Zoltán Somogyvári Hungarian Academy of Sciences, KFKI Research Institute for Particle and Nuclear Physics Department of Biophysics A model-based approach.

Slides:



Advertisements
Similar presentations
Introduction to Neural Networks
Advertisements

Rhythms in the Nervous System : Synchronization and Beyond Rhythms in the nervous system are classified by frequency. Alpha 8-12 Hz Beta Gamma
Chapter 10 Stability Analysis and Controller Tuning
The abrupt transition from theta to hyper- excitable spiking activity in stellate cells from layer II of the medial entorhinal cortex Horacio G. Rotstein.
Lecture 12: olfaction: the insect antennal lobe References: H C Mulvad, thesis ( Ch 2http://
A model for spatio-temporal odor representation in the locust antennal lobe Experimental results (in vivo recordings from locust) Model of the antennal.
Action Potentials and Limit Cycles Computational Neuroeconomics and Neuroscience Spring 2011 Session 8 on , presented by Falk Lieder.
WINNERLESS COMPETITION PRINCIPLE IN NEUROSCIENCE Mikhail Rabinovich INLS University of California, San Diego ’
Synchrony in Neural Systems: a very brief, biased, basic view Tim Lewis UC Davis NIMBIOS Workshop on Synchrony April 11, 2011.
III-28 [122] Spike Pattern Distributions in Model Cortical Networks Joanna Tyrcha, Stockholm University, Stockholm; John Hertz, Nordita, Stockholm/Copenhagen.
Learning crossmodal spatial transformations through STDP Gerhard Neumann Seminar B, SS 06.
Why are cortical spike trains irregular? How Arun P Sripati & Kenneth O Johnson Johns Hopkins University.
Part II: Population Models BOOK: Spiking Neuron Models, W. Gerstner and W. Kistler Cambridge University Press, 2002 Chapters 6-9 Laboratory of Computational.
Introduction to Mathematical Methods in Neurobiology: Dynamical Systems Oren Shriki 2009 Modeling Conductance-Based Networks by Rate Models 1.
Biological Modeling of Neural Networks: Week 11 – Continuum models: Cortical fields and perception Wulfram Gerstner EPFL, Lausanne, Switzerland 11.1 Transients.
The Decisive Commanding Neural Network In the Parietal Cortex By Hsiu-Ming Chang ( 張修明 )
How does the mind process all the information it receives?
How facilitation influences an attractor model of decision making Larissa Albantakis.
Connected Populations: oscillations, competition and spatial continuum (field equations) Lecture 12 Course: Neural Networks and Biological Modeling Wulfram.
Vrije Universiteit Amsterdam Computational Analysis of Spatiotemporal Patterns of Activity in Neuronal Networks Arjen van Ooyen (EN) Arjen Brussaard (EN)
Basic Mechanisms of Seizure Generation John G.R. Jefferys Marom BiksonPremysl Jiruska John FoxMartin Vreugdenhil Jackie DeansWei-Chih Chang Joseph CsicsvariXiaoli.
Introduction to Mathematical Methods in Neurobiology: Dynamical Systems Oren Shriki 2009 Modeling Conductance-Based Networks by Rate Models 1.
Epilepsy Lecture Neuro Course 4th year. Objectives – To Review: What the term epilepsy means Basic mechanisms of epilepsy How seizures and epilepsies.
Lecture 10: Mean Field theory with fluctuations and correlations Reference: A Lerchner et al, Response Variability in Balanced Cortical Networks, q-bio.NC/ ,
Cognition, Brain and Consciousness: An Introduction to Cognitive Neuroscience Edited by Bernard J. Baars and Nicole M. Gage 2007 Academic Press Chapter.
1 Dynamical System in Neuroscience: The Geometry of Excitability and Bursting پيمان گيفانی.
Correlation-Induced Oscillations in Spatio-Temporal Excitable Systems Andre Longtin Physics Department, University of Ottawa Ottawa, Canada.
John Wordsworth, Peter Ashwin, Gabor Orosz, Stuart Townley Mathematics Research Institute University of Exeter.
Lecture 9: Introduction to Neural Networks Refs: Dayan & Abbott, Ch 7 (Gerstner and Kistler, Chs 6, 7) D Amit & N Brunel, Cerebral Cortex 7, (1997)
Sensory Physiology. Concepts To Understand Receptor Potential Amplitude Coding Frequency Coding Activation/Inactivation Neural Adaptation Synaptic Depression.
The Function of Synchrony Marieke Rohde Reading Group DyStURB (Dynamical Structures to Understand Real Brains)
Neural Networks with Short-Term Synaptic Dynamics (Leiden, May ) Misha Tsodyks, Weizmann Institute Mathematical Models of Short-Term Synaptic plasticity.
Inverse solutions for localization of single cell currents based on extracellular measurements Zoltán Somogyvári 1, István Ulbert 2, Péter Érdi 1,3 1 KFKI.
Activity Dependent Conductances: An “Emergent” Separation of Time-Scales David McLaughlin Courant Institute & Center for Neural Science New York University.
Model-based learning: Theory and an application to sequence learning P.O. Box 49, 1525, Budapest, Hungary Zoltán Somogyvári.
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.
Mean Field Theories in Neuroscience B. Cessac, Neuromathcomp, INRIA.
Information encoding and processing via spatio-temporal spike patterns in cortical networks Misha Tsodyks, Dept of Neurobiology, Weizmann Institute, Rehovot,
Dynamics of Perceptual Bistability J Rinzel, NYU w/ N Rubin, A Shpiro, R Curtu, R Moreno Alternations in perception of ambiguous stimulus – irregular…
Ch 9. Rhythms and Synchrony 9.7 Adaptive Cooperative Systems, Martin Beckerman, Summarized by M.-O. Heo Biointelligence Laboratory, Seoul National.
Dynamic Causal Model for evoked responses in MEG/EEG Rosalyn Moran.
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.
Where are we? What’s left? HW 7 due on Wednesday Finish learning this week. Exam #4 next Monday Final Exam is a take-home handed out next Friday in class.
Neural Oscillations Continued
Dynamic Causal Model for evoked responses in M/EEG Rosalyn Moran.
42.13 Spike Pattern Distributions for Model Cortical Networks P-8
Computational models of epilepsy
Volume 36, Issue 5, Pages (December 2002)
OCNC Statistical Approach to Neural Learning and Population Coding ---- Introduction to Mathematical.
Volume 30, Issue 2, Pages (May 2001)
Brendan K. Murphy, Kenneth D. Miller  Neuron 
Volume 40, Issue 6, Pages (December 2003)
Ben Scholl, Xiang Gao, Michael Wehr  Neuron 
Hugh Pastoll, Lukas Solanka, Mark C.W. van Rossum, Matthew F. Nolan 
Dynamic Causal Modelling for M/EEG
A Cellular Mechanism for Prepulse Inhibition
Volume 79, Issue 2, Pages (July 2013)
Volume 36, Issue 5, Pages (December 2002)
Volume 37, Issue 4, Pages (February 2003)
Thomas Akam, Dimitri M. Kullmann  Neuron 
Multiplexing Visual Signals in the Suprachiasmatic Nuclei
Volume 47, Issue 3, Pages (August 2005)
Guillaume Hennequin, Tim P. Vogels, Wulfram Gerstner  Neuron 
Yann Zerlaut, Alain Destexhe  Neuron 
Volume 96, Issue 1, Pages e7 (September 2017)
Synaptic integration.
Gilad Silberberg, Henry Markram  Neuron 
Volume 30, Issue 2, Pages (May 2001)
In vitro networks: cortical mechanisms of anaesthetic action
Presentation transcript:

Zoltán Somogyvári Hungarian Academy of Sciences, KFKI Research Institute for Particle and Nuclear Physics Department of Biophysics A model-based approach to the cortical dynamics

Analysis of cortical dynamics via evoked epilepsy: Altering the dynamics with pharmacological agents on a more-or-less known way. Analysis of spatio-temporal dynamics of the evoked seizure. Chapter 1

Local field potential during seizure An experimental epilepsy model: seizure evoked by 4-Aminopyridin. 3 phases, based on typical waveshape and frequency.

Data analysis: Wavelet transformation Time (s) Frequency (Hz) Amplitude

A simple modell of cortical micro- circuits and populational dynamics Four populations of McCulloch-Pitts type neuron models Depressing excitatory and non-depressing inhibitory synapses Noise on the input Random or topographic connections between populations

The behaviour of the model: recurrent seizures and the dynamical attractors The synaptic depression drives the system from the slowing fully activated regime to regime of the irregular or chaotic oscillation.

Parameter dependence of dynamics Changeing the relative force of excitation and inhibition, - epileptiform spikes - complex seizures - status epilepticus could be obtained Seizures could be eliminated by increasing the strength of the inhibition

Comparison of the measured and the simulated pseudo-attractors Simulated Measured

Three epileptogen drug 4-aminopyridin (4AP): blocks K + -channels and increases thesynaptic transmission bicuculin (BMI):GABA-receptor blocker, inhibits the inhibition Mg 2+ -free solution (MFR):Eliminates the Mg 2+ blockades of the NMDA-receptors, thus strenghtens the excitatory synapses

Measurement of spatio-temporal potential patterns by micro-electrode systems

In electrolyte volume conductor the potential satisfies the Poisson-equation Potentials are driven by Poisson-equation J(r)=  E(r) Low frequency approximation: Ohm's law: Continuity equation:

Spatio temporal dynamics of epileptic spikes evoked by 4-AP

Spatio temporal dynamics of epileptic spikes evoked by BIC

Spatio temporal dynamics of epileptic spikes evoked by MFR

Characteristic features of the current source density maps Common features in all three type : A strong source in the IV th lamina A large sink from the top of the VI. th lamina to the V th lamina Late components with small amplitudes

Characteristic differences between the three CSD maps Typical differences: 4AP:simple structure, BMI:strong sink in the III. lamina MFR: A large sink in the VI. lamina