Effect of Small-World Connectivity on Sparsely Synchronized Cortical Rhythms W. Lim (DNUE) and S.-Y. KIM (LABASIS)  Fast Sparsely Synchronized Brain Rhythms.

Slides:



Advertisements
Similar presentations
Rhythms in the Nervous System : Synchronization and Beyond Rhythms in the nervous system are classified by frequency. Alpha 8-12 Hz Beta Gamma
Advertisements

Slow oscillation, Fast oscillation, and its interactions
A model for spatio-temporal odor representation in the locust antennal lobe Experimental results (in vivo recordings from locust) Model of the antennal.
Thermodynamic and Statistical-Mechanical Measures for Synchronization of Bursting Neurons W. Lim (DNUE) and S.-Y. KIM (LABASIS)  Burstings with the Slow.
Synchrony in Neural Systems: a very brief, biased, basic view Tim Lewis UC Davis NIMBIOS Workshop on Synchrony April 11, 2011.
Globally-Coupled Excitatory Izhikevich Neurons Coupling-Induced Population Synchronization in An Excitatory Population of Subthreshold Izhikevich Neurons.
Neural Connectivity from
Marseille, Jan 2010 Alfonso Renart (Rutgers) Jaime de la Rocha (NYU, Rutgers) Peter Bartho (Rutgers) Liad Hollender (Rutgers) Néstor Parga (UA Madrid)
Biological Modeling of Neural Networks: Week 11 – Continuum models: Cortical fields and perception Wulfram Gerstner EPFL, Lausanne, Switzerland 11.1 Transients.
CH12: Neural Synchrony James Sulzer Background Stability and steady states of neural firing, phase plane analysis (CH6) Firing Dynamics (CH7)
Neuromodulation - signal-to-noise - switching - burst/single spike - oscillations.
Stable Propagation of Synchronous Spiking in Cortical Neural Networks Markus Diesmann, Marc-Oliver Gewaltig, Ad Aertsen Nature 402: Flavio Frohlich.
Connectionist Modeling Some material taken from cspeech.ucd.ie/~connectionism and Rich & Knight, 1991.
SME Review - September 20, 2006 Neural Network Modeling Jean Carlson, Ted Brookings.
The Decisive Commanding Neural Network In the Parietal Cortex By Hsiu-Ming Chang ( 張修明 )
Connected Populations: oscillations, competition and spatial continuum (field equations) Lecture 12 Course: Neural Networks and Biological Modeling Wulfram.
Biological Modeling of Neural Networks: Week 15 – Population Dynamics: The Integral –Equation Approach Wulfram Gerstner EPFL, Lausanne, Switzerland 15.1.
Mechanisms for phase shifting in cortical networks and their role in communication through coherence Paul H.Tiesinga and Terrence J. Sejnowski.
1 Noise-Induced Bursting Synchronization in A Population of Coupled Neurons  Population Synchronization  Examples Flashing of Fireflies, Chirping of.
The search for organizing principles of brain function Needed at multiple levels: synapse => cell => brain area (cortical maps) => hierarchy of areas.
Cognitive Systems Foresight Brain Rhythms. Cognitive Systems Foresight Sensory Processing How does the brain build coherent perceptual accounts of sensory.
Cognition, Brain and Consciousness: An Introduction to Cognitive Neuroscience Edited by Bernard J. Baars and Nicole M. Gage 2007 Academic Press Chapter.
Karlijn van Aerde. Two independent cortical subnetworks control spike timing of layer 5 pyramidal neurons during dynamic β oscillation shifts Karlijn.
Changju Lee Visual System Neural Network Lab. Department of Bio and Brain Engineering.
Neural coding (1) LECTURE 8. I.Introduction − Topographic Maps in Cortex − Synesthesia − Firing rates and tuning curves.
1 Dynamical System in Neuroscience: The Geometry of Excitability and Bursting پيمان گيفانی.
Sparsely Synchronized Brain Rhythms in A Small-World Neural Network W. Lim (DNUE) and S.-Y. KIM (LABASIS)
Correlation-Induced Oscillations in Spatio-Temporal Excitable Systems Andre Longtin Physics Department, University of Ottawa Ottawa, Canada.
Spatiotemporal dynamics of coupled nonlinear oscillators on complex networks Zhonghuai Hou( 侯中怀 ) Beijing Department of Chemical Physics Hefei.
Neural dynamics of in vitro cortical networks reflects experienced temporal patterns Hope A Johnson, Anubhuthi Goel & Dean V Buonomano NATURE NEUROSCIENCE,
Multiple attractors and transient synchrony in a model for an insect's antennal lobe Joint work with B. Smith, W. Just and S. Ahn.
Graph Evolution: A Computational Approach Olaf Sporns, Department of Psychological and Brain Sciences Indiana University, Bloomington, IN 47405
11 Dynamical Routes to Clusters and Scaling in Globally Coupled Chaotic Systems Sang-Yoon Kim Department of Physics Kangwon National University  Globally.
Study on synchronization of coupled oscillators using the Fokker-Planck equation H.Sakaguchi Kyushu University, Japan Fokker-Planck equation: important.
Week 14 The Memory Function of Sleep Group 3 Tawni Voyles Alyona Koneva Bayou Wang.
The Function of Synchrony Marieke Rohde Reading Group DyStURB (Dynamical Structures to Understand Real Brains)
“Modeling Neurological Disease” Fields Introductory Tutorial
Chapter 2. From Complex Networks to Intelligent Systems in Creating Brain-like Systems, Sendhoff et al. Course: Robots Learning from Humans Baek, Da Som.
Sara A. Solla Northwestern University
Rhythms and Cognition: Creation and Coordination of Cell Assemblies Nancy Kopell Center for BioDynamics Boston University.
Neuronal Dynamics: Computational Neuroscience of Single Neurons
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.
Inhibitory Population of Bursting Neurons Frequency-Domain Order Parameter for Synchronization Transition of Bursting Neurons Woochang Lim 1 and Sang-Yoon.
Neural Synchronization via Potassium Signaling. Neurons Interactions Neurons can communicate with each other via chemical or electrical synapses. Chemical.
Ch 9. Rhythms and Synchrony 9.7 Adaptive Cooperative Systems, Martin Beckerman, Summarized by M.-O. Heo Biointelligence Laboratory, Seoul National.
From LIF to HH Equivalent circuit for passive membrane The Hodgkin-Huxley model for active membrane Analysis of excitability and refractoriness using the.
Alternating and Synchronous Rhythms in Reciprocally Inhibitory Model Neurons Xiao-Jing Wang, John Rinzel Neural computation (1992). 4: Ubong Ime.
Biological Modeling of Neural Networks: Week 10 – Neuronal Populations Wulfram Gerstner EPFL, Lausanne, Switzerland 10.1 Cortical Populations - columns.
2. Neuronal Structure and Function. Neuron Pyramidal cell Basal Dendrites Axon Myelin sheath Apica Dendrites Postsynaptic cells Preynaptic cells Synapse.
Cmpe 588- Modeling of Internet Emergence of Scale-Free Network with Chaotic Units Pulin Gong, Cees van Leeuwen by Oya Ünlü Instructor: Haluk Bingöl.
Structures of Networks
What weakly coupled oscillators can tell us about networks and cells
Jun-ichi Yamanishi, Mitsuo Kawato, Ryoji Suzuki Emilie Dolan
Theta, Gamma, and Working Memory
Neural Oscillations Continued
Information Processing by Neuronal Populations
Adrian Birzu University of A. I. Cuza, Iaşi, Romania Vilmos Gáspár
Presentation of Article:
Carlos D. Brody, J.J. Hopfield  Neuron 
Synchrony & Perception
Volume 30, Issue 2, Pages (May 2001)
Volume 36, Issue 5, Pages (December 2002)
Woochang Lim1 and Sang-Yoon Kim2
Yann Zerlaut, Alain Destexhe  Neuron 
Information Processing by Neuronal Populations Chapter 5 Measuring distributed properties of neural representations beyond the decoding of local variables:
Jozsef Csicsvari, Hajime Hirase, Akira Mamiya, György Buzsáki  Neuron 
Scale-Change Symmetry in the Rules Governing Neural Systems
Stefano Panzeri, Jakob H. Macke, Joachim Gross, Christoph Kayser 
Sparsely Synchronized Brain Rhythm in A Small-World Neural Network
In vitro networks: cortical mechanisms of anaesthetic action
Presentation transcript:

Effect of Small-World Connectivity on Sparsely Synchronized Cortical Rhythms W. Lim (DNUE) and S.-Y. KIM (LABASIS)  Fast Sparsely Synchronized Brain Rhythms  Population Level: Fast Oscillations e.g., gamma rhythm (30~100Hz) and sharp-wave ripple (100~200Hz)  Cellular Level: Stochastic and Intermittent Discharges  Associated with Diverse Cognitive Functions e.g., sensory perception, feature integration, selective attention, and memory formation and consolidation  Complex Brain Network Network Topology: Complex (Neither Regular Nor Random) Effect of Network Architecture on Fast Sparsely-Synchronized Brain Rhythms  Optimal Sparsely-Synchronized Rhythm in An Economic Network 1

Fast Sparsely Synchronized Cortical Rhythms 2  Gamma Rhythm ( Hz) in the Awake Behaving States Sharp-Wave Ripples in the Hippocampus Appearance during Slow-Wave Sleep (Associated with Memory Consolidation)  Sharp-Wave Ripples ( Hz) Fast Small-Amplitude Population Rhythm (55 Hz) with Stochastic and Intermittent Neural Discharges (Interneuron: 2 Hz & Pyramidal Neuron: 10 Hz) Associated with Diverse Cognitive Functions (sensory perception, feature integration, selective attention, and memory formation) Sharp-Wave Ripples in the Cerebellum Millisecond Synchrony between Purkinje Cells  Fine Motor Coordination

Sparse Synchronization vs. Full Synchronization 3  Fully Synchronized Rhythms  Sparsely Synchronized Rhythms Individual Neurons: Regular Firings like Clocks Large-Amplitude Population Rhythm via Full Synchronization of Individual Regular Firings Investigation of This Huygens Mode of Full Synchronization Using the Conventional Coupled (Clock-Like) Oscillator Model Individual Neurons: Intermittent and Stochastic Firings like Geiger Counters Small-Amplitude Fast Population Rhythm via Sparse Synchronization of Individual Complex Firings Investigation of Sparse Synchronization in Networks of Coupled (Geiger-Counter-Like) Neurons Exhibiting Complex Firing Patterns

Network of Inhibitory Fast-Spiking (FS) Izhikevich Interneurons 4  Interneuronal Network (I-I Loop) Playing the role of the backbones of many brain rhythms by providing a synchronous oscillatory output to the principal cells  FS Izhikevich Interneuron Izhikevich Interneuron Model: not only biologically plausible (Hodgkin-Huxley neuron-like), but also computationally efficient (IF neuron-like)

Population Synchronization in the Random Network of FS Izhikevich Interneurons 5  Conventional Erdös-Renyi (ER) Random Graph  State Diagram in the J-D Plane for I DC =1500 Complex Connectivity in the Neural Circuits: Modeled by Using The ER Random Graph for M syn =50

Evolution of Population Sync. in the Random Graph  Full Synchronization (f p =f i )  Unsynchronization  Full Synchronization (f p =f i )  Partial Synchronization (f p >f i )  Sparse Synchronization (f p >4 f i )  Unsynchronization e.g., J=100: f p =f i =197Hz e.g., J=1400 D=100; f p =f i =68Hz, D=200; f p =110Hz, f i =38Hz, D=500; f p =147Hz, f i =33Hz. 6 Appearance of Sparse Synchronization under the Balance between Strong External Excitation and Strong Recurrent Inhibition

Emergence of Sparsely Synchronized Rhythms in a Small World Network of FS Interneurons Cortical Circuits: Neither Regular Nor Random 7  Watts-Strogatz Small World Network Interpolating between the Regular Lattice and the Random Graph via Rewiring Start with directed regular ring lattice with N neurons where each neuron is coupled to its first k neighbors. Rewire each outward connection at random with probability p such that self-connections and duplicate connections are excluded. Investigation of Population Synchronization by Increasing the Rewiring Probability p for J=1400 & D=500  Asynchrony-Synchrony Transition Incoherent State: N , then O  0 Coherent State: N , then O  Non-zero value Thermodynamic Order Parameter: (Population-Averaged Membrane Potential) Occurrence of Population Synchronization for p>p th (~ 0.12)

Unsynchronized and Synchronized Population States 8  Unsynchronized State in the Regular Lattice (p=0) Raster plot: Zigzag pattern intermingled with inclined partial stripes V G : Coherent parts with regular large-amplitude oscillations and incoherent parts with irregular small-amplitude fluctuations. With increasing N, Partial stripes become more inclined. Spikes become more difficult to keep pace Amplitude of V G becomes smaller & duration of incoherent parts becomes longer V G tends to be nearly stationary as N ,  Unsynchronized Population State Raster plot: Little zigzagness V G displays more regular oscillation as N   Synchronized Population State  Synchronized State for p=0.25

Characterization of Sparsely Synchronized States 9  Raster Plot and Global Potential  Interspike Interval Histograms Multiple peaks at multiples of the period of the global potential Stochastic Phase Locking Leading to Stochastic Spike Skipping With increasing p, the zigzagness degree in the raster plot becomes reduced. p>p max (~0.4): Raster plot composed of stripes without zigzag and nearly same pacing degree. Amplitude of V G increases up to p max, and saturated. Appearance of Ultrafast Rhythm with f p = 147 Hz  Statistical-Mechanical Spiking Measure Taking into Consideration the Occupation (O i ) and the Pacing Degrees (P i ) of Spikes in the Stripes of the Raster Plot

Economic Small-World Network  Synchrony Degree M s and Wiring Length Optimal Sparsely-Synchronized Ultrafast Rhythm for p=p * DE (~ 0.31) Raster plot with a zigzag pattern due to local clustering of spikes Regular oscillating global potential Optimal Ultrafast Rhythm Emerges at A Minimal Wiring Cost in An Economic Small-World Network for p=p * DE (~0.31).  Optimal Sparsely-Synchronized Rhythm for p=p * DE  Dynamical Efficiency Factor Tradeoff between Synchrony and Wiring Economy 10 Wiring length increases linearly with respect to p.  With increasing p, the wiring cost becomes expensive. With increasing p, synchrony degree M s is increased until p=p max because global efficiency of information transfer becomes better.

Summary  Emergence of Fast Sparsely Synchronized Rhythm in A Small-World Network of Inhibitory Izhikevich FS Interneurons Regular Lattice of Izhikevich FS Interneurons (p=0)  Unsynchronized Population State Occurrence of Ultrafast Sparsely Synchronized Rhythm as the Rewiring Probability Passes a Threshold p th (~0.12):  Population Rhythm ~ 147 Hz (Small-Amplitude Ultrafast Sinusoidal Oscillation) Intermittent and Irregular Discharge of Individual Interneurons at 33 Hz (Geiger-Counter-Like Firings) Emergence of Optimal Ultrafast Sparsely-Synchronized Rhythm at A Minimal Wiring Cost in An Economic Small-World Network for p=p * DE (~0.31) 11