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[Fwd: Another Math-Neuro talk: Oct 9th]
by Yoshua Bengio 02 Oct '07

02 Oct '07

-------- Original Message -------- Subject: Another Math-Neuro talk: Oct 9th Date: Tue, 2 Oct 2007 15:03:15 -0400 From: Paul Cisek <paul.cisek(a)umontreal.ca> Hello, We are going to have another math-neuro presentation next week, on Tuesday Oct 9th, from 14h00-15h00 in room 4106. The presentation will be by Jean-Phillippe Thivierge, who is briefly visiting from Indiana (I apologize for doing this on a Tuesday before the "travaux récents", but it was the only possible time). Here's an abstract: "How Random is Brain Activity?" "Neurons of several regions of the brain, including hippocampus and cortex, have a tendency to spontaneously alternate between states of activity and quiescence at non-periodic, seemingly random times. Despite the ubiquitous nature of this phenomenon across species and protocols, the mechanisms responsible remain largely unknown. Using computer simulations, we show that spontaneous synchronization can emerge naturally from completely deterministic cellular interactions, without randomness. Noise-free simulations capture the Poisson-like distribution of both spontaneous and evoked activity recorded in acute CA1 slices of hippocampus, and suggest new experiments and their expected outcome. This talk is hoped to generate some interesting discussions among neurophysiologists and modelers." Regards, Paul ----------------------------------------------- Paul Cisek, Ph.D. Department of physiology, room 4141 University of Montreal C.P. 6128 Succursale Centre-ville Montreal QC H3C 3J7 Canada phone: 514-343-6111 x4355 FAX: 514-343-2111 email: paul.cisek(a)umontreal.ca <mailto:paul.cisek@umontreal.ca> ----------------------------------------------- -- Yoshua Bengio

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[Fwd: Math-Neuro lecture on Oct 4th: Basal Ganglia and Reinforcement Learning]
by Yoshua Bengio 01 Oct '07

01 Oct '07

-------- Message original -------- Sujet: Math-Neuro lecture on Oct 4th: Basal Ganglia and Reinforcement Learning Date: Mon, 1 Oct 2007 12:55:15 -0400 De: Paul Cisek <paul.cisek(a)umontreal.ca> Répondre à :: Paul Cisek <paul.cisek(a)umontreal.ca> Hello everyone, This Thursday, we will have a Math-Neuro meeting in which Francois Rivest will give us a presentation on models of reinforcement learning. Here is the abstract: "Over the past 10 years, a set of computationnel models coming from reinforcement learning have gained popularity in modeling of dopaminergic neurons phasic signal (Montague, Schultz, Daw, & others). These models allow making a number of hypotheses about possible roles of the basal ganglia (especially striatum) and about local plasticity. In this presentation, I will introduce the actor-critic temporal-difference learning family of algorithm that has been used to model the phasic dopaminergic neuron signal. I will concentrate on explaining how this algorithm works with minimal mathematics, and as many examples as possible. I hope to generate some discussion with people working on dopaminergic or basal ganglia neurons." Our meeting will take place in room 1120 of Pavillon Paul-Desmarais (2960 chemin de la tour) from 13h30 to 15h00 on Thursday, October 4th. Hope to see you there, Paul P.S. Trevor, Louis-Eric, Peter: I thought you and/or your students might be interested as well. ----------------------------------------------- Paul Cisek, Ph.D. Department of physiology, room 4141 University of Montreal C.P. 6128 Succursale Centre-ville Montreal QC H3C 3J7 Canada phone: 514-343-6111 x4355 FAX: 514-343-2111 email: paul.cisek(a)umontreal.ca <mailto:paul.cisek@umontreal.ca> -----------------------------------------------

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REMINDER: UdeM-McGill-MITACS machine learning seminar, PAA 1409 (UdeM), Fri Sept 28, 11:30am]
by Hugo Larochelle 27 Sep '07

27 Sep '07

This week's seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en) How Many Clusters? An Information-Theoretic Perspective by Susanna Still, Department of Information and Computer Sciences University of Hawaii Location: Pavillon Andre-Aisenstadt (UdeM), room 1409 Time: September 28th 2007, 11h30 Clustering provides a common means of identifying structure in complex data, and there is renewed interest in clustering as a tool for the analysis of large data sets in many fields. Anatural question is how many clusters are appropriate for the description of a given system. Traditional approaches to this problem are based on either a framework in which clusters of a particular shape are assumed as a model of the system or on a two-step procedure in which a clustering criterion determines the optimal assignments for a given number of clusters and a separate criterion measures the goodness of the classification to determine the number of clusters. In a statistical mechanics approach, clustering can be seen as a trade-off between energy- and entropy-like terms, with lower temperature driving the proliferation of clusters to provide a more detailed description of the data. For finite data sets, we expect that there is a limit to the meaningful structure that can be resolved and therefore a minimum temperature beyond which we will capture sampling noise. This suggests that correcting the clustering criterion for the bias that arises due to sampling errors will allow us to find a clustering solution at a temperature that is optimal in the sense that we capture maximal meaningful structure-without having to define an external criterion for the goodness or stability of the clustering. We show that in a general information-theoretic framework, the finite size of a data set determines an optimal temperature, and we introduce a method for finding the maximal number of clusters that can be resolved from the data in the hard clustering limit. _______________________________________________ Lisa_seminaires mailing list Lisa_seminaires(a)mercure.iro.umontreal.ca https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_seminaires

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UdeM-McGill-MITACS machine learning seminar, PAA 1409 (UdeM), Fri Sept 28, 11:30am
by Hugo Larochelle 24 Sep '07

24 Sep '07

This week's seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en) How Many Clusters? An Information-Theoretic Perspective by Susanna Still, Department of Information and Computer Sciences University of Hawaii Location: Pavillon Andre-Aisenstadt (UdeM), room 1409 Time: September 28th 2007, 11h30 Clustering provides a common means of identifying structure in complex data, and there is renewed interest in clustering as a tool for the analysis of large data sets in many fields.Anatural question is how many clusters are appropriate for the description of a given system. Traditional approaches to this problem are based on either a framework in which clusters of a particular shape are assumed as a model of the system or on a two-step procedure in which a clustering criterion determines the optimal assignments for a given number of clusters and a separate criterion measures the goodness of the classification to determine the number of clusters. In a statistical mechanics approach, clustering can be seen as a trade-off between energy- and entropy-like terms, with lower temperature driving the proliferation of clusters to provide a more detailed description of the data. For finite data sets, we expect that there is a limit to the meaningful structure that can be resolved and therefore a minimum temperature beyond which we will capture sampling noise. This suggests that correcting the clustering criterion for the bias that arises due to sampling errors will allow us to find a clustering solution at a temperature that is optimal in the sense that we capture maximal meaningful structure-without having to define an external criterion for the goodness or stability of the clustering. We show that in a general information-theoretic framework, the finite size of a data set determines an optimal temperature, and we introduce a method for finding the maximal number of clusters that can be resolved from the data in the hard clustering limit.

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ROOM CHANGE: UdeM-McGill-MITACS machine learning seminar, MC 437
by Hugo Larochelle 13 Sep '07

13 Sep '07

The UdeM-McGill-MITACS Machine Learning seminars are back! Note that this year, there will be seminars only every 2 weeks. The seminar will be in room 437, NOT 103! This week's seminar (see https://www.iro.umontreal.ca/article.php3? id_article=107&lang=en). Optimal Causal Inference by Susanna Still, Department of Information and Computer Sciences University of Hawaii Location: McConnell Engineering building (McGill), room 437 Time: September 14th 2007, 11h30 I will talk about how theory building can naturally distinguish between regularity and randomness. Starting from basic modeling principles I will argue for a general information-theoretic objective function that embodies a trade-off between a model’s complexity and its predictive power. The family of solutions derived from this principle corresponds to a hierarchy of models. At each level of complexity, those models achieve maximal predictive power, and in the limit of optimal prediction a process’ exact causal organization is identified. Examples show how theory building can profit from analyzing a process’ causal compressibility, which is reflected in the optimal models’ rate-distortion curve.

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UdeM-McGill-MITACS machine learning seminar, MC 103 (McGill), Fry Sept 14, 11:30am
by Hugo Larochelle 10 Sep '07

10 Sep '07

The UdeM-McGill-MITACS Machine Learning seminars are back! Note that this year, there will be seminars only every 2 weeks. This week's seminar (see https://www.iro.umontreal.ca/article.php3? id_article=107&lang=en). Optimal Causal Inference by Susanna Still, Department of Information and Computer Sciences University of Hawaii Location: McConnell Engineering building (McGill), room 103 Time: September 14th 2007, 11h30 I will talk about how theory building can naturally distinguish between regularity and randomness. Starting from basic modeling principles I will argue for a general information-theoretic objective function that embodies a trade-off between a model’s complexity and its predictive power. The family of solutions derived from this principle corresponds to a hierarchy of models. At each level of complexity, those models achieve maximal predictive power, and in the limit of optimal prediction a process’ exact causal organization is identified. Examples show how theory building can profit from analyzing a process’ causal compressibility, which is reflected in the optimal models’ rate-distortion curve.

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