In memoriam: Naftali Tishby Lab

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Prof. Naftali Tishby passes away at the age of 68
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Ruth & Stan Flinkman Family Endowment Fund Chair in Brain Research

Tishby's Lab

NEWS

The 2019 IBT Award in Mathematical Neuroscience – THANKS!

Our Information Bottleneck Theory of Deep Learning has recently been noticed – at last!

See the Quanta-Magazine article on our work and my June 2017 Berlin Deep Learning Workshop talk which triggered it.

A longer talk given at Yandex, Moscow, October 10, 2017.

Recent & popular talks

ACDL, Siena, July 2019
MPI, Gottingen, July 2019
ISIT, Paris, July 2019
CBMM, Italy, June 2019
Gatsby triCenter meeting, London, June 2019
IPAM, Geometry of Big Data, UCLA, May 2019
Columbia University Economics, May 2019
IMVC, Tel-Aviv, April 2019
CERN, Geneva, ML for HEP, April 2019
BrainTech, Tel-Aviv, March 2019
ICERM, Brown University, February 2019
Deep Learning & the Brain, ELSC, Jerusalem, January 2019
Directions in Theoretical Physics, Edinburgh, January 2019
Statistical Physics and Machine Learning: A 30 Year Perspectives, APS Physics Next Workshop, October 2018
Part I of a Mini-Course on The Information Theory of Deep Learning, ICTP, Trieste, November 2018
Part II of a Mini-Course on The Information Theory of Deep Learning, ICTP, Trieste, November 2018
Part III of a Mini-Course on The Information Theory of Deep Learning, ICTP, Trieste, November 2018
“What Makes Us Human: From Genes to Machines”, June 6, 2018, Jerusalem.
A Talk at the Israel Academy of Science, June 6, 2018, Jerusalem
Interview on the future of Artificial Intelligence, The AI Summit, Berlin, June 2018.
Interview at SISSA , Trieste, Italy, before my physics colloquium, May 2018.
Perimeter Institute Physics Colloquium, Waterloo, Canada, April 2018.
S tanford CSE Colloquium, April 4, 2018.
Simons Institute, Berkeley, Public talk, April 9, 2018.
Simons Institute, Berkeley, “Brain & Computation program”, March 2018.
Human creativity, Music & Deep Learning, BIU, Nov. 29, 2017.
The Synergy between Information and Control
שבוע אמנות ומוח 2016 דוד גרוסמן ופרופ’ נפתלי תשבי בדיאלוג על מוח, מחשבה, דמיון ויצירה

My Current Lab:

Selected Research Projects

We work at the interface between computer science, physics, and biology which provides some of the most challenging problems in today’s science and technology. We focus on organizing computational principles that govern information processing in biology, at all levels. To this end, we employ and develop methods that stem from statistical physics, information theory and computational learning theory, to analyze biological data and develop biologically inspired algorithms that can account for the observed performance of biological systems. We hope to find simple yet powerful computational mechanisms that may characterize evolved and adaptive systems, from the molecular level to the whole computational brain and interacting populations. An example is the Information Bottleneck method that provides a general principle for extracting relevant structure in multivariate data, characterizes complex processes, and suggests a general approach for understanding optimal adaptive biological behavior

Jonathan Rubin, Nahum Ulinovsky, Israel Nelken and Naftali Tishby

The Representation of Prediction Error in Auditory Cortex

PLoS Comput Biol 12(8) e1005058 (2016)

Nori Jacoby, Naftali Tishby, Bruno H. Repp, Merav Ahissar and Peter E. Keller

Parameter Estimation of Linear Sensorimotor Synchronization Models: Phase Correction, Period Correction, and Ensemble Synchronization

Timing & Time Perception, Volume 3: Issue 1-2 (2015)

Nori Jacoby, Peter E. Keller, Bruno H. Repp, Naftali Tishby, Merav Ahissar

Lower bound on the accuracy of parameter estimation methods for linear sensorimotor synchronization models : quantifying the problem and suggesting a solution

Timing & Time Perception, Volume 3: Issue 1-2 (2015)

Nori Jacoby, Naftali Tishby, and Dmitri Tymoczko

An Information Theoretic Approach to Chord Categorization and Functional Harmony

Journal of New Music Research Volume 44, 2015 - Issue 3 (2015)

S. Sabato, N. Srebro and N. Tishby

Distribution-Dependent Sample Complexity of Large Margin Learning

Journal of Machine Learning Research, 14(Jul):2119-2149 (2013)

Amir Globerson, Eran Stark, Eilon Vaadia, and Naftali Tishby

The minimum information principle and its application to neural code analysis

PNAS March 3, 2009 106 (9) 3490-3495; (2009)

Chechik G, Anderson MJ, Bar-Yosef O, Young ED, Tishby N, Nelken I.

Reduction of information redundancy in the ascending auditory pathway

Neuron. 2006 Aug 3;51(3):359-68. (2006)

Israel Nelken, Nachum Ulanovsky, Liora Las, Omer Bar-Yosef, Michael Anderson, Gal Chechik, Naftali Tishby, Eric D. Young

Transformation of stimulus representation in the ascending auditory system

Auditory Signal Processing pp 264-273 (2003)

Schneidmann E., Segev I., and Tishby N.

Information capacity and robustness of stochastic neuron models

Advances in neural information processing systems · February 2000 (2000)

Abeles M, Bergman H, Gat I, Meilijson I, Seidemann E, Tishby N, Vaadia E.

Cortical activity flips among quasi-stationary states

Proc Natl Acad Sci U S A. 1995 Sep 12;92(19):8616-20. (1995)

Seung HS, Sompolinsky H, Tishby N.

Statistical mechanics of learning from examples

Phys. Rev. A 45, 6056 – Published 1 April 1992 (1992)

H. Sompolinsky and N. Tishby

Learning in a Two-Layer Neural Network of Edge Detectors

Europhysics Letters 13, 567 (1990)

H. Sompolinsky, N. Tishby, and H. S. Seung

Learning from examples in large neural networks

Phys. Rev. Lett. 65, 1683 – Published 24 September 1990 (1990)