m8ta
use https for features.
text: sort by
tags: modified
type: chronology
[0] Bar-Gad I, Morris G, Bergman H, Information processing, dimensionality reduction and reinforcement learning in the basal ganglia.Prog Neurobiol 71:6, 439-73 (2003 Dec)

{1521}
hide / / print
ref: -2005 tags: dimensionality reduction contrastive gradient descent date: 09-13-2020 02:49 gmt revision:2 [1] [0] [head]

Dimensionality reduction by learning and invariant mapping

  • Raia Hadsell, Sumit Chopra, Yann LeCun
  • Central idea: learn and invariant mapping of the input by minimizing mapped distance (e.g. the distance between outputs) when the samples are categorized as the same (same numbers in MNIST eg), and maximizing mapped distance when the samples are categorized as distant.
    • Two loss functions for same vs different.
  • This is an attraction-repulsion spring analogy.
  • Use gradient descent to change the weights to satisfy these two competing losses.
  • Resulting constitutional neural nets can extract camera pose information from the NORB dataset.
  • Surprising how simple analogies like this, when iterated across a great many samples, pull out intuitively correct invariances.

{1308}
hide / / print
ref: -0 tags: polyimide polyamide basic reduction salt surface modification date: 02-27-2015 19:45 gmt revision:0 [head]

Kinetics of Alkaline Hydrolysis of a Polyimide Surface

  • The alkaline hydrolysis of a polyimide (PMDA-ODA) surface was studied as a function of time, temperature and hydroxide ion concentration.
  • Quantification of the number of carboxylic acid groups formed on the modified polyimide surface was accomplished by analysis of data from contact angle titration experiments.
  • Using a large excess of base, pseudo-first-order kinetics were found, yielding kobs ≈ 0.1−0.9 min-1 for conversion of polyimide to poly(amic acid) depending on [OH-].
  • From the dependence of kobs on [OH-], a rate equation is proposed.
  • Conversion of the polyimide surface to one of poly(amic acid) was found to reach a limiting value with a formation constant, K, in the range 2−10 L·mol-1.

{1144}
hide / / print
ref: -0 tags: dopamine reinforcement learning funneling reduction basal ganglia striatum DBS date: 02-28-2012 01:29 gmt revision:2 [1] [0] [head]

PMID-15242667 Anatomical funneling, sparse connectivity and redundancy reduction in the neural networks of the basal ganglia

  • Major attributes of the BG:
    • Numerical reduction in the number of neurons across layers of the 'feed forward' (wrong!) network,
    • lateral inhibitory connections within the layers
    • modulatory effects of dopamine and acetylcholine.
  • Stochastic decision making task in monkeys.
  • Dopamine and ACh deliver different messages. DA much more specific.
  • Output nuclei of BG show uncorrelated activity.
    • THey see this as a means of compression -- more likely it is a training signal.
  • Striatum:
    • each striatal projection neuron receives 5300 cortico-striatal synapses; the dendritic fields of same contains 4e5 axons.
    • Say that a typical striatal neuron is spherical (?).
    • Striatal dendritic tree is very dense, whereas pallidal dendritic tree is sparse, with 4 main and 13 tips.
    • A striatal axon provides 240 synapses in the pallidum and makes 10 contacts with one pallidal neuron on average.
  • I don't necessarily disagree with the information-compression hypothesis, but I don't disagree either.
    • Learning seems a more likely hypothesis; could be that we fail to see many effects due to the transient nature of the signals, but I cannot do a thorough literature search on this.

PMID-15233923 Coincident but distinct messages of midbrain dopamine and striatal tonically active neurons.

  • Same task as above.
  • both ACh (putatively, TANs in this study) and DA neurons respond to reward related events.
  • dopamine neurons' response reflects mismatch between expectation and outcome in the positive domain
  • TANs are invariant to reward predictability.
  • TANs are synchronized; most DA neurons are not.
  • Striatum displays the densest staining in the CNS for dopamine (Lavoie et al 1989) and ACh (Holt et al 1997)
    • Depression of striatal acetylcholine can be used to treat PD (Pisani et al 2003).
    • Might be a DA/ ACh balance problem (Barbeau 1962).
  • Deficit of either DA or ACh has been shown to disrupt reward-related learning processes. (Kitabatake et al 2003, Matsumoto 1999, Knowlton et al 1996).
  • Upon reward, dopaminergic neurons increase firing rate, whereas ACh neurons pause.
  • Primates show overshoot -- for a probabalistic relative reward, they saturate anything above 0.8 probability to 1. Rats and pigeons do not show this effect (figure 2 F).

{255}
hide / / print
ref: BarGad-2003.12 tags: information dimensionality reduction reinforcement learning basal_ganglia RDDR SNR globus pallidus date: 01-16-2012 19:18 gmt revision:3 [2] [1] [0] [head]

PMID-15013228[] Information processing, dimensionality reduction, and reinforcement learning in the basal ganglia (2003)

  • long paper! looks like they used latex.
  • they focus on a 'new model' for the basal ganglia: reinforcement driven dimensionality reduction (RDDR)
  • in order to make sense of the system - according to them - any model must ingore huge ammounts of information about the studied areas.
  • ventral striatum = nucelus accumbens!
  • striatum is broken into two, rough, parts: ventral and dorsal
    • dorsal striatum: the caudate and putamen are a part of the
    • ventral striatum: the nucelus accumbens, medial and ventral portions of the caudate and putamen, and striatal cells of the olifactory tubercle (!) and anterior perforated substance.
  • ~90 of neurons in the striatum are medium spiny neurons
    • dendrites fill 0.5mm^3
    • cells have up and down states.
      • the states are controlled by intrinsic connections
      • project to GPe GPi & SNr (primarily), using GABA.
  • 1-2% of neurons in the striatum are tonically active neurons (TANs)
    • use acetylcholine (among others)
    • fewer spines
    • more sensitive to input
    • TANs encode information relevant to reinforcement or incentive behavior

____References____