Presented by Sarah Hedayat. Supervised by Pr.Cappy and Dr.Hoel

Similar documents
Math 345 Intro to Math Biology Lecture 20: Mathematical model of Neuron conduction

Signal processing in nervous system - Hodgkin-Huxley model

1 Hodgkin-Huxley Theory of Nerve Membranes: The FitzHugh-Nagumo model

Lecture 10 : Neuronal Dynamics. Eileen Nugent

6.3.4 Action potential

FRTF01 L8 Electrophysiology

Bo Deng University of Nebraska-Lincoln UNL Math Biology Seminar

Electrophysiology of the neuron

Lecture Notes 8C120 Inleiding Meten en Modelleren. Cellular electrophysiology: modeling and simulation. Nico Kuijpers

Chapter 2 The Hodgkin Huxley Theory of Neuronal Excitation

From neuronal oscillations to complexity

Physiology Unit 2. MEMBRANE POTENTIALS and SYNAPSES

Decoding. How well can we learn what the stimulus is by looking at the neural responses?

Compartmental Modelling

Neural Modeling and Computational Neuroscience. Claudio Gallicchio

Basic elements of neuroelectronics -- membranes -- ion channels -- wiring

Νευροφυσιολογία και Αισθήσεις

Chapter 24 BIFURCATIONS

Biomedical Instrumentation

MATH 3104: THE HODGKIN-HUXLEY EQUATIONS

Basic elements of neuroelectronics -- membranes -- ion channels -- wiring. Elementary neuron models -- conductance based -- modelers alternatives

Introduction and the Hodgkin-Huxley Model

9.01 Introduction to Neuroscience Fall 2007

Computational Neuroscience Summer School Neural Spike Train Analysis. An introduction to biophysical models (Part 2)

Neurochemistry 1. Nervous system is made of neurons & glia, as well as other cells. Santiago Ramon y Cajal Nobel Prize 1906

Structure and Measurement of the brain lecture notes

Simulating Hodgkin-Huxley-like Excitation using Comsol Multiphysics

An Introductory Course in Computational Neuroscience

Mathematical Foundations of Neuroscience - Lecture 3. Electrophysiology of neurons - continued

Topics in Neurophysics

Single neuron models. L. Pezard Aix-Marseille University

Lecture 11 : Simple Neuron Models. Dr Eileen Nugent

BIOELECTRIC PHENOMENA

Ionic basis of the resting membrane potential. Foundations in Neuroscience I, Oct

Simulation of Cardiac Action Potentials Background Information

Action Potentials & Nervous System. Bio 219 Napa Valley College Dr. Adam Ross

PNS Chapter 7. Membrane Potential / Neural Signal Processing Spring 2017 Prof. Byron Yu

BME 5742 Biosystems Modeling and Control

Dynamical systems in neuroscience. Pacific Northwest Computational Neuroscience Connection October 1-2, 2010

Physiology Unit 2. MEMBRANE POTENTIALS and SYNAPSES

All-or-None Principle and Weakness of Hodgkin-Huxley Mathematical Model

2013 NSF-CMACS Workshop on Atrial Fibrillation

Simple models of neurons!! Lecture 4!

Lokta-Volterra predator-prey equation dx = ax bxy dt dy = cx + dxy dt

Deconstructing Actual Neurons

Mathematical analysis of a 3D model of cellular electrophysiology

Single-Cell and Mean Field Neural Models

STUDENT PAPER. Santiago Santana University of Illinois, Urbana-Champaign Blue Waters Education Program 736 S. Lombard Oak Park IL, 60304

Systems Biology: Theoretical Biology. Kirsten ten Tusscher, Theoretical Biology, UU

Overview Organization: Central Nervous System (CNS) Peripheral Nervous System (PNS) innervate Divisions: a. Afferent

Single-Compartment Neural Models

/639 Final Solutions, Part a) Equating the electrochemical potentials of H + and X on outside and inside: = RT ln H in

Nonlinear Observer Design and Synchronization Analysis for Classical Models of Neural Oscillators

Neuroscience applications: isochrons and isostables. Alexandre Mauroy (joint work with I. Mezic)

Membrane Potentials, Action Potentials, and Synaptic Transmission. Membrane Potential

Resting Distribution of Ions in Mammalian Neurons. Outside Inside (mm) E ion Permab. K Na Cl

BRIEF COMMUNICATIONS COMPUTATION OF AXON GATING CURRENTS FROM DIPOLE MOMENT CHANGES IN CHANNEL SUBUNITS

Neurons. The Molecular Basis of their Electrical Excitability

Dynamics from Seconds to Hours in Hodgkin Huxley Model

Modeling cells in tissue by connecting. electrodiffusion and Hodgkin Huxley-theory.

Action Potential Propagation

1 Introduction and neurophysiology

Peripheral Nerve II. Amelyn Ramos Rafael, MD. Anatomical considerations

Modelling biological oscillations

The Resonate-and-fire Neuron: Time Dependent and Frequency Selective Neurons in Neural Networks

PETER PAZMANY CATHOLIC UNIVERSITY Consortium members SEMMELWEIS UNIVERSITY, DIALOG CAMPUS PUBLISHER

Quantitative Electrophysiology

Effects of Betaxolol on Hodgkin-Huxley Model of Tiger Salamander Retinal Ganglion Cell

9 Generation of Action Potential Hodgkin-Huxley Model

Parameters for Minimal Model of Cardiac Cell from Two Different Methods: Voltage-Clamp and MSE Method

Modeling Action Potentials in Cell Processes

NEURONS, SENSE ORGANS, AND NERVOUS SYSTEMS CHAPTER 34

An Investigation of the Coefficient of Variation Using the Dissipative Stochastic Mechanics Based Neuron Model

Neurons, Synapses, and Signaling

Limitations of the Hodgkin-Huxley Formalism: Effects of Single Channel Kinetics on Transmembrane Voltage Dynamics

Chapter 2: Neurons and Glia

Microsystems for Neuroscience and Medicine. Lecture 9

Quantitative Electrophysiology

Biosciences in the 21st century

Alteration of resting membrane potential

Lecture 2. Excitability and ionic transport

9 Generation of Action Potential Hodgkin-Huxley Model

General Physics. Nerve Conduction. Newton s laws of Motion Work, Energy and Power. Fluids. Direct Current (DC)

On Parameter Estimation for Neuron Models

LIMIT CYCLE OSCILLATORS

80% of all excitatory synapses - at the dendritic spines.

Biological Modeling of Neural Networks

Housekeeping, 26 January 2009

Neurons. 5 th & 6 th Lectures Mon 26 & Wed 28 Jan Finish Solutes + Water. 2. Neurons. Chapter 11

ACTION POTENTIAL. Dr. Ayisha Qureshi Professor MBBS, MPhil

Dynamics and complexity of Hindmarsh-Rose neuronal systems

Voltage-clamp and Hodgkin-Huxley models

Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting

Spike-Frequency Adaptation: Phenomenological Model and Experimental Tests

Introduction to Neural Networks U. Minn. Psy 5038 Spring, 1999 Daniel Kersten. Lecture 2a. The Neuron - overview of structure. From Anderson (1995)

Transmission of Nerve Impulses (see Fig , p. 403)

Introduction to Neural Networks. Daniel Kersten. Lecture 2. Getting started with Mathematica. Review this section in Lecture 1

Lecture 04, 04 Sept 2003 Chapters 4 and 5. Vertebrate Physiology ECOL 437 University of Arizona Fall instr: Kevin Bonine t.a.


4. Active Behavior of the Cell Membrane 4.1 INTRODUCTION

Transcription:

1 Presented by Sarah Hedayat Supervised by Pr.Cappy and Dr.Hoel

Outline 2 Project objectives Key elements Membrane models As simple as possible Phase plane analysis Review of important Concepts Conclusion and perspectives References

Project Objectives 3 Short term: Build and validate a membrane model Energy budget of one spiking neuron Long term: Design and fabrication of neuromorphic circuit This circuit must be simple and low power.

Key elements : Human Brain 4 The human brain have around : 11 10 neurons. 15 100 000 synapses/neuron; 10 synapses. 3 300 billions connections in 1cm.

5 Key elements : Neuron Neuron: Soma Axon Synapses Dendrites Membrane Information : Action potential or Spike Regenerated (Ranvier node) Non attenuation

6 Key elements : Neuronal membrane Outside Neuronal membrane : 1) Na +, K + gated channels 2) Ion pump channels 3) Leaky channels Channels are selective Ion gradient => Built in potential Opposite sign (Ena Ek) Ena< Spike amplitude <Ek Inside

7 Membrane model 1945 : The first intracellular recording of an action potential 1952 : Discovered the relation between : Ionic mechanisms nerve cell membrane & Spike [1] 1963 : Nobel Prize Medicine [1] Hodgkin and Huxley, 1952 The Journal of Physiology,2012

Membrane model 8 Hodgkin Huxley Vs. Wei : Infinite Volume Vs. definite volume. Charge density conservation Vs. Individual charge conservation. Constant E Nernst Vs. Variable E Nernst Non Ipump Vs. Ipump Squid axon Vs. Human neuron [2]: Wei et al. 2014

MembraneModel : HH Vs. Wei 9 HH (1952) Wei (2014)

10 Membrane Model: Wei Model Wei model: Based on Hodgkin Huxley equations. Variable E Nernst Ipump Individual ions conservation

11 Membrane Model: Excitability of neuron Iex=7, 50 and 100µA/cm 2, Ts=7ms

12 Membrane Model: For Iext= 7µA/cm 2 Ts=7 ms, Ionics current (INa, IK, IL)

13 Membrane Model: For Iext= 7µA/cm 2 Ts=7ms, Power (PNa, PK, Pd)

14 Membrane Model: Energy Dissipation for Iext= 7µA/cm 2 Ts=7ms S=10-6 cm 2 Wei s Energy (pj) ENa = 0.0027 EK = 0.0002 ED = 0.0029 == 0.03 LF model energy by Cea-Leti (pj) ED = 1.4-10 [3] Antoine Joubert, Thèse Neurone analogique robuste et technologies emergentes pour les architectures neuromorphiques, Cea Leti 2014.

15 Membrane Model The membrane model is a nonlinear four dimensional dynamical system (V, m, n, h). The understanding of nonlinear dynamic is difficult. To propose a simple neuromorphic circuit a simplified membrane model is suitable. Various simpler mathematical model, which capture the key features of the full system, have been proposed. The best known is the FitzHugh-Nagumo model.

As simple as possible 16 In the mid-1950 s, FitzHugh sought to reduce the Hodgkin- Huxley model to a two variable model for which phase plane analysis applies. Ø n and h have slow kinetics relative to m. => m = m steady state Ø n + h is approximately 0.8. [4] Richard FitzHugh at the National Institute of Health.

17 As simple as possible : Wei model simplified Wei model with 4 variables [2] Wei model with 2 variables Allow phase plane analysis. n w (V) : time constant [2] Y. Wei et al, Unification of Neuronal Spikes, Seizures, and Spreading Depression, The Journal of Neuroscience (August 27, 2014): 341173

18 Phase plane analysis : Nullclines, Equilibrium point, Stability Nullclines: V Nullcline : dv/dt=0 N Nullcline : dn/dt=0 Equilibrium point : The intersection of nullclines is an equilibrium point. The equilibrium point may be unstable. Stability : Definition stable equilibrium point : (dv/dt). (dn/dt) < 0 Definition unstable equilibrium point : (dv/dt). (dn/dt) > 0

19 Phase plane analysis : Iext = 0 µa/cm 2 3 Equilibrium point : 1 stable 2 unstable

20 Phase plane analysis : A step of Iext = 7 µa/cm 2 1 Equilibrium point : 1 unstable

21 Phase plane analysis : A step of Iext = 0 µa/cm 2 Vs Iext = 7µA/cm 2 3 Equilibrium point for Iext=0 µa/cm 2 Stable 1 Unstable 2 1 Equilibrium point for Iext=7µA/cm 2 Stable 0 Unstable 1 N Nullcline remain the same for both Iext=0µA/cm 2 and Iex=7µA/cm 2.

22 Phase plane analysis : A step of Iext= 0 µa/cm 2 to 1.8µA/cm 2 Iext= 0 to 1.6 µa/cm 2 : Stable 1 Unstable 2 Iext= 1.7µA/cm 2 is the break point : Stable 1 Unstable 2 Iext = 1.8 µa/cm 2 : Stable 0 Unstable 1

23 Phase plane analysis Iex = 7µA/cm 2 N- N+ V- V+ Cycle : Counterclockwise 4 different zone s signs.

24 Review of important Concepts q Neurons are dynamical systems. q A good neuron model must reproduce not only electrophysiology but also the non linear dynamics of neurons. q A good neuron circuit has to be low power and as simple as possible.

25 Conclusion and perspectives Conclusion The HH and Wei publications has been coded with MATLAB and results show similar behavior. These models permit the estimation of the energy dissipated for one spiking neuron. Wei code was simplified for non linear analysis and remain our choice for cell membrane model. Perspectives Two inter-connected neurons. Matlab Spice Spice Modeling of MOS (EKV, BSIM) or other devices (TiFET, Memristors, ) Manufacturing of simple, ultra low power Neuoroinspired circuit.

26 References Publications Books & Journals [1]A.L. Hodgkin, and Huxley, A.F., A quantitative description of membrane current and its application to conduction and excitation in nerve, J.Physiol.1952; 117, 504-544. [2] Y. Wei et al, Unification of Neuronal Spikes, Seizures, and Spreading Depression, The Journal of Neuroscience (August 27, 2014): 341173

THANK YOU FOR LISTENING! Neuroinspired Project. Supervised by Pr Cappy and Dr Hoel. Presented by Sarah Hedayat.