Network-level effects of optogenetic stimulation: experiment and simulation Cliff Kerr, Dan O'Shea, Werapong Goo, Salvador Dura-Bernal, Joe Francis, Ilka.

Slides:

Advertisements

Similar presentations

Driving fast-spiking cells induces gamma rhythm and controls sensory responses Driving fast-spiking cells induces gamma rhythm and controls sensory responses.

Advertisements

Suppression of seizure-like activity by optogenetic stimulation in reticular thalamus Student: Hsueh Tai-En 薛岱恩 Department of Psychology, NTU Advisors:

Steven L. Bressler Cognitive Neurodynamics Laboratory Center for Complex Systems & Brain Sciences Department of Psychology Florida Atlantic University.

CNTRICS April 2010 Center-surround: Adaptation to context in perception Robert Shapley Center for Neural Science New York University.

William Buchser Neuroscience Journal Club April 28 th 2004 Timing of neural responses in cortical organotypic slices.

Chapter 4: Local integration 2: Neural correlates of the BOLD signal

Neural Network of the Cerebellum: Temporal Discrimination and the Timing of Responses Michael D. Mauk Dean V. Buonomano.

The abrupt transition from theta to hyper- excitable spiking activity in stellate cells from layer II of the medial entorhinal cortex Horacio G. Rotstein.

Human Physiology Chapter 10 The Mechanisms of Body Function

Synchrony in Neural Systems: a very brief, biased, basic view Tim Lewis UC Davis NIMBIOS Workshop on Synchrony April 11, 2011.

Introduction: Neurons and the Problem of Neural Coding Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne Swiss Federal Institute of Technology.

The role of spike blocking as spike-timing-dependent plasticity mechanism Eleftheria Kyriaki Pissadaki Computational Biology Laboratory Institute of Molecular.

The visual system V Neuronal codes in the visual system.

Globally-Coupled Excitatory Izhikevich Neurons Coupling-Induced Population Synchronization in An Excitatory Population of Subthreshold Izhikevich Neurons.

Romain Brette Computational neuroscience of sensory systems Dynamics of neural excitability.

Neural Coding 4: information breakdown. Multi-dimensional codes can be split in different components Information that the dimension of the code will convey.

Biological Modeling of Neural Networks: Week 11 – Continuum models: Cortical fields and perception Wulfram Gerstner EPFL, Lausanne, Switzerland 11.1 Transients.

FAPs Bat avoidance is modulated by a constant stream of sensory input.

Gain Modulation Huei-Ju Chen Papers: Chance, Abbott, and Reyes(2002) E. Salinas & T. Sejnowski(2001) E. Salinas & L.G. Abbott (1997, 1996) Pouget & T.

CH12: Neural Synchrony James Sulzer Background Stability and steady states of neural firing, phase plane analysis (CH6) Firing Dynamics (CH7)

Feedforward networks. Complex Network Simpler (but still complicated) Network.

SME Review - September 20, 2006 Neural Network Modeling Jean Carlson, Ted Brookings.

Motor systems III: Cerebellum April 16, 2007 Mu-ming Poo Population coding in the motor cortex Overview and structure of cerebellum Microcircuitry of cerebellum.

The McCulloch-Pitts Neuron. Characteristics The activation of a McCulloch Pitts neuron is binary. Neurons are connected by directed weighted paths. A.

How facilitation influences an attractor model of decision making Larissa Albantakis.

Biological Modeling of Neural Networks: Week 15 – Population Dynamics: The Integral –Equation Approach Wulfram Gerstner EPFL, Lausanne, Switzerland 15.1.

Functional Brain Signal Processing: Current Trends and Future Directions Kaushik Majumdar Indian Statistical Institute Bangalore Center

Vrije Universiteit Amsterdam Computational Analysis of Spatiotemporal Patterns of Activity in Neuronal Networks Arjen van Ooyen (EN) Arjen Brussaard (EN)

H exam 1 H CH6: flight in locusts H locust flight H flight system H sensory integration during flight H summary PART 3: MOTOR STRATEGIES #13: FLIGHT IN.

Basal Ganglia. Involved in the control of movement Dysfunction associated with Parkinson’s and Huntington’s disease Site of surgical procedures -- Deep.

Optogenetics1 Consolato Sergi, M.D., Ph.D. University of Alberta Hospital.

Multiscale modeling of cortical information flow in Parkinson's disease Cliff Kerr, Sacha van Albada, Sam Neymotin, George Chadderdon, Peter Robinson,

Cognition, Brain and Consciousness: An Introduction to Cognitive Neuroscience Edited by Bernard J. Baars and Nicole M. Gage 2007 Academic Press Chapter.

How Parkinson’s disease affects cortical information flow: A multiscale model Cliff Kerr Complex Systems Group University of Sydney Neurosimulation Laboratory.

DCM for ERPs/EFPs Clare Palmer & Elina Jacobs Expert: Dimitris Pinotsis.

How well do we understand the neural origins of the fMRI BOLD signal? Owen J Arthurs and Simon Boniface Trends in Neuroscience, 2002 Gillian Elizabeth.

Biomedical Sciences BI20B2 Sensory Systems Human Physiology - The basis of medicine Pocock & Richards,Chapter 8 Human Physiology - An integrated approach.

Modulating seizure-permissive states with weak electric fields Marom Bikson Davide Reato, Thomas Radman, Lucas Parra Neural Engineering Laboratory - Department.

Sensory Physiology. Concepts To Understand Receptor Potential Amplitude Coding Frequency Coding Activation/Inactivation Neural Adaptation Synaptic Depression.

TEMPLATE DESIGN © In analyzing the trajectory as time passes, I find that: The trajectory is trying to follow the moving.

Romain Brette Ecole Normale Supérieure, Paris What is the most realistic single-compartment neuron model?

Dynamic Causal Modelling for EEG and MEG

Network-level effects of optogenetic stimulation: experiment and simulation Cliff Kerr 1, Dan O'Shea 2, Werapong Goo 2, Salvador Dura-Bernal 1, Joe Francis.

Complex brain networks: graph theoretical analysis of structural and functional systems.

Computational Modeling of the Auditory Periphery:

Chapter 2. From Complex Networks to Intelligent Systems in Creating Brain-like Systems, Sendhoff et al. Course: Robots Learning from Humans Baek, Da Som.

Chapter 3. Stochastic Dynamics in the Brain and Probabilistic Decision-Making in Creating Brain-Like Intelligence, Sendhoff et al. Course: Robots Learning.

Biological Modeling of Neural Networks: Week 15 – Fast Transients and Rate models Wulfram Gerstner EPFL, Lausanne, Switzerland 15.1 Review Populations.

Two Mean Neuronal Waveforms Distribution of Spike Widths Interaction of Inhibitory and Excitatory Neurons During Visual Stimulation David Maher Department.

Keeping the neurons cool Homeostatic Plasticity Processes in the Brain.

FMRI Methods Lecture8 – Electrophysiology & fMRI.

Wang TINS 2001 Wang et al PNAS 2004 Wang Neuron ms.

Electrophysiology & fMRI. Neurons Neural computation Neural selectivity Hierarchy of neural processing.

Biological Modeling of Neural Networks: Week 10 – Neuronal Populations Wulfram Gerstner EPFL, Lausanne, Switzerland 10.1 Cortical Populations - columns.

A neural test bed for simulating executive control deficits in saccade generation Uday Jagadisan Neeraj Gandhi University of Pittsburgh.

Richard Tomsett1,2 Marcus Kaiser1,3,4

Principles of Dynamic Causal Modelling

Effective Connectivity

1st Breaking Video IT_Pro 27/11/2018 Optogenetics.

? Dynamical properties of simulated MEG/EEG using a neural mass model

Stimulating Neurons electrical stimulation with an electrode to bring neuron above AP threshold depolarize neuron by changing extracellular K+ concentration.

Posterior parietal cortex

CRIS Workshop: Computational Neuroscience and Bayesian Modelling

Adaptation without Plasticity

Effective Connectivity

Neuromodulation of Brain States

Targeting Neural Circuits

Yann Zerlaut, Alain Destexhe Neuron

Adaptation without Plasticity

Volume 27, Issue 1, Pages e6 (April 2019)

Presentation transcript:

Network-level effects of optogenetic stimulation: experiment and simulation Cliff Kerr, Dan O'Shea, Werapong Goo, Salvador Dura-Bernal, Joe Francis, Ilka Diester, Paul Kalanithi, Karl Deisseroth, Krishna V. Shenoy, William W. Lytton Neurosimulation Laboratory, SUNY Downstate Medical Center www.neurosimlab.org

Outline Methods Results How does optogenetics work? How does a spiking network model work? Results How does optogenetic stimulation influence network actvity – and vice versa? How does optogenetic stimulation influence information flow?

Optogenetics Viral insertion of channelrhodopsin Neuronal activation and recording via optrode (electrode + optical fiber) New York Times, 2011 Wang et al., IEEE 2011 Adamantidis et al., Nature 2007

Spiking network model 6-layered cortex Izhikevich (integrate-and-fire) neurons 4 types of neuron: regular or bursting (excitatory), fast or low-threshold (inhibitory) 24,800 neurons total Kerr et al., Frontiers 2014

Spiking network model Anatomy & physiology based on experimental data Generates realistic dynamics Adaptable to different brain regions (e.g. sensory, motor) Demonstrated control of virtual arm Chadderdon et al., PLOS ONE 2012 Neural equations: 𝑑𝑉 𝑑𝑡 =0.04 𝑉 2 +5𝑉+140−𝑈+𝐼 𝑑𝑈 𝑑𝑡 =𝑎(𝑏𝑉−𝑈) if 𝑉≥30 mV: 𝑉←𝑐 𝑈←𝑑

Spiking network model Connectivity matrix based on rat, cat, and macaque data Strong connectivity within each layer

Model dynamics

Optogenetic response 1

Optogenetic response 2

Network-level effects Response falls off as 1/r2 from optrode Connectivity can explain firing rate heterogeneity Simulation Experiment

Granger causality: primer Time series A Granger-causes B if knowledge of A’s past helps predict B:

Granger causality: results Stimulation reduces causality in mu rhythm band (~10 Hz)

Summary First network model of optogenetics Synaptic connections determine the network’s response to optogenetic stimulation Optogenetic stimulation may be used to modulate information flow Future work: predicting the effects of specific stimulation protocols

Acknowledgements Daniel J. O'Shea (experiments) Salvador Dura-Bernal (modeling) Ilka Diester (optogenetics) Karl Deisseroth (optogenetics) William W. Lytton (modeling) Werapong Goo (experiments) Joseph T. Francis (modeling) Paul Kalanithi (optogenetics) Krishna V. Shenoy (optogenetics) .