artificial-neural-networks &laquo; WordPress.com Tag Feed http://wordpress.com/tag/artificial-neural-networks/ Feed of posts on WordPress.com tagged "artificial-neural-networks" Fri, 25 Jul 2008 20:11:00 +0000 http://wordpress.com/tags/ en <![CDATA[Badania w Statistice ("Statistica entertainment";)]]> http://aeva.wordpress.com/?p=40 Wed, 20 Feb 2008 01:33:44 +0000 aeva http://aeva.wordpress.com/?p=40 A oto i pierwsze mądre wnioski z badań - dotyczące... trybów pracy z SSN w Statistice:)

Zaczynałam od "Automatycznego projektanta" - taki odpowiednik łizarda, który na podstawie podanych parametrów (liczba opóźnień szeregu, liczba warstw i neuronów w warstwach ukrytych itd.)  przeszukuje w zadanym czasie lub dla zadanej liczby - przestrzeń topologii sieci. Dużą zaletą jest to, że pozwala wyrobić intuicję i wskazuje dalsze kierunki poszukiwań, a dużą wadą to, że wiele ważnych parametrów jest ukrytych, nie mamy kontroli nad procesem budowania modelu, czyli... "czarna skrzynka w czarnej skrzynce";)

Dalej pracuje z projektami predykcyjnych modeli Data-Mining. Używam tam Automatycznego Projektanta jako obiektu, przy czym mam widok na model jako całość, mogę edytować kod , podłączać różne źródła danych i  podawać dane"do wdrożenia" (czyli do zastosowania na gotowym modelu).

Następnie wybraną sieć - zapisaną w pliku lub opisaną ręcznie -  analizuję szczegółowo w trybie "Projekt sieci użytkownika". Tam widać wszystkie szczegóły, w tym nawet wagi poszczególnych krawędzi.

Oto moje wnioski po setkach (sic!) godzin pracy ze Statisticą - najlepszą z możliwych - metodą "trial&error":)

PS: W kolejnym odcinku - zarys planu badań, czyli jakie hipotezy będę badać oraz jak pozyskiwać uran domowym sposobem...

]]> <![CDATA[The Motivation behind Artificial Neural Networks]]> http://onionesquereality.wordpress.com/?p=62 Fri, 15 Feb 2008 15:53:19 +0000 Shubhendu Trivedi http://onionesquereality.wordpress.com/?p=62 neurons

The human brain is an incredibly impressive information processor, even though it "works" quite a bit slower than an ordinary computer. Many researchers in artificial intelligence look to the organization of the brain as a model for building intelligent machines. Think of a sort of "analogy" between the complex webs of interconnected neurons in a brain and the densely interconnected units making up an artificial neural network (ANN), where each unit--just like a biological neuron--is capable of taking in a number of inputs and producing an output. Consider this description: "To develop a feel for this analogy, let us consider a few facts from neurobiology. The human brain is estimated to contain a densely interconnected network of approximately 1011 neurons, each connected, on average, to 104 others. Neuron activity is typically excited or inhibited through connections to other neurons. The fastest neuron switching times are known to be on the order of 10-3 seconds---quite slow compared to computer switching speeds of 10-10 seconds. Yet humans are able to make surprisingly complex decisions, surprisingly quickly. For example, it requires approximately 10-1 seconds to visually recognize your mother.Notice the sequence of neuron firings that can take place during this 10-1-second interval cannot possibly be longer than a few hundred steps, giving the switching speed of single neurons. This observation has led many to speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. One motivation for ANN systems is to capture this kind of highly parallel computation based on distributed representations."

via Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).]


]]> <![CDATA[dla tych, którzy nie lubią czytać;)]]> http://aeva.wordpress.com/?p=39 Wed, 13 Feb 2008 23:29:07 +0000 aeva http://aeva.wordpress.com/?p=39 Odkrycie dnia - youtube w służbie nauki:

http://youtube.com/watch?v=AyzOUbkUf3M

Godzinny wykład, fajny wstępniak o sieciach i prezentacja nowego pomysłu na algorytm uczenia.

]]> <![CDATA[myślę po polsku, więc piszę po polsku...:P]]> http://aeva.wordpress.com/?p=38 Fri, 08 Feb 2008 09:00:07 +0000 aeva http://aeva.wordpress.com/?p=38 Language jezyk = new Language("Polski");

Zmieniłam język i poprzestaję na angielskich tagach. No i wklejam kolejnego ciekawego linka (ciekawe, czy ktoś w nie klika...):

http://www.e-mentor.edu.pl/artykul_v2.php?numer=20&id=448  

No to jeszcze jak zacznę pisać dłuższe notki, to już w ogóle będzie sukces;)

ps: już wkrótce - wyniki symulacji ze Statistici (moje neurony zaczynają do mnie mówić "Mama":) plus pewnie jakieś ciekawe materiały ze starych esejów i projektów.

houk

]]> <![CDATA[Biologically Inspired Artificial Intelligence for Computer Games]]> http://learninggames.wordpress.com/2008/01/04/biologically-inspired-artificial-intelligence-for-computer-games/ Fri, 04 Jan 2008 15:42:49 +0000 Daniel Livingstone http://learninggames.wordpress.com/2008/01/04/biologically-inspired-artificial-intelligence-for-computer-games/ A little off-topic, but I'm pleased to announce that a book I co-authored is soon to be published by IGI Global:

Biologically Inspired Artificial Intelligence for Computer Games

ISBN: 978-1-59140-646-4; 278 pp; November 2007

Published under the imprint Medical Information Science Reference (formerly Idea Group Publishing)

Authors: Darryl Charles, University of Ulster, Ireland and Colin Fyfe, Daniel Livingstone, and Stephen McGlinchey, University of Paisley, UK

DESCRIPTION

Computer games are often played by a human player against an artificial intelligence software entity. In order to truly respond in a human-like manner, the artificial intelligence in games must be adaptive, or respond as a human player would as he/she learns to play a game.

Biologically Inspired Artificial Intelligence for Computer Games reviews several strands of modern artificial intelligence, including supervised and unsupervised artificial neural networks; evolutionary algorithms; artificial immune systems, swarms, and shows—using case studies for each to display how they may be applied to computer games. This book spans the divide which currently exists between the academic research community working with advanced artificial intelligence techniques and the games programming community which must create and release new, robust, and interesting games on strict deadlines, thereby creating an invaluable collection supporting both technological research and the gaming industry.

This book concentrates on technologies which are still the subject of large scale effort in the research field – such as artificial neural networks, genetic algorithms or artificial immune systems.”
-Darryl Charles, University of Ulster, Ireland

For more information about Biologically Inspired Artificial Intelligence for Computer Games, you can view the title information sheet at http://www.igi-global.com/downloads/pdf/charles.pdf. You can read more details and view the preface of the publication online at http://www.igi-global.com/reference/details.asp?id=7717.ABOUT THE AUTHORS

Colin Fyfe is an active researcher in artificial neural networks, genetic algorithms, artificial immune systems and artificial life having written over 250 refereed papers, several book chapters and two books. He is a member of the editorial board of the International Journal of Knowledge-Based Intelligent Engineering Systems and an associate editor of International Journal of Neural Systems. He currently supervises six PhD students and has been director of studies for 16 PhDs since 1998. He is a member of the academic advisory board of the International Computer Science Conventions group and a committee member of the European Network of Excellence on Intelligent Technologies for Smart Adaptive Systems (EUNITE). He has been visiting researcher at the University of Strathclyde, the Riken Institute in Tokyo, the Chinese University of Hong Kong, and visiting professor at the University of Vigo, the University of Burgos, and the University of Salamanca, all in Spain.

Darryl Charles graduated from Queens University Belfast with a degree in electrical and electronic engineering (Hons.) in 1988. After qualifying as a teacher at Stranmillis College (Belfast), he taught technology and design at Portadown College until 1995 and during this spell completed an MSc in microelectronics and microcomputer applications. He then spent a year as head of IT at Cox Green School in Maidenhead before going back into higher education to study for a PhD at the University of Paisley (Scotland). After completing a PhD in unsupervised neural networks in 1999 he was appointed as a lecturer then as a senior lecturer in computing at Paisley. In 2001, he returned to Northern Ireland to take up a lecturing post at the University of Ulster where his teaching and research specialism is now within the realm of computer games and in particular adaptation.

Stephen McGlinchey received the BSc (Hons) degree in computing science from the University of Paisley (1996), and went on to do a PhD in neural networks (2000) also at Paisley. He now works as a lecturer at the University of Paisley, teaching computer games technology. He has published several research papers, mainly on neural networks and artificial intelligence for games. Recently, his research work has focused on ant colony algorithms for path-finding in computer games, and automatic post-processing of motion capture data.

Daniel Livingstone received a BSc (Hons) in computer & electronic engineering from the University of Strathclyde (1993), an MSc with distinction in computer science (AI) from the University of Essex (1995) and a PhD (modelling the evolution of human language and languages) from the University of Paisley (2003). He currently lectures a range of classes related to computer game development, and his research interests range from AI and Artificial Life for computer games to the use of game technology in education. His current work is now focusing on the use of massively-multiplayer virtual worlds as learning platforms.

]]> <![CDATA[Eliza in the Chinese Room]]> http://aeva.wordpress.com/2007/10/22/eliza-in-the-chinese-room/ Mon, 22 Oct 2007 07:16:18 +0000 aeva http://aeva.wordpress.com/2007/10/22/eliza-in-the-chinese-room/ Brain produces mind - syntax vs semantics. That puts my work in the area of "weak AI".

http://pl.wikipedia.org/wiki/Chi%C5%84ski_pok%C3%B3j

http://en.wikipedia.org/wiki/Chinese_room

Is thinking a kind of computation?

http://en.wikipedia.org/wiki/Philosophy_of_artificial_intelligence#Strong_AI_vs._weak_AI

]]> <![CDATA[Nothing to add ;)]]> http://aeva.wordpress.com/2007/10/18/nothing-to-add/ Thu, 18 Oct 2007 23:14:30 +0000 aeva http://aeva.wordpress.com/2007/10/18/nothing-to-add/ That's how one of NN gurus recommends and describes neural network approach:

"multivariate nonlinear nonparametric inference technique that is data driven and model free."

Azoff(1994)

]]> <![CDATA[video lectures]]> http://amwp.wordpress.com/2007/08/25/video-lectures-3/ Sat, 25 Aug 2007 00:36:28 +0000 am http://amwp.wordpress.com/2007/08/25/video-lectures-3/ on Machine Learning, Data Mining, Image Analysis, Semantic Web
http://videolectures.net/Top/Computer_Science/
http://videolectures.net/site/list/latest/
http://videolectures.net/site/list/tutorials/

]]> <![CDATA[Conditional random fields]]> http://amwp.wordpress.com/2007/08/18/conditional-random-fields/ Sat, 18 Aug 2007 02:46:54 +0000 am http://amwp.wordpress.com/2007/08/18/conditional-random-fields/ "Markov Random Fields and Their Applications" by R.Kindermann and J.L.Snell (1980)
http://www.ams.org/online_bks/conm1/
Wikipedia:
http://en.wikipedia.org/wiki/Markov_random_field
http://en.wikipedia.org/wiki/Conditional_random_field
J.Lafferty, A.McCallum, F.Pereira "Conditional random fields: Probabilistic models for segmenting and labeling
sequence data." In: Proc. 18th Intl Conf. on Machine Learning, Morgan Kaufmann, SF, CA (2001) pp.282–289
http://citeseer.ist.psu.edu/lafferty01conditional.html
http://www.cis.upenn.edu/~pereira/papers/crf.pdf
J.Lafferty, X.Zhu, Y.Liu "Kernel Conditional Random Fields: Representation and Clique Selection" (2004)
http://citeseer.ist.psu.edu/lafferty04kernel.html
F.Perez-Cruz, Z.Ghahramani, M.Pontil "Conditional Graphical Models" (2006)
http://www.gatsby.ucl.ac.uk/~fernando/CGM.pdf
A.McCallum, D.Freitag, F.Pereira "Maximum entropy Markov models for information extraction
and segmentation" In proceedings of ICML-2000, 2000
http://citeseer.ist.psu.edu/mccallum00maximum.html
annotated bibliography:
http://www.inference.phy.cam.ac.uk/hmw26/crf/

]]> <![CDATA[face recognition]]> http://amwp.wordpress.com/2007/06/22/face-recognition/ Fri, 22 Jun 2007 15:26:47 +0000 am http://amwp.wordpress.com/2007/06/22/face-recognition/ useful core algorithms papers collected:
http://www.face-rec.org/algorithms/

]]> <![CDATA[]]> http://amwp.wordpress.com/2007/06/13/389/ Wed, 13 Jun 2007 05:46:58 +0000 am http://amwp.wordpress.com/2007/06/13/389/ Annutka ([info]annutka) wrote, 12 June 07, 06:24:00

 http://annutka.livejournal.com/1050160.html

"

еще один блог человека известного в теор информатике
о теор информатике, образование итд итп
Michael Mitzenmacher из Гарварда
http://www.mybiasedcoin.blogspot.com/
там попалась ссылка на хороший ридинг лист его курса
http://www.eecs.harvard.edu/%7Emichaelm/CS222/class.html

"

]]> <![CDATA[induced memory]]> http://amwp.wordpress.com/2007/06/12/induced-memory/ Tue, 12 Jun 2007 01:32:21 +0000 am http://amwp.wordpress.com/2007/06/12/induced-memory/ in live neural network

http://www.newscientist.com/channel/being-human/brain/mg19426075.700-data-stored-in-live-neurons.html
I.Baruchi, E.Ben-Jacob "Towards neuro-memory-chip: Imprinting multiple memories in cultured neural networks."
Phys.Rev. E 75, 050901(R), 2007 http://link.aps.org/doi/10.1103/PhysRevE.75.050901
http://star.tau.ac.il/~eshel/papers/Neuro%20Memory%20Chip.pdf

learning by external electrical signal pattern induction:
G.Shahaf, S.Marom "Learning in Networks of Cortical Neurons." The Journal of Neuroscience, Nov.15, 2001,
21(22):8782–8788 http://www.jneurosci.org/cgi/content/abstract/21/22/8782
S.Marom, G.Shahaf "Development, learning and memory in large random networks
of cortical neurons: lessons beyond anatomy." Quarterly Reviews of Biophysics 35, 1 (2002)
pp. 63–87. Cambridge University Press. http://brc.technion.ac.il/QRB.pdf
D.Eytan, N.Brenner, S.Marom "Selective Adaptation in Networks of Cortical Neurons." J. Neurosci., Oc.15, 2003;
23(28): 9349 - 9356. http://www.jneurosci.org/cgi/content/abstract/23/28/9349
D.Eytan, S.Marom "Dynamics and Effective Topology Underlying Synchronization in Networks of Cortical Neurons."
The Journal of Neuroscience, August 16, 2006, 26(33):8465-8476; doi:10.1523/JNEUROSCI.1627-06.2006
http://www.jneurosci.org/cgi/content/abstract/26/33/8465
S.Marom's full papers PDFs are available here: http://brc.technion.ac.il/marom.html

]]> <![CDATA[information flow in multiagents systems]]> http://amwp.wordpress.com/2007/06/07/information-flow-in-multiagents-systems/ Thu, 07 Jun 2007 03:47:00 +0000 am http://amwp.wordpress.com/2007/06/07/information-flow-in-multiagents-systems/

Capdepuy,P., Polani,D., Nehaniv,C. (2007) "Maximization of Potential Information Flow as a Universal
Utility for Collective Behaviour. In 2007 IEEE Symposium on Artificial Life
found here: http://homepages.feis.herts.ac.uk/~comqdp1/publications.html
Klyubin, A., Polani, D., Nehaniv, C. (2007) "Representations of Space and Time in the Maximization
of Information Flow in the Perception-Action Loop." Neural Computation. In Press.
A.S.Klyubin, D.Polani, C.L.Nehaniv (2004) "Organization of the information flow in the perception-action
loop of evolved agents" In Proc. of 2004 NASA/DoD Conf. on Evolvable Hardware, pp.177-180. IEEE Comp. Soc.
http://homepages.feis.herts.ac.uk/~comqdp1/publications/files/eh2004.pdf
http://homepages.feis.herts.ac.uk/~ka2by/papers/eh2004.pdf
A.S.Klyubin, D.Polani, C.L.Nehaniv (2005) "Empowerment: a universal agent-centric measure of control"
In Proc. IEEE Congress on Evolutionary Comput. 2-5 Sept. 2005, Edinburgh, Scotland (CEC2005), pp.128-135
http://homepages.feis.herts.ac.uk/~comqdp1/publications/files/cec2005_klyubin_polani_nehaniv.pdf
C.R.Shalizi, J.P.Crutchfield (2002) "Information bottlenecks, casual states, statistical relevance bases: how to
represent relevant information in memoryless transduction" Adv. in Complex Syst., 5(1):91-95
http://www.santafe.edu/research/publications/workingpapers/00-07-035.pdf
http://www.citebase.org/abstract?id=oai%3AarXiv.org%3Anlin%2F0006025

]]> <![CDATA[JAM - just another mindmap;)]]> http://aeva.wordpress.com/2007/04/15/jam-just-another-mindmap/ Sun, 15 Apr 2007 20:56:16 +0000 aeva http://aeva.wordpress.com/2007/04/15/jam-just-another-mindmap/ Application possibilities by P.Lula (Polish)

metody-analizy-danych.jpeg

]]> <![CDATA[Currently studied... - Efstathios Kalyvas' article ]]> http://aeva.wordpress.com/2007/04/15/currently-studied-efstathios-kalyvas-article/ Sun, 15 Apr 2007 18:29:50 +0000 aeva http://aeva.wordpress.com/2007/04/15/currently-studied-efstathios-kalyvas-article/ Using Neural Networks and Genetic Algorithms to Predict Stock Market Returns

please read through (or thoroughly;) - I'm very curious about your opinions...

]]> <![CDATA[please find enclosed...]]> http://aeva.wordpress.com/2007/04/14/please-find-enclosed/ Sat, 14 Apr 2007 13:05:21 +0000 aeva http://aeva.wordpress.com/2007/04/14/please-find-enclosed/  ... my Master's Thesis Seminar presentations:

Second seminar presentation

First seminar presentation

Thank's for all who contributed in creating such a nice, ideas-generating and discussion -friendly atmosphere at our seminar:)

]]> <![CDATA[Currently studied... - FF tutorial and distributed parallel processing mechanism]]> http://aeva.wordpress.com/2007/03/25/currently-studied/ Sun, 25 Mar 2007 18:20:43 +0000 aeva http://aeva.wordpress.com/2007/03/25/currently-studied/ After some doubts (Matlab? Statistica?) - coming back to Joone:)

http://www.jooneworld.com/wiki/tiki-index.php?page=FinancialForecastTutorial

And I came along the DTE - Distributed Training Environment idea, which essence is: 'train once, run anywhere' . I think it's genius! Especially the possible application mentioned (here: http://www.jooneworld.com/docs/dte.html) - global optimization for Financial Forecasting! Can you imagine that?

The key words are: enhanced input and parameter selection, parallel training and committee of experts. Should you need further details - please find the link enclosed;)

]]> <![CDATA[Cellular neural networks]]> http://amwp.wordpress.com/2005/02/14/cellular-neural-networks/ Mon, 14 Feb 2005 03:11:00 +0000 am http://amwp.wordpress.com/2005/02/14/cellular-neural-networks/ for contour tracking
http://lab.analogic.sztaki.hu/cnnbooks.html
Link from:
http://www.ercim.org/publication/Ercim_News/enw60/hillier.html
http://www.ercim.org/publication/Ercim_News/enw60/szabo.html
another CNN:
http://lslwww.epfl.ch/biowall/VersionE/ApplicationsE/ApplicationsE.html

]]> <![CDATA[Varshavskiy, von Neumann]]> http://amwp.wordpress.com/2005/02/02/varshavskiy-von-neumann/ Wed, 02 Feb 2005 09:17:00 +0000 am http://amwp.wordpress.com/2005/02/02/varshavskiy-von-neumann/ Хорошая книга:
Варшавский В. И., Поспелов Д. А.
"Оркестр играет без дирижера: размышления об эволюции некоторых
технических систем и управлении ими"
http://www.imnlp.kiev.ua/show_id.php?sct=lib&id=1044111839

%%%%%%%%%%%%%%

Джон фон Нейман
"Теория самовоспроизводящихся автоматов." M.:Мир, 1971
не полностью
http://djvu-lib.narod.ru/theorycompsci/index.html
link from
http://www.delphikingdom.ru/foliant/djvu.htm

Фомин С. В., Беркинблит М.Б. "Математические проблемы в биологии" 1973. 200 с.
http://www.library.biophys.msu.ru/FominBerk/main.htm

нет в сети:
Цетлин М.Л. "Исследования по теории автоматов и моделирование биологических систем".
-М.:Наука, 1969. -316 с.

В.И. Варшавский умер
http://www.eltech.ru/news/varhavskiy.htm

]]> <![CDATA[few DSP links refresh]]> http://amwp.wordpress.com/2004/10/13/few-dsp-links-refresh/ Wed, 13 Oct 2004 12:32:00 +0000 am http://amwp.wordpress.com/2004/10/13/few-dsp-links-refresh/ from the post @ 2002-04-29 18:25:00

Lambert, R. H., "Multichannel Blind Deconvolution:
FIR Matrix Algebra and Separation of Multipath Mixtures",
PHD dissertation, University of Southern California, May 1996
http://home.socal.rr.com/russdsp/Mydis.zip
http://sipi.usc.edu/reports/abstracts/usc-sipi.297.html
http://citeseer.ist.psu.edu/lambert96multichannel.html
Signal and Image Processing Institute of the University of Southern California,
Technical reports list: http://sipi.usc.edu/reports/ReportList.html
Lambert's home page (cool matlab package):
http://home.socal.rr.com/russdsp/

related:

"Polynomial Matrix Whitening And Application To The Multichannel Blind Deconvolution Problem" (1995)
Russell H. Lambert, Chrysostomos L. Nikias
http://citeseer.ist.psu.edu/lambert95polynomial.html

Marcel Joho, Philip Schniter, "Frequency domain realization of a multichannel blind deconvolution
algorithm based on the natural gradient", 4th International Symposium on Independent Component
Analysis and Blind Signal Separation (ICA2003), April 2003, Nara, Japan
http://www.kecl.ntt.co.jp/icl/signal/ica2003/cdrom/data/0090.pdf

"An FFT-Based Algorithm for Multichannel Blind Deconvolution" (1999)
Marcel Joho, Heinz Mathis, George S. Moschytz
http://citeseer.ist.psu.edu/joho99fftbased.html

some OTHERS

A. Agarwal and Y.M. Cheng,
"Two-stage mel warped Wiener filter for robust speech recognition".
The 1999 International Workshop on Automatic Speech Recognition and Understanding
(ASRU'99), December 12-15, 1999, Keystone, Colorado, USA
http://citeseer.ist.psu.edu/300967.html

Zibulevsky, M. and Pearlmutter, B.A. (1999).
"Blind Source Separation by Sparse Decomposition"
http://citeseer.ist.psu.edu/zibulevsky00blind.html
papers: http://iew3.technion.ac.il/~mcib/

Smaragdis, P., Ph.D. dissertation, MAS Dept., MIT. 2001.
Redundancy reduction for computational audition, a unifying approach.
http://web.media.mit.edu/~paris/phd/
Smaragdis, P., Masters Thesis, MAS Dept., MIT. 1997.
Information Theoretic Approaches to Source Separation,
http://sound.media.mit.edu/~paris/paris-msc.ps.gz

from the end of the list
...
"Learning with missing data using Genetic Programming"
Gerriet Backer
http://www.pa.info.mie-u.ac.jp/bioele/wsc1/papers/p041.html

"A Tutorial on Learning with Bayesean Networks"
David Heckerman
http://psrg.lcs.mit.edu/6892/handouts/tutbayesheckerman.pdf
http://citeseer.ist.psu.edu/135897.html

"EM Optimization of Latent-Variable Density Models"
C. M. Bishop, M. Svensen, C. K. I. Williams
http://www.ncrg.aston.ac.uk/Papers/postscript/NCRG_96_011.ps.zip

"Empirical Entropy Manipulation for Real-World Problems",
Paul Viola, Nicol N. Schraudolph and Terrence J. Sejnowski
http://citeseer.ist.psu.edu/26138.html

"Adaptive Back-Propagation in On-Line learning of Multilayer Networks",
Ansgar H.L. West and David Saad
http://citeseer.ist.psu.edu/west96adaptive.html

]]> <![CDATA[video]]> http://amwp.wordpress.com/2007/05/21/video/ Mon, 21 May 2007 14:35:00 +0000 am http://amwp.wordpress.com/2007/05/21/video/ J.Cowan: "Spontaneous pattern formation in large scale brain activity:
what visual migraines and hallucinations tell us about the brain" (2006)
http://www.archive.org/details/redwood_center_2006_02_14_cowan
[Redwood Center for Theoretical Neuroscience]

]]> <![CDATA[lectures videos]]> http://amwp.wordpress.com/2007/05/08/videos/ Tue, 08 May 2007 20:00:00 +0000 am http://amwp.wordpress.com/2007/05/08/videos/ Brain, Consci., Cogn., Modelling

 

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"Les universaux de pensйe", Jean-Pierre Changeux
http://www.canalu.fr/canalu/chainev2/utls/programme/675628210_les_universaux_de_pensee

"Nombres et neurones", Alain Connes & Jean-Pierre Changeux
Director: Benoоt Jacquot, Country: France, Release: 1990, Runtime: 40min

"NeuroSciences et sociйtй" Jean-Pierre Changeux, 7 Dйcembre 2004
http://webcast.in2p3.fr/as/

Stanislas Dehaene "L'imagerie cйrйbrale et la transition dynamique du non-conscient au conscient"
http://webcast.in2p3.fr/physiqueetconscience/index.php?video=dehaene.ram

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"Panel Discussion: The Role and Future Prospects for Math/Computational Theories"
Redwood Neurosciences Institute (B.Olshausen, H.Barlow, T.Sejnowski)
41 min 27 sec - Mar 31, 2006
http://video.google.com/videoplay?docid=5154297266379993899
-- SEE ALSO: lectures of Olshausen, Barlow, Sejnowski, Wörgötter there!

"How Do We Predict the Future: Brains, Rewards and Addiction" (serotonine, dopamine)
T.Sejnowski, UCTV: UC San Diego
59 Min. 20 Sek. - 27.06.2005
http://video.google.com/videoplay?docid=1109575117793246083

"Brain, Mind and Consciousness" - Session_3 (J.Allman on ACC)
The Science Network
2 Std. 42 Min. 8 Sek. - 14.05.2005
thesciencenetwork.org
http://video.google.com/videoplay?docid=-4692065277230230087

"Cortical Dynamics of Working Memory"
Joaquin Fuster, UCLA
1 hr 13 min 4 sec
http://video.google.com/videoplay?docid=-3002336180397686566

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]]> <![CDATA[Gran Canaria]]> http://amwp.wordpress.com/2005/10/20/352/ Thu, 20 Oct 2005 09:57:00 +0000 am http://amwp.wordpress.com/2005/10/20/352/ Fifth International Conference on Engineering Computational Technology.

http://www.civil-comp.com/conf/cst2006.htm

]]> <![CDATA[ tags]]> http://amwp.wordpress.com/2005/09/13/tags/ Tue, 13 Sep 2005 03:15:00 +0000 am http://amwp.wordpress.com/2005/09/13/tags/ Categories ]]> <![CDATA[Some links from]]> http://amwp.wordpress.com/2005/07/20/some-links-from/ Wed, 20 Jul 2005 03:10:00 +0000 am http://amwp.wordpress.com/2005/07/20/some-links-from/ K.Sakai, O.Hikosaka, K.Nakamura "Emergence of rhythm during motor learning"
Trends in Cogn. Sci., Vol.8, I.12 , Dec.2004, pp. 547-553
http://dx.doi.org/10.1016/j.tics.2004.10.005

M.F. Rushworth et al., "Action sets and decisions in the medial frontal cortex",
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J. Exp. Psychol. Learn. Mem. Cogn. 28 (2002), pp. 445–457
http://www.people.virginia.edu/~js4fh/jackie/Publication/ShinIvry2002.pdf
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=12018497
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of movement sequences", J. Neurophysiol. 90 (2003), pp. 3725–3735
http://jn.physiology.org/cgi/content/abstract/90/6/3725
D. Bullock, "Adaptive neural models of queuing and timing in fluent action",
Trends Cogn. Sci. 8 (2004), pp. 426–433
http://dx.doi.org/10.1016/j.tics.2004.07.003
F. Gobet et al., "Chunking mechanisms in human learning",
Trends Cogn. Sci. 5 (2001), pp. 236–243
http://dx.doi.org/10.1016/S1364-6613(00)01662-4

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