~~ (The talk will be given in English)

Speaker:   Prof. Ron Meir
                        Department of Electrical Engineering, Technion

Monday, May 16th, 2016
15:00 - 16:00
Room 011, Kitot Bldg., Faculty of Engineering

Control Theoretic Challenges in Theoretical Neuroscience: Combining Estimation and Control

Abstract
Biology discovered feedback and control long before the first control engineers appeared on the planet, and developed sophisticated control policies operating at multiple levels from molecules to cells to organisms to populations. The vast multi-scale complexity of biological systems, the highly effective control solutions provided in biology, and the limited, yet sophisticated, toolkit available to control engineers, suggests that there is much space for interaction between biological control and engineering, where both sides stand to gain. In this talk I will focus on the interaction of sensory adaptation and control in the context of a simple partially observable sensorimotor task. I will describe an approximate analytic approach to the intractable estimation/control problem, and will show that it suggests an optimal control based explanation of observed biological phenomena. I will conclude with some of the many open problems facing the reverse engineering of biological control systems.

Short Bio: 
Ron Meir is a Professor in the department of Electrical Engineering at the Technion. He completed his PhD in Physics at the Weizmann Institute in Statistical Physics, and worked for many years in the field of Machine Learning. One of his main current interests is in control and learning in natural and artificial systems.

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Speaker: Gil Sadeh
M.Sc. student under the supervision of Prof. Lior Wolf and Dr. Benny Applebaum 

Wednesday, May 18th, 2016 at 15:30
Room 011, Kitot Bldg., Faculty of Engineering

RNN Fisher Vectors for action recognition
Abstract

Recently, Recurrent Neural Networks (RNNs) have had considerable success in classifying and predicting sequences. Additionally, the Fisher Vector (FV) encoding has been widely used for pooling local features. We present the RNN-FV, which is a novel pooling method designed especially for sequential features. The methodology we use is based on FVs, where the RNNs are the generative probabilistic models, instead of the commonly used Gaussian Mixture Model (GMM), and the partial derivatives are computed using backpropagation. This proposed method is applied on sequential feature representation of videos, to achieve a new, fixed-length, discriminative video representation. We also explore different sequential feature representations of videos on which we apply our proposed method. Using our new RNN-FV based video representations, state of the art results are obtained in the task of video action recognition on two challenging datasets, UCF101 and HMDB51. We also demonstrate how to exploit the fact that the RNN is trained in an unsupervised manner in terms of the action labels, and show that training the RNN on one dataset and testing on another does not reduce performance significantly, and state-of-the-art results are achieved while using this transfer learning approach as well. We also show another surprising transfer learning result, from the task of image annotation to the task of video action recognition, which additionally improved our results.

~~(The talk will be given in English)

Speaker:   Dr. Yonatan Savir
                       Faculty of Medicine, Technion

Sunday, May 8th, 2016
15:00 - 16:00
Room 011, Kitot Bldg., Faculty of Engineering

Yeast response to multiple carbon sources: a case study of combinatorial signal integration

Abstract
A major determinant of the fitness of biological systems is their ability to integrate multiple cues from the environment and coordinate their metabolism and regulatory networks accordingly. While much is known about the response to a single stimulus, our understating of combinatorial integration of multiple inputs is still limited. As a model system, we studied how yeast responds to hundreds of mixtures of preferred carbon source, glucose, and a less preferred one, galactose. Many of the components of this response, known as catabolite repression, are conserved from yeast to human. We found that, in contrast to the textbook view, instead of simply inhibiting galactose utilization when glucose is above a threshold concentration, individual cells respond to the ratio of glucose and galactose, and based on this ratio determine whether to induce genes involved in galactose metabolism. We investigate the genetic architectures that can result in a ratio sensing and how these architectures provide a fitness advantage which could have shaped the evolution of this property.

Bio: Dr. Yonatan Savir received his Bachelor’s degree from the Technion in Electrical Engineering and Physics and his PhD from the Dept. of physics of Complex Systems at the Weizmann Institute. He did his postdoctoral training at the Dept. of Systems Biology at Harvard Medical School. Dr. Savir joined the Faculty of Medicine at the Technion as a principal investigator in October 2015. His lab focuses on studying, both experimentally and theoretically, the signal processing that links nutrient sensing, uptake, growth rate and understating its system level failure in disease and in aged cells.