friends, and peers are likely to all be similar in personality and preference via self-selection and social reinforcement - hence their reactions to a situation will be similar.
  • They used a speed-dating scenario in their experiments, from which they observe: "Women made more accurate predictions about how much they would enjoy a date with a man when they knew how much another woman in their social network enjoyed dating the man than when they read the man's personal profile and saw his photograph."
  • Next, they employ personality-evaluation "Men and women made more accurate predictions about how they would feel after being evaluated by a peer when they knew how another person in their social network had felt after being evaluated than when they previewed the evaluation itself."
  • Conclusion: "But given people's mistaken beliefs about the relative ineffectiveness of surrogation and their misplaced confidence in the accuracy of their own mental simulations (39), it seems likely that in everyday life, La Rochefoucauld's advice—like the advice of good neighbors—is more often than not ignored.
  • Editorializing: I'm not quite convinced that 'neighborly advice' is an accurate predictor of our absolute reaction to a situation as much as it socially informs us of reaction we are *supposed* to have. Society by consensus - that's what some of my European friends dislike about (some parts of) American culture. They need to run some controls in other cultures (?)

____References____

[0] Gilbert DT, Killingsworth MA, Eyre RN, Wilson TD, The surprising power of neighborly advice.Science 323:5921, 1617-9 (2009 Mar 20)

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ref: Narayanan-2005.04 tags: Laubach M1 motor rats statistics BMI prediction methods date: 09-07-2008 19:51 gmt revision:4 [3] [2] [1] [0] [head]

PMID-15858046[] Redundancy and Synergy of Neuronal Ensembles in Motor Cortex

  • timing task.
  • rats.
  • 50um teflon microwires in motor cortex
  • ohno : neurons that were the best predictors of task performance were not necessarily the neurons that contributed the most predictive information to an ensemble of neurons.
  • most all contribute redundant predictive information to the ensemble.
    • this redundancy kept the predictions high, even if neurons were dropped.
  • small groups of neurons were more synergistic
  • large groups were more redundant.
  • used wavelet based discriminant pursuit.
    • validated with draws from a random data set.
  • used R and Weka
  • data looks hella noisy ?

____References____

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ref: Wood-2004.01 tags: spikes sorting BMI Black Donoghue prediction kalman date: 04-06-2007 21:57 gmt revision:2 [1] [0] [head]

PMID-17271178[0] automatic spike sorting for neural decoding

  • idea: select the number of units (and, indeed, clustering) based on the ability to predict a given variable. makes sense!
  • results:
    • human sorting: 13,5 cm^2 MSE
    • automatic spike sorting: 11.4 cm^2 MSE
      • yes, I know, the increase is totally dramatic.
  • they do not say if this could be implemented in realtime or not. hence, probably not.

____References____

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ref: bookmark-0 tags: smith predictor motor control wolpert cerebellum machine_learning prediction date: 0-0-2006 0:0 revision:0 [head]

http://prism.bham.ac.uk/pdf_files/SmithPred_93.PDF

  • quote in reference to models in which the cerebellum works as a smith predictor, e.g. feedforward prediction of the behavior of the limbs, eyes, trunk: Motor performance based on the use of such internal models would be degraded if the model was inavailable or inaccurate. These theories could therefore account for dysmetria, tremor, and dyssynergia, and perhaps also for increased reaction times.
  • note the difference between inverse model (transforms end target to a motor plan) and inverse models 9is used on-line in a tight feedback loop).
  • The difficulty becomes one of detecting mismatches between a rapid prediction of the outcome of a movement and the real feedback that arrives later in time (duh! :)
  • good set of notes on simple simulated smith predictor performance.

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ref: Stapleton-2006.04 tags: Stapleton Lavine poisson prediction gustatory discrimination statistical_model rats bayes BUGS date: 0-0-2006 0:0 revision:0 [head]

PMID-16611830

http://www.jneurosci.org/cgi/content/full/26/15/4126