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Dharmendra S. Modha

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Robert Knight

October 8, 2010 By dmodha

Today, we had a genuinely great talk by Professor Bob Knight.

TITLE:  Neural Oscillations Track Behavior in the Human Cortex

ABSTRACT: Since the discovery of the EEG in 1920’s, neurophysiological dogma for the ensuing 80 years stated that the human cortex did not generate reliable rhythms above 50-60 Hz. However, findings over the last decade  reliably report neural activity up to 250 Hz in the human cortex. This high frequency activity is the key neural response measuring cortical activation in humans. We record the human electrocorticogram (ECoG) from subdural electrodes implanted directly on the cortex of neurosurgical patients. We have observed that every cognitive process examined including language, attention, memory and motor control generates high frequency oscillatory activity in the range of 70-250 Hz (high gamma, HG). Importantly, the HG band of the human ECoG has the most precise spatial localization and task specificity of any frequency observed. For instance, during linguistic processing, HG precisely tracks the spatio-temporal evolution from comprehension in posterior temporal areas to production structures in the left frontal region. HG a precisely tracks the time course of the behavior needed to comprehend the word, select a noun and articulate a response all occurring within a second. Similar findings of HG activity are observed for working memory, contextual processing and a host of other human behaviors.  Importantly the HG response can be reliably extracted at the single trial level providing a powerful tool for studying the physiology of human behavior. HG is also phase locked to the trough of theta rhythms in the human neocortex parallel to findings of HG-theta coupling  observed in animal hippocampus and cortex. HG-theta coupling occurs in a task specific manner with different cognitive tasks eliciting unique distributed spatial patterns of HG-theta coupling.  These results indicate that transient coupling between low- and high-frequency brain rhythms provide a mechanism for effective communication in distributed neural networks engaged during cognitive processing in humans. Taken together the results indicate that HG activity provides a powerful new tool for understanding the real-time cortical dynamics subserving cognition in humans.

BIO: Dr. Knight received his BS in Physics from the Illinois Institute of Technology in 1970 and his MD from Northwestern University Medical School in 1974.  He did Neurology residency at the University of California at San Diego followed by post-doctoral work at the Salk Institute for Biological Studies in the area of human neurophysiology. He was a faculty member in the Department of Neurology at the University of California at Davis School of Medicine from 1980-1998. In 1998 Dr. Knight moved to UC Berkeley to lead a program in human neuroscience. He is currently the Evan Rauch Professor of Neuroscience and Director of the Helen Wills Neuroscience Institute at UC Berkeley.  His laboratory utilizes electrophysiological and MRI techniques in neurological and neurosurgical patients to delineate the role of prefrontal cortex in human cognitive and social behavior. His laboratory also records electrocorticographic activity  directly from the human cortex from neurosurgical patients in an effort  to delineate the cortical mechanisms supporting behavior. His laboratory is also involved in developing neural prosthesis devices for motor and language restoration in patients with stroke or spinal cord injury using electrocorticographic recordings. Dr. Knight received the Jacob Javits Award from the National Institute of Neurological Disorders and Stroke for distinguished contributions to understanding neurological disorders, the IBM Cognitive Computing Award and the 2008 German Humboldt Prize in Neurobiology.

Filed Under: Brain-inspired Computing, Interesting People

Manfred Warmuth

September 30, 2010 By dmodha

Today, I had the good fortune to spend time with Manfred Warmuth who is a leading scientist in Computational Learning Theory.

ABSTRACT: Multiplicative updates multiply the parameters by nonnegative factors. These updates are motivated by a Maximum Entropy Principle and they are prevalent in evolutionary processes where the parameters are for example concentrations of species and the factors are survival rates. The simplest such update is Bayes rule and we give an in vitro selection algorithm for RNA strands that implements this rule in the test tube where each RNA strand represents a different model. In one liter of the RNA soup there are approximately 10^20 different strands and therefore this is a rather high-dimensional implementation of Bayes rule.

We investigate multiplicative updates for the purpose of learning online while processing a stream of examples. The “blessing” of these updates is that they learn very fast because the good parameters grow exponentially. However their “curse” is that they learn too fast and wipe out parameters too quickly. We describe a number of methods developed in the realm of online learning that ameliorate the curse of these updates. The methods make the algorithm robust against data that changes over time and prevent the currently good parameters from taking over. We also discuss how the curse is circumvented by nature. Some of nature’s methods parallel the ones developed in Machine Learning, but nature also has some additional tricks.

Filed Under: Brain-inspired Computing, Interesting People

The thermodynamic temperature of a rhythmic spiking network

September 29, 2010 By dmodha

Paul Merolla, Tristan Ursell, John Arthur published today on arXiv.org a beautiful and unconventional paper that applies concepts from statistical mechanics to spiking neurons:

ABSTRACT: Artificial neural networks built from two-state neurons are powerful computational substrates, whose computational ability is well understood by analogy with statistical mechanics. In this work, we introduce similar analogies in the context of spiking neurons in a fixed time window, where excitatory and inhibitory inputs drawn from a Poisson distribution play the role of temperature. For single neurons with a "bandgap" between their inputs and the spike threshold, this temperature allows for stochastic spiking. By imposing a global inhibitory rhythm over the fixed time windows, we connect neurons into a network that exhibits synchronous, clock-like updating akin to neural networks. We implement a single-layer Boltzmann machine without learning to demonstrate our model.

Filed Under: Brain-inspired Computing

The Human Connectome Project

September 28, 2010 By dmodha

The National Institutes of Health awarded grants totaling $40 million over five years to map the human brain’s connections in high resolution. It is "an ambitious effort to map the neural pathways that underlie human brain function". Here is the website with more information.

Filed Under: Brain-inspired Computing

Horst Simon Named Deputy Director for Berkeley Lab

September 17, 2010 By dmodha

Dr. Horst Simon, our collaborator for "The Cat is Out of the Bag", was named as named Deputy Director of Lawrence Berkeley National Laboratory. See the news here. In related news, Horst Simon will also Chair the Gordon Bell Prize Award Committee.  

Filed Under: Brain-inspired Computing

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