Abstract: 

The space-time dynamics of interactions in neural systems are often described using terminology of information processing, or computation, in particular with reference to information being stored, transferred and modified in these systems. In this talk, we describe an information-theoretic framework -- information dynamics --  that we have used to quantify each of these operations on information, and their dynamics in space and time. Not only does this framework quantitatively align with natural qualitative descriptions of neural information processing, it provides multiple complementary perspectives on how, where and why a system is exhibiting complexity. We will review the application of this framework in computational neuroscience, describing what it can and indeed has revealed in this domain. First, we discuss examples of characterising behavioural regimes and responses in terms of information processing, including under different neural conditions and around critical states. Next, we show how the space-time dynamics of information storage, transfer and modification directly reveal how distributed computation is implemented in a system, highlighting information processing hot-spots and emergent computational structures, and providing evidence for conjectures on neural information processing such as predictive coding theory. Finally, via applications to several models of dynamical networks and human brain images, we demonstrate how information dynamics relates the structure of complex networks to their function, and how it can invert such analysis to infer structure from dynamics.

This event is organized by the CBMM Trainee Leadership Council.

Abstract: By a quirk of evolution, camouflaging octopus and cuttlefish report their visual perceptions by modulating their skin color and 3-d texture on time scales of seconds or minutes to match their surroundings (they are generative image modelers).  Their survival demands that predators perceive them as visual noise, whereas the survival of a predator demands that it detect them as signal, in a feedback loop that evolved on evolutionary time scales over millions of years (the generalized adversarial network).
 
Whereas the mechanical and physiological mechanisms of this camouflage have been studied intensively over the last few decades with steady progress, my research group seeks instead to elucidate the computation underlying it. Following the phenomenological tradition of Helmholtz, who discovered the RGB basis of human color perception over one hundred years before its physics and physiology emerged, we couple experimental, computational, and theoretical methods to characterize the i/o transfer function of the eye to skin mapping, in the first instance by identifying its fixed points.

Abstract:

Rapid advances in convolutional networks and other machine learning techniques, in combination with large data bases and the relentless hardware advances due to Moore’s Law, have brought us closer to the day when we will be able to have extended conversations with programmable systems, such as advanced versions of Alexa or Siri, without being able to tell their siren voices from those of humans. This raises the questions to which extent systems that can pass a non-trivial version of the Turing test will also feel anything, that is, be conscious. I shall argue against this possibility for three reasons. Firstly, intelligent behavior, including speech, is conceptually radically different from subjective experience. Secondly, clinical case studies demonstrate that the neural basis of intelligence, self-monitoring, insights and other higher-order cognitive processes in the frontal regions of neocortex are distinct from the neural correlates of conscious experience in the posterior cortex. Thirdly, Integrated Information Theory (IIT), a fundamental theory of consciousness, predicts that conventional computers, even though they will be able, at least in principle, to simulate human-level behavior, will not experience anything. Building human-level consciousness requires neuromorphic computer architectures.

Speaker Bio: 

Christof Koch is an American neuroscientist best known for his studies and writings exploring the basis of consciousness. Trained as a physicist, Koch was for 27 years a professor of biology and engineering at the California Institute of Technology. He is now Chief Scientist and President of the Allen Institute for Brain Science in Seattle, leading a ten year, large-scale, high through-put effort to build brain observatories to map, analyze and understand the mouse and human cerebral cortex.

On a quest to understand the physical roots of consciousness, he published his first paper on the neural correlates of consciousness with the molecular biologist Francis Crick more than a quarter of a century ago.

He is a frequent public speaker and writes a regular column for Scientific American. Christof is a vegetarian and cyclist who lives in Seattle and loves big dogs, climbing and rowing.

Abstract: Google Accelerated Sciences is a translational research team that brings Google's technological expertise to the scientific community.  Recent advances in machine learning have delivered incredible results in consumer applications (e.g. photo recognition, language translation), and is now beginning to play an important role in life sciences.  Taking examples from active collaborations in the biochemical, biological, and biomedical fields, I will focus on how our team transforms science problems into data problems and applies Google's scaled computation, data-driven engineering, and machine learning to accelerate discovery.

Speaker Bio: Philip Nelson is a Director of Engineering in Google Research. He joined Google in 2008 and was previously responsible for a range of Google applications and geo services. In 2013, he helped found and currently leads the Google Accelerated Science team that collaborates with academic and commercial scientists to apply Google's knowledge and experience running complex algorithms over large data sets to important scientific problems. Philip graduated from MIT in 1985 where he did award-winning research on hip prosthetics at Harvard Medical School. Before Google, Philip helped found and lead several Silicon Valley start ups in search (Verity), optimization (Impresse), and genome sequencing (Complete Genomics) and was also an Entrepreneur in Residence at Accel Partners.