Celebrating the 50th Anniversary of the Neurosciences Research Program: Neuroscience and Higher Brain Function: State of the Art

This Tuesday The Neurosciences Institute holds a workshop, titled "Neuroscience and Higher Brain Function: State of the Art". Here is the schedule: http://www.nsi.edu/nrp/index.html. See you tomorrow!

Celebrating the 50^th Anniversary of the Neurosciences Research Program

Neuroscience and Higher Brain Function: State of the Art
Tuesday, April 17, 2012
The Neurosciences Institute Auditorium
10640 John Jay Hopkins Drive, San Diego, CA 92121
8:30 am - 5:30 pm

Welcome and Introduction
8:30 am	Gerald Edelman
Cortex: A View from the Top Chairman: Gerald Edelman
8:40 am	Ranulfo Romo	Perceptual Decision Processes across Cortex
	Jon Kaas	Cortical Sensorimotor Networks
	Thomas Albright	On the Perception of Probable Things
Memory and Cognitive Control Chairman: Larry Squire
10:50 am	György Buzsaki	Neural Syntax
	John O'Keefe	Memory Systems of the Medial Temporal Lobes
	Mark D'Esposito	Modularity, Networks, and Cognitive Control
12:45 pm	Lunch
Value and Cognition Chairman: Einar Gall
1:45 pm	Michael Merzenich	"Cultural Neuroscience": How the World Changes Your Brain and Our Brains Change the World
	Joaquin Fuster	The Prefrontal Cortex: Executive, Controller, or Enabler of the Perception/Action Cycle?
	Yasushi Miyashita	Cognitive Memory and its Cellular/Network Machinery: How Global Brain-wide Networks Interact with Local Micro-circuits
Diagnosis and Repair Chairman: Gerald Edelman
3:55 pm	Eberhard Fetz	Bidirectional Interactions between the Brain and Implantable Computers
	Apostolos Georgopoulos	Prewhitening for Brain Discovery
Summary
5:20 pm	Gerald Edelman

Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs

Sometimes people asked me why I am interested in EEG/MEG source localization. In their eyes EEG/MEG source localization seems to be a direction with minor importance. However, when I researched in ICA with application to EEG/MEG data analysis before 2009, I realized the importance of EEG/MEG source localization, and this is why I started my research on sparse signal recovery in 2009.

I can spend more than 1000 words to detail the reasons. But the following paper well explained for me. Particularly, this nice paper implies the true value of EEG/MEG source localization study: the true value is not to pursuit higher spatial resolution, but serves as a crucial step for mining the brain connectivity. Here is the paper:

Satu Palva, J.Matias Palva, Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs, Trends in Cognitive Sciences, vol.16, no.4, 2012, pp.219-230

Abstract:

The systems-level neuronal mechanisms that coordinate temporally, anatomically and functionally distributed neuronal activity into coherent cognitive operations in the human brain have remained poorly understood. Synchronization of neuronal oscillations may regulate net- work communication and could thus serve as such a mechanism. Evidence for this hypothesis, however, was until recently sparse, as methodological challenges limit the investigation of interareal interactions with non- invasive magneto- and electroencephalography (M/EEG) recordings. Nevertheless, recent advances in M/EEG source reconstruction and clustering methods support complete phase-interaction mappings that are essential for uncovering the large-scale neuronal assemblies and their functional roles. These data show that synchroniza- tion is a robust and behaviorally significant phenomenon in task-relevant cortical networks and could hence bind distributed neuronal processing to coherent cognitive states.

Paper Club: Whatever Next? Predictive Brains, Situated agents and the future of cognitive science

Today 11 am at Ed's lab (3509 Mandler) we will discuss a very interesting and comprehensive paper:

Andy Clark (2012) "Whatever Next? Predictive Brains, Situated agents and the future of cognitive science", to appear in Behavioral and Brain Sciences

Here is the download link:  http://www.fil.lu.se/files/eventfile2811.pdf

Abstract: Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. The paper critically examines this ‘hierarchical prediction machine’ approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.

My comment: Although the paper does not involve any computational tools or specific computational frameworks, it in fact points out the incapability of ICA and sparse coding in the computational vision field. Also, I further understand that why deep belief networks and other hierarchical Bayesian models are so promising in the computational vision field.

My New and Permanent Homepage

Considering I will graduate and will leave UCSD, I made another homepage: https://sites.google.com/site/researchbyzhang/,  and this should be my permanent homepage no matter where I am. Welcome to visit my new homepage.

Presentation: COMPRESSED SENSING AND SPARSE SIGNAL RECOVERY BY SPARSE BAYESIAN LEARNING: MODELS, ALGORITHMS, AND APPLICATIONS

In this Thursday's Research Expo 2012 (April 12), I will present my previous and on-going work on sparse signal recovery/compressed sensing using sparse Bayesian learning (SBL).

Here is the abstract:

Compressed sensing / sparse signal recovery is a hot field in signal processing. Numerous algorithms have been proposed and have shown promising successes in applications. Among these algorithms, sparse Bayesian learning (SBL) has outstanding performance. In this presentation I will summarize our lab's recent work on SBL. I will present four new models that data-adaptively learn and exploit signals' temporal, spatial, spatiotemporal, and dynamic information. The derived algorithms from these models have shown the best, or at least top-tier, performance among existing compressed sensing algorithms in both computer simulations and practical applications (e.g. telemonitoring, biomarker selection in gene expression, source localization, earthquake detection, neuroimaging). Particularly, some of them have:

(1) solved the challenge of non-sparse physiological signals (e.g. fetal ECG contaminated by strong noise, EEG, EMG, etc) telemonitoring via wireless body-area networks with ultra-low power consumption, which was not solved before;  (this application will show SBL's ability to recover non-sparse signals with little distortion, using simple sparse binary matrices as its sensing matrices)
(2) achieved higher EEG source localization accuracy than other famous algorithms in more complicated environments (this application will show SBL's excellent ability under strong noise ( 0dB), highly coherent sensing matrix, and frequently changing signals);
(3) broke the record of predicting cognitive outcomes from neuroimaging measures in Alzheimer's disease in 2011 (showing SBL's ability to  deal with highly coherent sensing matrix);
(4) Brain-Computer Interface (solving the speed problem of SBL)
(5) obtained the best accuracy in earthquake detection in some common datasets;

Some of these work have been published or submitted to: IEEE Trans. on Signal Processing, IEEE Journal of Selected Topics in Signal Processing, Proceedings of the IEEE, IEEE Trans. on Biomedical Engineering, NeuroImage, CVPR 2012, and ICASSP 2010, 2011, 2012. Also, a US patent is pending.

Welcome to hear my presentation on the recent progress on various spatiotemporal SBL models!

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Photo: N. talangensis (taken by April 7)