The Function of Synchrony Marieke Rohde Reading Group DyStURB (Dynamical Structures to Understand Real Brains)

Slides:



Advertisements
Similar presentations
Read this article for Friday next week [1]Chelazzi L, Miller EK, Duncan J, Desimone R. A neural basis for visual search in inferior temporal cortex. Nature.
Advertisements

Rhythms in the Nervous System : Synchronization and Beyond Rhythms in the nervous system are classified by frequency. Alpha 8-12 Hz Beta Gamma
Chrisantha Fernando & Sampsa Sojakka
Driving fast-spiking cells induces gamma rhythm and controls sensory responses Driving fast-spiking cells induces gamma rhythm and controls sensory responses.
Neural Network Models in Vision Peter Andras
CNTRICS April 2010 Center-surround: Adaptation to context in perception Robert Shapley Center for Neural Science New York University.
Neural Network of the Cerebellum: Temporal Discrimination and the Timing of Responses Michael D. Mauk Dean V. Buonomano.
Purpose The aim of this project was to investigate receptive fields on a neural network to compare a computational model to the actual cortical-level auditory.
Read this article for Friday next week [1]Chelazzi L, Miller EK, Duncan J, Desimone R. A neural basis for visual search in inferior temporal cortex. Nature.
Electrophysiology. Electroencephalography Electrical potential is usually measured at many sites on the head surface More is sometimes better.
Spike Train Statistics Sabri IPM. Review of spike train  Extracting information from spike trains  Noisy environment:  in vitro  in vivo  measurement.
Neurophysics Part 1: Neural encoding and decoding (Ch 1-4) Stimulus to response (1-2) Response to stimulus, information in spikes (3-4) Part 2: Neurons.
Impact of Correlated inputs on Spiking Neural Models Baktash Babadi Baktash Babadi School of Cognitive Sciences PM, Tehran, Iran PM, Tehran, Iran.
Functional Link Network. Support Vector Machines.
Biological Modeling of Neural Networks: Week 11 – Continuum models: Cortical fields and perception Wulfram Gerstner EPFL, Lausanne, Switzerland 11.1 Transients.
Brain Rhythms and Short-Term Memory Earl K. Miller The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts.
Some concepts from Cognitive Psychology to review: Shadowing Visual Search Cue-target Paradigm Hint: you’ll find these in Chapter 12.
1 The Neural Basis of Temporal Processing Michael D. Mauk Department of Neurobiology and Anatomy University of Texas Houston Medical School Slideshow by.
How facilitation influences an attractor model of decision making Larissa Albantakis.
Laurent Itti: CS599 – Computational Architectures in Biological Vision, USC Lecture 7: Coding and Representation 1 Computational Architectures in.
Connected Populations: oscillations, competition and spatial continuum (field equations) Lecture 12 Course: Neural Networks and Biological Modeling Wulfram.
Zicong Zhang Authors Wendy A. Suzuki Professor of Neural Science and Psychology, New York University Research interest: Organization of memory.
Functional Brain Signal Processing: Current Trends and Future Directions Kaushik Majumdar Indian Statistical Institute Bangalore Center
Synchronization in Epilepsy and Schizophrenia Kaushik Majumdar Indian Statistical Institute 8th Mile, Mysore Road Bangalore
Michael P. Kilgard Sensory Experience and Cortical Plasticity University of Texas at Dallas.
Changju Lee Visual System Neural Network Lab. Department of Bio and Brain Engineering.
Methods Neural network Neural networks mimic biological processing by joining layers of artificial neurons in a meaningful way. The neural network employed.
Sparsely Synchronized Brain Rhythms in A Small-World Neural Network W. Lim (DNUE) and S.-Y. KIM (LABASIS)
Biological Cybernetics By: Jay Barra Sean Cain. Biological Cybernetics An interdisciplinary medium for experimental, theoretical and application- oriented.
Correlation-Induced Oscillations in Spatio-Temporal Excitable Systems Andre Longtin Physics Department, University of Ottawa Ottawa, Canada.
Neural dynamics of in vitro cortical networks reflects experienced temporal patterns Hope A Johnson, Anubhuthi Goel & Dean V Buonomano NATURE NEUROSCIENCE,
Chapter 7. Network models Firing rate model for neuron as a simplification for network analysis Neural coordinate transformation as an example of feed-forward.
Multiple attractors and transient synchrony in a model for an insect's antennal lobe Joint work with B. Smith, W. Just and S. Ahn.
$ recognition & localization of predators & prey $ feature analyzers in the brain $ from recognition to response $ summary PART 2: SENSORY WORLDS #10:
TEMPLATE DESIGN © In analyzing the trajectory as time passes, I find that: The trajectory is trying to follow the moving.
The Time Dimension for Scene Analysis DeLiang Wang Perception & Neurodynamics Lab The Ohio State University, USA.
Neural Networks with Short-Term Synaptic Dynamics (Leiden, May ) Misha Tsodyks, Weizmann Institute Mathematical Models of Short-Term Synaptic plasticity.
Biological Neural Network & Nonlinear Dynamics Biological Neural Network Similar Neural Network to Real Neural Networks Membrane Potential Potential of.
Rhythms and Cognition: Creation and Coordination of Cell Assemblies Nancy Kopell Center for BioDynamics Boston University.
Effect of Small-World Connectivity on Sparsely Synchronized Cortical Rhythms W. Lim (DNUE) and S.-Y. KIM (LABASIS)  Fast Sparsely Synchronized Brain Rhythms.
Activity Dependent Conductances: An “Emergent” Separation of Time-Scales David McLaughlin Courant Institute & Center for Neural Science New York University.
Visual Computation I. Physiological Foundations
Review – Objectives Transitioning 4-5 Spikes can be detected from many neurons near the electrode tip. What are some ways to determine which spikes belong.
An Oscillatory Correlation Approach to Scene Segmentation DeLiang Wang The Ohio State University.
Ch 9. Rhythms and Synchrony 9.7 Adaptive Cooperative Systems, Martin Beckerman, Summarized by M.-O. Heo Biointelligence Laboratory, Seoul National.
Keeping the neurons cool Homeostatic Plasticity Processes in the Brain.
Speech Segregation Based on Oscillatory Correlation DeLiang Wang The Ohio State University.
Dynamic Causal Model for evoked responses in MEG/EEG Rosalyn Moran.
$ studying barn owls in the laboratory $ sound intensity cues $ sound timing cues $ neural pathways for sound location $ auditory space $ interaural time.
The role of synchronous gamma-band activity in schizophrenia Jakramate 2009 / 01 / 14.
Network Models (2) LECTURE 7. I.Introduction − Basic concepts of neural networks II.Realistic neural networks − Homogeneous excitatory and inhibitory.
Biological Modeling of Neural Networks: Week 10 – Neuronal Populations Wulfram Gerstner EPFL, Lausanne, Switzerland 10.1 Cortical Populations - columns.
Date of download: 6/28/2016 Copyright © 2016 American Medical Association. All rights reserved. From: Teamwork Matters: Coordinated Neuronal Activity in.
Ch. 13 A face in the crowd: which groups of neurons process face stimuli, and how do they interact? KARI L. HOFFMANN 2009/1/13 BI, Population Coding Seminar.
The Neural Code Baktash Babadi SCS, IPM Fall 2004.
EEG Definitions EEG1: electroencephalogram—i.e., the “data”
Brain Electrophysiological Signal Processing: Postprocessing
Neural Oscillations Continued
? Dynamical properties of simulated MEG/EEG using a neural mass model
Andy Dykstra HST.722 November 1, 2007
Presentation of Article:
Synchrony & Perception
Volume 40, Issue 6, Pages (December 2003)
Ben Scholl, Xiang Gao, Michael Wehr  Neuron 
Woochang Lim1 and Sang-Yoon Kim2
Thomas Akam, Dimitri M. Kullmann  Neuron 
Yann Zerlaut, Alain Destexhe  Neuron 
Information Processing by Neuronal Populations Chapter 5 Measuring distributed properties of neural representations beyond the decoding of local variables:
Stefano Panzeri, Jakob H. Macke, Joachim Gross, Christoph Kayser 
Sparsely Synchronized Brain Rhythm in A Small-World Neural Network
Presentation transcript:

The Function of Synchrony Marieke Rohde Reading Group DyStURB (Dynamical Structures to Understand Real Brains)

Structure 1.Sound recognition by transient synchrony. (Hopfield & Brody) 2.Long distance synchronisation in Human subjects (Rodriguez et. Al.) 3.Discuss!

1.) Hopfield, Brody: What is a moment? (Puzzle and Answer)

Mus Silicium Short time integration in an artificial organism Biologically plausible model, spiking neurons. Auditory task: one syllable recognition – short time integration required to "represent" the world. Mastered robustly

Mus Silicium - Anatomy Layer 4: –50% inhibitory, 50% excitatory –Lots of cells and connections no delays, no plasticity Sensors: cells are frequency tuned and respond to –Onsets –Offsets –Peaks Transient decay of neural activity at different decay rates.

Mus Silicium - Anatomy The alpha and beta neurons from „cortical layer 4“ exhibit the same properties as the sensory neurons!

Mus Silicium: Responses Gamma cells: highly specific to learned syllable.

Mus Silicium: The Solution General Principle: Transient synchrony of APs to „signal“ recognition Representation of time of a stimulus by different decay rates spatiotemporal patterns: Convergence of firing rate of decaying currents. Same rate neurons (coupled oscillators) tend to synchronise. (set weights accordingly) Detection by cell with small time constant Invariant to time-warping (rescaling in time), delays and salience

Mus Silicium: The Solution 800 lines (different stimuli and decay rates) from area A project on an excitatory and an inhibitory cell Training = find set of coinciding neurons on pattern and mutually couple them (excitatory and inhibitory) Balance between excitation and inhibition, to assure network input current from outside. Connect whole set to a gamma neuron, to yield a reaction.

Mus Silicium Extensions: –reactivation of sensors? (several, probablistic activation) –Negative evidence. Destroy synchrony/detection.  Robustness against noise –Multiple patterns: Phase transition n  infty to general synchrony Structure, not weights. Several structures conceivable Biological plausibility. Conclusion: –A „Many are now equal“ operator. –Model spiking networks if you want to explain the brain! How could you have guessed it?

2.) Rodriguez et.al.: Perception's shadow: longdistance synchronization of human brain activity

Long Distance Synchrony Hz oscillations (gamma) synchronise during a cognitive act. (EEG MEG measurements) Task: Recognition of a degraded stimulus (Mooney face)

Long Distance Synchrony: Methods 1.Detect induced gamma response: "wigner ville time frequency transforms“ of single trials and average. first peak is known (much stronger in perception condition) second new, practically the same for both conditions. 1.Phase synchrony: –the phase synchrony profile is very different from the gamma activity profile –baseline: shuffled data. –no perception remains close to baseline. –perception: synchronisation, desynchronisation, synchronisation (zero centered distribution of phase lags).

Long Distance Synchrony: Conclusions biological significance for cogntion confirmed. (refutation to different criticisms) High level, rather than low local feature binding New finding: desynchronisation to prepare for next synchronisation (destroy old pattern). gamma activity != synchrony.

Discussion Differences: –Local vs. Global (+ role of delays) –Detectors vs. Unknown function. –Low level vs. High level What methods to detect it in organisms? –Phase lag: 0 or different? –Time spans vs. every spike. Synchrony - Asynchrony What function could synchrony have? –Attractive state (type of population code) –Internal clock