Computing with Spikes in Recurrent Neural Networks
|
|
- Jocelin Hood
- 6 years ago
- Views:
Transcription
1 Computing with Spikes in Recurrent Neural Networks Dezhe Jin Department of Physics The Pennsylvania State University Presented at ICS Seminar Course, Penn State Jan 9, 2006
2 Outline Introduction Neurons, neural networks, and neural computations with dynamical attractors Spike sequence attractors Exist for a large class of neural networks Fast convergence Rich structures Summary
3 Introduction
4 Brain & local neural networks Human brain : neurons Hierarchal, modular, interacting structures: cortical areas Local neural networks
5 Neuron: membrane potential & spikes A neuron is like a battery charged leaky capacitor Dendrite Cell body Axon Inhibitory neurons Inhibitory conductance Excitatory neurons Excitatory conductance Membrane potential V ~ -70 mv Outside 0 mv Ions Leak conductance Voltagedependent conductance Membrane Input V (mv) Spikes are transmitted to other neurons 0-70 Spike (width ~1msec) Threshold Reset Output time
6 Local networks: lateral excitation & global inhibition Composition Excitatory neurons : number ~80%, output to other networks Inhibitory neurons : number ~20%, no output to other networks Coupling between the excitatory neurons Lateral excitation Global inhibition via the inhibitory neurons (inter-neurons) Inhibitory neuron Excitatory neuron Inputs from lower area neurons Outputs to other local networks
7 Computing with dynamical attractors spike Local neural network Membrane potential Tiger attractor time Cow attractor Dynamical attractors potential
8 Charactering the attractors Encoding capability Is the convergence fast? Is the number of attractors large enough to encode a large number of external input patterns? Spatial or spatiotemporal? Spatial: only spiking rates are important (Hopfield, PNAS, 1984)
9 Spatiotemporal patterns of spikes Neurons of the local networks in locust antennal lobe responding to odor presentation Trial 1 Neuron 1 Neuron 2 Membrane potential Trial 2 Neuron 1 Neuron 2 Presentation of odor 200 msec 40 mv Stopfer & Laurent (Nature, 1999)
10 Spatiotemporal spike attractors For a large class of neural networks, spatiotemporal spikes with precise timings are the dynamical attractors. Fast convergence with a few transient spikes Rich spatiotemporal structures Simplifications Simple models of neurons and the coupling between them No inter-neurons, allowing direct excitation and inhibition between neurons No noise, spike transmission delay,... Roadmap: A special case: winner-take-all computation General case
11 Winner-take-all computation
12 The structure of the network Inhibitory connection (global inhibition) Excitatory connection (self-excitation) External inputs No inhibitory inter-neurons Identical neurons, excitatory connection strength, and inhibitory connection strength External inputs constant in time but vary spatially
13 Neuron model : Leaky integrate-and-fire neuron Leaky integrate- Membrane potential Leak time constant τ dv dt = E R V + I External input Resting membrane potential Spike (not modeled) -and-fire (spike) Spike threshold If the membrane potential reaches a threshold V th (< 0mV), send a spike out and reset the mebrane potential to V r < V th. Reset
14 δ-pulse coupling τ dv dt = E R V + I G E δ ( t t spike )V ' + G I δ t t spike ( ) ( ) E I V G E : strength of excitatory connection G I : strength of inhibitory connection E I : reversal potential, -75 mv ' t spike, t spike : time of spike reception conductance Spike time
15 The winner-take-all attractor No spikes External inputs time potential Periodic spiking Neuron with the maximum input The attractor Only the neuron with the maximum input spikes; it spikes periodically.
16 Fast winner-take-all computation Computation Maximum input selection: peak detection in the external inputs Fast convergence The computation is done as soon as the neuron with the maximum input spikes once. Very few transient spikes are needed. (simulation) Jin & Seung (PRE, 2002)
17 Intuitive picture Two stage dynamics: between spikes & at the spike With a strong inhibition, spikes from the winner suppress spiking of all other neurons. Between spikes: Race to spike At spike: membrane potentials jump
18 A mapping technique
19 The Γ-mapping Spike time without interaction T j,k(n) = τ log 1+ V th V + j,k(n) I j I th τlog ( Γ j,k(n) ). Neuron ID of next spike Threshold current Γ k(n+1),k(n) = min ( Γ j,k(n) ), j=1...n Γ j,k(n) Γ = ψ + ε. j,k(n +1) j Γ k(n+1),k(n) Pseudo-spike time Neuron ID of the nth spike of the network Pseudo-spike times relative to next spike Constants depending on the external inputs and the connection strength
20 Condition for winner-take-all I i I th > η( G E,G I )( I j I ) th for all j i. Γ i,k(n)=i < Γ j,k(n)=i for all j i. After neuron i spikes once, no other neuron can spike. Maximum input selection : η( G E,G I ) = 1.
21 Spatiotemporal spike attractors
22 A class of neural networks Excitatory connection Network structure Strong global inhibition Arbitrary number of spiking neurons Arbitrary connectivity Arbitrary patterns of the external inputs Heterogeneity in neuron properties External inputs Inhibitory connection Simplifications No inter-neurons Leaky integrate-and-fire neuron model Synaptic coupling : δ-pulse No noise, no spike transmission delay External inputs constant in time but distributed spatially
23 Spike sequence attractors Spike sequence attractor All spike sequences flow into spike sequence attractors. Timings of the spikes in the attractor are precise. The convergence is fast when the inhibition is strong. (simulation) Jin (PRL, 2002)
24 Description of the dynamics In between spikes: race to spike One neurons spikes; all membrane potentials jump discontinuously
25 The Γ-mapping Neuron ID of next spike Pseudo-spike time Γ k(n+1),k(n) = min ( Γ j,k(n) ), j=1...n Γ j,k(n+1) = ψ j,k(n+1) + ε j,k(n+1) Neuron ID of the nth spike of the network Γ j,k(n) Γ k(n+1),k(n). Pseudo-spike times relative to next spike Constants depending on the external inputs and the connection strength
26 Stability of the mapping Exponential damping of small perturbations Neuron ID Γ Perturbed Unperturbed st 2 nd 3 rd 3 Spike No. Define Δ n max l=1...n Γ l,k(n) Γ ' l,k(n). Then Δ n < λ n 2DΔ 1. ( ). Here, λ < 1, and λ exp minimum connection strength
27 Trapping of spike sequences Consider two spike sequences: S1=(...,i1,i2,...,iP,iP+1,...), S2=(..., j1, j2,..., jp, jp+1,...), with i n = j n for n = 1,..., P. There exits a finite P * such that if P > P *, i n = j n for all n > P. Moreover, the spike timing difference decreases exponentially with P. Here P * 1 log λ.
28 Spike sequence attractors All spike sequences will be trapped in periodic patterns (spike sequence attractors). Subsequences of any finite length will appear again in an infinite sequence with finite number of neurons. For N = 2 and P * = 4 : S = ( 1,1,1,1,2,2,1,1,2,1,2,2,1,2,2,2,2,1,2,2,1,2,2,2,... ) Spike sequence attractor
29 An example N = <inhibition strength < <excitation strength<0.05. τ = 40 msec. Random inputs.
30 Fast convergence - statistics Histogram Number of transient spikes Number of transient spikes Length of the attractor sequence Simulation 2000 runs. For each run, the connections and the external inputs are randomly set. The maximum of the external inputs is fixed. The range of the connection strength is fixed. Results Poisson distribution of the number of the transient spikes No relationship between the length of the spike sequence attractor and the number of transient spikes
31 Rich structures - statistics Number of attractors Spike sequence attractors Number of neurons, N Spatial pattern attractors Simulation Averaged over 20 random networks 10 N sets of randomly selected inputs with fixed maximum for each network 10 random initial conditions for each network and each set of inputs Results Exponential growth of the number of spike sequence attractors with the network size On average one attractor for one set of external inputs
32 Summary Spike sequence attractors are the dynamical attractors for a large class of neural networks. These attractors have two favorable characteristics for neural computation: fast convergence and rich structures.
Spiking Inputs to a Winner-take-all Network
Spiking Inputs to a Winner-take-all Network Matthias Oster and Shih-Chii Liu Institute of Neuroinformatics University of Zurich and ETH Zurich Winterthurerstrasse 9 CH-857 Zurich, Switzerland {mao,shih}@ini.phys.ethz.ch
More informationNeuromorphic computing
Neuromorphic computing Robotics M.Sc. programme in Computer Science lorenzo.vannucci@santannapisa.it April 19th, 2018 Outline 1. Introduction 2. Fundamentals of neuroscience 3. Simulating the brain 4.
More informationBasics of Computational Neuroscience: Neurons and Synapses to Networks
Basics of Computational Neuroscience: Neurons and Synapses to Networks Bruce Graham Mathematics School of Natural Sciences University of Stirling Scotland, U.K. Useful Book Authors: David Sterratt, Bruce
More informationInternational Journal of Advanced Computer Technology (IJACT)
Abstract An Introduction to Third Generation of Neural Networks for Edge Detection Being inspired by the structure and behavior of the human visual system the spiking neural networks for edge detection
More informationRolls,E.T. (2016) Cerebral Cortex: Principles of Operation. Oxford University Press.
Digital Signal Processing and the Brain Is the brain a digital signal processor? Digital vs continuous signals Digital signals involve streams of binary encoded numbers The brain uses digital, all or none,
More informationSUPPLEMENTARY INFORMATION
doi:10.1038/nature10776 Supplementary Information 1: Influence of inhibition among blns on STDP of KC-bLN synapses (simulations and schematics). Unconstrained STDP drives network activity to saturation
More informationComputational cognitive neuroscience: 2. Neuron. Lubica Beňušková Centre for Cognitive Science, FMFI Comenius University in Bratislava
1 Computational cognitive neuroscience: 2. Neuron Lubica Beňušková Centre for Cognitive Science, FMFI Comenius University in Bratislava 2 Neurons communicate via electric signals In neurons it is important
More informationModeling the Primary Visual Cortex
Modeling the Primary Visual Cortex David W. McLaughlin Courant Institute & Center for Neural Science New York University http://www.cims.nyu.edu/faculty/dmac/ Ohio - MBI Oct 02 Input Layer of Primary Visual
More informationTemporal coding in the sub-millisecond range: Model of barn owl auditory pathway
Temporal coding in the sub-millisecond range: Model of barn owl auditory pathway Richard Kempter* Institut fur Theoretische Physik Physik-Department der TU Munchen D-85748 Garching bei Munchen J. Leo van
More informationHow Neurons Do Integrals. Mark Goldman
How Neurons Do Integrals Mark Goldman Outline 1. What is the neural basis of short-term memory? 2. A model system: the Oculomotor Neural Integrator 3. Neural mechanisms of integration: Linear network theory
More informationInput-speci"c adaptation in complex cells through synaptic depression
0 0 0 0 Neurocomputing }0 (00) } Input-speci"c adaptation in complex cells through synaptic depression Frances S. Chance*, L.F. Abbott Volen Center for Complex Systems and Department of Biology, Brandeis
More informationEvaluating the Effect of Spiking Network Parameters on Polychronization
Evaluating the Effect of Spiking Network Parameters on Polychronization Panagiotis Ioannou, Matthew Casey and André Grüning Department of Computing, University of Surrey, Guildford, Surrey, GU2 7XH, UK
More informationHeterogeneous networks of spiking neurons: self-sustained activity and excitability
Heterogeneous networks of spiking neurons: self-sustained activity and excitability Cristina Savin 1,2, Iosif Ignat 1, Raul C. Mureşan 2,3 1 Technical University of Cluj Napoca, Faculty of Automation and
More informationDifferent inhibitory effects by dopaminergic modulation and global suppression of activity
Different inhibitory effects by dopaminergic modulation and global suppression of activity Takuji Hayashi Department of Applied Physics Tokyo University of Science Osamu Araki Department of Applied Physics
More informationCell Responses in V4 Sparse Distributed Representation
Part 4B: Real Neurons Functions of Layers Input layer 4 from sensation or other areas 3. Neocortical Dynamics Hidden layers 2 & 3 Output layers 5 & 6 to motor systems or other areas 1 2 Hierarchical Categorical
More informationInformation Processing During Transient Responses in the Crayfish Visual System
Information Processing During Transient Responses in the Crayfish Visual System Christopher J. Rozell, Don. H. Johnson and Raymon M. Glantz Department of Electrical & Computer Engineering Department of
More informationTEMPORAL PRECISION OF SENSORY RESPONSES Berry and Meister, 1998
TEMPORAL PRECISION OF SENSORY RESPONSES Berry and Meister, 1998 Today: (1) how can we measure temporal precision? (2) what mechanisms enable/limit precision? A. 0.1 pa WHY SHOULD YOU CARE? average rod
More informationNeurons: Structure and communication
Neurons: Structure and communication http://faculty.washington.edu/chudler/gall1.html Common Components of a Neuron Dendrites Input, receives neurotransmitters Soma Processing, decision Axon Transmits
More informationWhat is Anatomy and Physiology?
Introduction BI 212 BI 213 BI 211 Ecosystems Organs / organ systems Cells Organelles Communities Tissues Molecules Populations Organisms Campbell et al. Figure 1.4 Introduction What is Anatomy and Physiology?
More informationModels of visual neuron function. Quantitative Biology Course Lecture Dan Butts
Models of visual neuron function Quantitative Biology Course Lecture Dan Butts 1 What is the neural code"? 11 10 neurons 1,000-10,000 inputs Electrical activity: nerve impulses 2 ? How does neural activity
More informationNoise in attractor networks in the brain produced by graded firing rate representations
Noise in attractor networks in the brain produced by graded firing rate representations Tristan J. Webb, University of Warwick, Complexity Science, Coventry CV4 7AL, UK Edmund T. Rolls, Oxford Centre for
More informationShunting Inhibition Does Not Have a Divisive Effect on Firing Rates
Communicated by Anthony Zador Shunting Inhibition Does Not Have a Divisive Effect on Firing Rates Gary R. Holt Christof Koch Computation and Neural Systems Program, California Institute of Technology,
More informationSynaptic Transmission: Ionic and Metabotropic
Synaptic Transmission: Ionic and Metabotropic D. Purves et al. Neuroscience (Sinauer Assoc.) Chapters 5, 6, 7. C. Koch. Biophysics of Computation (Oxford) Chapter 4. J.G. Nicholls et al. From Neuron to
More informationResonant synchronization of heterogeneous inhibitory networks
Cerebellar oscillations: Anesthetized rats Transgenic animals Recurrent model Review of literature: γ Network resonance Life simulations Resonance frequency Conclusion Resonant synchronization of heterogeneous
More informationSpatial and temporal coding in an olfaction-inspired network model
Spatial and temporal coding in an olfaction-inspired network model Jeffrey Groff 1, Corrie Camalier 2, Cindy Chiu 3, Ian Miller 4 and Geraldine Wright 5 1 Department of Mathematics, College of William
More informationSTDP enhances synchrony in feedforward network
1 1 of 10 STDP enhances synchrony in feedforward network STDP strengthens/weakens synapses driving late/early-spiking cells [Laurent07] Olfactory neurons' spikes phase-lock (~2ms) to a 20Hz rhythm. STDP
More informationNeuron Phase Response
BioE332A Lab 4, 2007 1 Lab 4 February 2, 2007 Neuron Phase Response In this lab, we study the effect of one neuron s spikes on another s, combined synapse and neuron behavior. In Lab 2, we characterized
More informationHierarchical dynamical models of motor function
ARTICLE IN PRESS Neurocomputing 70 (7) 975 990 www.elsevier.com/locate/neucom Hierarchical dynamical models of motor function S.M. Stringer, E.T. Rolls Department of Experimental Psychology, Centre for
More informationActive Control of Spike-Timing Dependent Synaptic Plasticity in an Electrosensory System
Active Control of Spike-Timing Dependent Synaptic Plasticity in an Electrosensory System Patrick D. Roberts and Curtis C. Bell Neurological Sciences Institute, OHSU 505 N.W. 185 th Avenue, Beaverton, OR
More informationOutline. Animals: Nervous system. Neuron and connection of neurons. Key Concepts:
Animals: Nervous system Neuron and connection of neurons Outline 1. Key concepts 2. An Overview and Evolution 3. Human Nervous System 4. The Neurons 5. The Electrical Signals 6. Communication between Neurons
More informationSample Lab Report 1 from 1. Measuring and Manipulating Passive Membrane Properties
Sample Lab Report 1 from http://www.bio365l.net 1 Abstract Measuring and Manipulating Passive Membrane Properties Biological membranes exhibit the properties of capacitance and resistance, which allow
More informationIntroduction to Computational Neuroscience
Introduction to Computational Neuroscience Lecture 6: Single neuron models Lesson Title 1 Introduction 2 Structure and Function of the NS 3 Windows to the Brain 4 Data analysis I 5 Data analysis II 6 Single
More informationNeurobiology: The nerve cell. Principle and task To use a nerve function model to study the following aspects of a nerve cell:
Principle and task To use a nerve function model to study the following aspects of a nerve cell: INTRACELLULAR POTENTIAL AND ACTION POTENTIAL Comparison between low and high threshold levels Comparison
More informationIntroduction to Computational Neuroscience
Introduction to Computational Neuroscience Lecture 7: Network models Lesson Title 1 Introduction 2 Structure and Function of the NS 3 Windows to the Brain 4 Data analysis 5 Data analysis II 6 Single neuron
More informationChapter 2--Introduction to the Physiology of Perception
Chapter 2--Introduction to the Physiology of Perception Student: 1. Our perception of the environment depends on A. the properties of the objects in the environment. B. the properties of the electrical
More informationPSY 215 Lecture 3 (1/19/2011) (Synapses & Neurotransmitters) Dr. Achtman PSY 215
Corrections: None needed. PSY 215 Lecture 3 Topic: Synapses & Neurotransmitters Chapters 2 & 3, pages 40-57 Lecture Notes: SYNAPSES & NEUROTRANSMITTERS, CHAPTER 3 Action Potential (above diagram found
More informationChapter 11 Introduction to the Nervous System and Nervous Tissue Chapter Outline
Chapter 11 Introduction to the Nervous System and Nervous Tissue Chapter Outline Module 11.1 Overview of the Nervous System (Figures 11.1-11.3) A. The nervous system controls our perception and experience
More informationAll questions below pertain to mandatory material: all slides, and mandatory homework (if any).
ECOL 182 Spring 2008 Dr. Ferriere s lectures Lecture 6: Nervous system and brain Quiz Book reference: LIFE-The Science of Biology, 8 th Edition. http://bcs.whfreeman.com/thelifewire8e/ All questions below
More informationModel neurons!!!!synapses!
Model neurons ynapses uggested reading: Chapter 5.8 in Dayan,. & Abbott, L., Theoretical Neuroscience, MIT ress, 200. Model neurons: ynapse Contents: ynapses ynaptic input into the RC-circuit pike-rate
More informationUsing An Expanded Morris-Lecar Model to Determine Neuronal Dynamics In the Event of Traumatic Brain Injury
Using An Expanded Morris-Lecar Model to Determine Neuronal Dynamics In the Event of Traumatic Brain Injury Ryan W. Tam rwtam@ucsd.edu Department of Bioengineering University of California San Diego La
More informationPHGY 210,2,4 - Physiology SENSORY PHYSIOLOGY. Martin Paré
PHGY 210,2,4 - Physiology SENSORY PHYSIOLOGY Martin Paré Associate Professor of Physiology & Psychology pare@biomed.queensu.ca http://brain.phgy.queensu.ca/pare PHGY 210,2,4 - Physiology SENSORY PHYSIOLOGY
More informationImplantable Microelectronic Devices
ECE 8803/4803 Implantable Microelectronic Devices Fall - 2015 Maysam Ghovanloo (mgh@gatech.edu) School of Electrical and Computer Engineering Georgia Institute of Technology 2015 Maysam Ghovanloo 1 Outline
More informationInhibition: Effects of Timing, Time Scales and Gap Junctions
Inhibition: Effects of Timing, Time Scales and Gap Junctions I. Auditory brain stem neurons and subthreshold integ n. Fast, precise (feed forward) inhibition shapes ITD tuning. Facilitating effects of
More informationA developmental learning rule for coincidence. tuning in the barn owl auditory system. Wulfram Gerstner, Richard Kempter J.
A developmental learning rule for coincidence tuning in the barn owl auditory system Wulfram Gerstner, Richard Kempter J.Leo van Hemmen Institut fur Theoretische Physik, Physik-Department der TU Munchen
More informationHuman Brain and Senses
Human Brain and Senses Outline for today Levels of analysis Basic structure of neurons How neurons communicate Basic structure of the nervous system Levels of analysis Organism Brain Cell Synapses Membrane
More informationEE 791 Lecture 2 Jan 19, 2015
EE 791 Lecture 2 Jan 19, 2015 Action Potential Conduction And Neural Organization EE 791-Lecture 2 1 Core-conductor model: In the core-conductor model we approximate an axon or a segment of a dendrite
More informationMechanisms of stimulus feature selectivity in sensory systems
Mechanisms of stimulus feature selectivity in sensory systems 1. Orientation and direction selectivity in the visual cortex 2. Selectivity to sound frequency in the auditory cortex 3. Feature selectivity
More informationThe inertial-dnf model: spatiotemporal coding on two time scales
Neurocomputing 65 66 (2005) 543 548 www.elsevier.com/locate/neucom The inertial-dnf model: spatiotemporal coding on two time scales Orit Kliper a, David Horn b,, Brigitte Quenet c a School of Computer
More informationLearning in neural networks
http://ccnl.psy.unipd.it Learning in neural networks Marco Zorzi University of Padova M. Zorzi - European Diploma in Cognitive and Brain Sciences, Cognitive modeling", HWK 19-24/3/2006 1 Connectionist
More informationWinter 2017 PHYS 178/278 Project Topics
Winter 2017 PHYS 178/278 Project Topics 1. Recurrent Neural Integrator Network Model for Horizontal Eye Position Stability of the Memory of Eye Position in a Recurrent Network of Conductance- Based Model
More informationNeural response time integration subserves. perceptual decisions - K-F Wong and X-J Wang s. reduced model
Neural response time integration subserves perceptual decisions - K-F Wong and X-J Wang s reduced model Chris Ayers and Narayanan Krishnamurthy December 15, 2008 Abstract A neural network describing the
More informationShadowing and Blocking as Learning Interference Models
Shadowing and Blocking as Learning Interference Models Espoir Kyubwa Dilip Sunder Raj Department of Bioengineering Department of Neuroscience University of California San Diego University of California
More informationCAS Seminar - Spiking Neurons Network (SNN) Jakob Kemi ( )
CAS Seminar - Spiking Neurons Network (SNN) Jakob Kemi (820622-0033) kemiolof@student.chalmers.se November 20, 2006 Introduction Biological background To be written, lots of good sources. Background First
More informationQuestions Addressed Through Study of Behavioral Mechanisms (Proximate Causes)
Jan 28: Neural Mechanisms--intro Questions Addressed Through Study of Behavioral Mechanisms (Proximate Causes) Control of behavior in response to stimuli in environment Diversity of behavior: explain the
More informationNervous System. 2. Receives information from the environment from CNS to organs and glands. 1. Relays messages, processes info, analyzes data
Nervous System 1. Relays messages, processes info, analyzes data 2. Receives information from the environment from CNS to organs and glands 3. Transmits impulses from CNS to muscles and glands 4. Transmits
More informationLESSON 3.3 WORKBOOK. Why does applying pressure relieve pain? Workbook. Postsynaptic potentials
Depolarize to decrease the resting membrane potential. Decreasing membrane potential means that the membrane potential is becoming more positive. Excitatory postsynaptic potentials (EPSP) graded postsynaptic
More informationDynamics of Hodgkin and Huxley Model with Conductance based Synaptic Input
Proceedings of International Joint Conference on Neural Networks, Dallas, Texas, USA, August 4-9, 2013 Dynamics of Hodgkin and Huxley Model with Conductance based Synaptic Input Priyanka Bajaj and Akhil
More informationWhy is our capacity of working memory so large?
LETTER Why is our capacity of working memory so large? Thomas P. Trappenberg Faculty of Computer Science, Dalhousie University 6050 University Avenue, Halifax, Nova Scotia B3H 1W5, Canada E-mail: tt@cs.dal.ca
More informationASSOCIATIVE MEMORY AND HIPPOCAMPAL PLACE CELLS
International Journal of Neural Systems, Vol. 6 (Supp. 1995) 81-86 Proceedings of the Neural Networks: From Biology to High Energy Physics @ World Scientific Publishing Company ASSOCIATIVE MEMORY AND HIPPOCAMPAL
More informationCommunication within a Neuron
Neuronal Communication, Ph.D. Communication within a Neuron Measuring Electrical Potentials of Axons The Membrane Potential The Action Potential Conduction of the Action Potential 1 The withdrawal reflex
More informationCHARACTERIZING NEUROTRANSMITTER RECEPTOR ACTIVATION WITH A PERTURBATION BASED DECOMPOSITION METHOD. A Thesis. presented to
CHARACTERIZING NEUROTRANSMITTER RECEPTOR ACTIVATION WITH A PERTURBATION BASED DECOMPOSITION METHOD A Thesis presented to the Faculty of California Polytechnic State University, San Luis Obispo In Partial
More informationA Connectionist Model based on Physiological Properties of the Neuron
Proceedings of the International Joint Conference IBERAMIA/SBIA/SBRN 2006-1st Workshop on Computational Intelligence (WCI 2006), Ribeirão Preto, Brazil, October 23 28, 2006. CD-ROM. ISBN 85-87837-11-7
More informationThe Ever-Changing Brain. Dr. Julie Haas Biological Sciences
The Ever-Changing Brain Dr. Julie Haas Biological Sciences Outline 1) Synapses: excitatory, inhibitory, and gap-junctional 2) Synaptic plasticity, and Hebb s postulate 3) Sensory maps and plasticity 4)
More informationNeural coding and information theory: Grandmother cells v. distributed codes
Neural coding and information theory: Grandmother cells v. distributed codes John Collins 1/20 Strengths of computers v. brains Computer Brain Accurate and fast computation Accurate and fast storage Doesn
More informationAnalysis of in-vivo extracellular recordings. Ryan Morrill Bootcamp 9/10/2014
Analysis of in-vivo extracellular recordings Ryan Morrill Bootcamp 9/10/2014 Goals for the lecture Be able to: Conceptually understand some of the analysis and jargon encountered in a typical (sensory)
More informationVS : Systemische Physiologie - Animalische Physiologie für Bioinformatiker. Neuronenmodelle III. Modelle synaptischer Kurz- und Langzeitplastizität
Bachelor Program Bioinformatics, FU Berlin VS : Systemische Physiologie - Animalische Physiologie für Bioinformatiker Synaptische Übertragung Neuronenmodelle III Modelle synaptischer Kurz- und Langzeitplastizität
More informationLESSON 3.3 WORKBOOK. Why does applying pressure relieve pain?
Postsynaptic potentials small changes in voltage (membrane potential) due to the binding of neurotransmitter. Receptor-gated ion channels ion channels that open or close in response to the binding of a
More informationWhat is Neuroinformatics/ Computational Neuroscience? Computational Neuroscience? Computational Neuroscience? Computational Neuroscience?
Computational Neuroscience 1. Introduction. Current Issues: Neural Coding Dr Chris Christodoulou Department of Computer Science University of Cyprus Summer School of Intelligent Systems Tuesday, 3 July
More informationSUPPLEMENTARY INFORMATION
doi:10.1038/nature22324 Effects of photoinhibition on licking Photoinhibition of ALM or thalamus caused only small changes in lick early rates, no response rates, and licking latency. ALM photoinhibition
More informationA toy model of the brain
A toy model of the brain arxiv:q-bio/0405002v [q-bio.nc] 2 May 2004 B. Hoeneisen and F. Pasmay Universidad San Francisco de Quito 30 March 2004 Abstract We have designed a toy brain and have written computer
More informationMulti-Associative Memory in flif Cell Assemblies
Multi-Associative Memory in flif Cell Assemblies Christian R. Huyck (c.huyck@mdx.ac.uk) Kailash Nadh (k.nadh@mdx.ac.uk) School of Engineering and Information Sciences Middlesex University London, UK Abstract
More informationTheory of correlation transfer and correlation structure in recurrent networks
Theory of correlation transfer and correlation structure in recurrent networks Ruben Moreno-Bote Foundation Sant Joan de Déu, Barcelona Moritz Helias Research Center Jülich Theory of correlation transfer
More informationYou submitted this quiz on Sun 19 May :32 PM IST (UTC +0530). You got a score of out of
Feedback Ex6 You submitted this quiz on Sun 19 May 2013 9:32 PM IST (UTC +0530). You got a score of 10.00 out of 10.00. Question 1 What is common to Parkinson, Alzheimer and Autism? Electrical (deep brain)
More informationImperfect Synapses in Artificial Spiking Neural Networks
Imperfect Synapses in Artificial Spiking Neural Networks A thesis submitted in partial fulfilment of the requirements for the Degree of Master of Computer Science by Hayden Jackson University of Canterbury
More informationVisual Motion Perception using Critical Branching Neural Computation
Visual Motion Perception using Critical Branching Neural Computation Janelle K. Szary (jszary@ucmerced.edu) Christopher T. Kello (ckello@ucmerced.edu) Cognitive and Information Science, 5200 North Lake
More informationWhat is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration. Abstract
What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration. J. J. Hopfield Department of Molecular Biology Princeton University, Princeton NJ 08544-1014 Carlos D. Brody
More informationTemporally asymmetric Hebbian learning and neuronal response variability
Neurocomputing 32}33 (2000) 523}528 Temporally asymmetric Hebbian learning and neuronal response variability Sen Song*, L.F. Abbott Volen Center for Complex Systems and Department of Biology, Brandeis
More informationBeyond bumps: Spiking networks that store sets of functions
Neurocomputing 38}40 (2001) 581}586 Beyond bumps: Spiking networks that store sets of functions Chris Eliasmith*, Charles H. Anderson Department of Philosophy, University of Waterloo, Waterloo, Ont, N2L
More informationThe Role of Mitral Cells in State Dependent Olfactory Responses. Trygve Bakken & Gunnar Poplawski
The Role of Mitral Cells in State Dependent Olfactory Responses Trygve akken & Gunnar Poplawski GGN 260 Neurodynamics Winter 2008 bstract Many behavioral studies have shown a reduced responsiveness to
More informationProf. Greg Francis 7/31/15
s PSY 200 Greg Francis Lecture 06 How do you recognize your grandmother? Action potential With enough excitatory input, a cell produces an action potential that sends a signal down its axon to other cells
More informationChapter 4 Neuronal Physiology
Chapter 4 Neuronal Physiology V edit. Pg. 99-131 VI edit. Pg. 85-113 VII edit. Pg. 87-113 Input Zone Dendrites and Cell body Nucleus Trigger Zone Axon hillock Conducting Zone Axon (may be from 1mm to more
More informationEffects of synaptic noise on a neuronal pool model with strong excitatory drive and recurrent inhibition
BioSystems 48 (1998) 113 121 Effects of synaptic noise on a neuronal pool model with strong excitatory drive and recurrent inhibition André Fabio Kohn * Uni ersidade de São Paulo, Escola Politécnica, DEE,
More informationThe storage and recall of memories in the hippocampo-cortical system. Supplementary material. Edmund T Rolls
The storage and recall of memories in the hippocampo-cortical system Supplementary material Edmund T Rolls Oxford Centre for Computational Neuroscience, Oxford, England and University of Warwick, Department
More informationLEARNING AS A PHENOMENON OCCURRING IN A CRITICAL STATE. Gan W. et al., 2000, Neuron High magnification image of cortical
25µ LEARNING AS A PHENOMENON OCCURRING IN A CRITICAL STATE Gan W. et al., 2000, Neuron High magnification image of cortical Neuronal avalanches Beggs & Plenz (J. Neuroscience 2003, 2004) have measured
More informationTHE HISTORY OF NEUROSCIENCE
THE HISTORY OF NEUROSCIENCE BIOLOGICAL ASPECTS OF BEHAVIOR: THE NEURON & NEURAL COMMUNICATION NERVOUS SYSTEM Combined activity of the brain, spinal cord & other nerve fibers Acts as an information processing
More informationThe Role of Coincidence-Detector Neurons in the Reliability and Precision of Subthreshold Signal Detection in Noise
The Role of Coincidence-Detector Neurons in the Reliability and Precision of Subthreshold Signal Detection in Noise Yueling Chen 1,2, Hui Zhang 1, Hengtong Wang 1, Lianchun Yu 1, Yong Chen 1,3 * 1 Institute
More informationOptimal information decoding from neuronal populations with specific stimulus selectivity
Optimal information decoding from neuronal populations with specific stimulus selectivity Marcelo A. Montemurro The University of Manchester Faculty of Life Sciences Moffat Building PO Box 88, Manchester
More informationA COMPETITIVE NETWORK OF SPIKING VLSI NEURONS
A COMPETITIVE NETWORK OF SPIKING VLSI NEURONS Indiveri G., Horiuchi T., Niebur, E., and Douglas, R. Institute of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland Computational Sensorimotor
More informationModeling synaptic facilitation and depression in thalamocortical relay cells
College of William and Mary W&M ScholarWorks Undergraduate Honors Theses Theses, Dissertations, & Master Projects 5-2011 Modeling synaptic facilitation and depression in thalamocortical relay cells Olivia
More informationRunning PyNN Simulations on SpiNNaker
Introduction Running PyNN Simulations on SpiNNaker This manual will introduce you to the basics of using the PyNN neural network language on SpiNNaker neuromorphic hardware. Installation The PyNN toolchain
More informationArtificial Neural Networks (Ref: Negnevitsky, M. Artificial Intelligence, Chapter 6)
Artificial Neural Networks (Ref: Negnevitsky, M. Artificial Intelligence, Chapter 6) BPNN in Practice Week 3 Lecture Notes page 1 of 1 The Hopfield Network In this network, it was designed on analogy of
More informationAmeen Alsaras. Ameen Alsaras. Mohd.Khatatbeh
9 Ameen Alsaras Ameen Alsaras Mohd.Khatatbeh Nerve Cells (Neurons) *Remember: The neural cell consists of: 1-Cell body 2-Dendrites 3-Axon which ends as axon terminals. The conduction of impulse through
More informationRecognition of English Characters Using Spiking Neural Networks
Recognition of English Characters Using Spiking Neural Networks Amjad J. Humaidi #1, Thaer M. Kadhim *2 Control and System Engineering, University of Technology, Iraq, Baghdad 1 601116@uotechnology.edu.iq
More informationSpiking neural network simulator: User s Guide
Spiking neural network simulator: User s Guide Version 0.55: December 7 2004 Leslie S. Smith, Department of Computing Science and Mathematics University of Stirling, Stirling FK9 4LA, Scotland lss@cs.stir.ac.uk
More informationBIONB/BME/ECE 4910 Neuronal Simulation Assignments 1, Spring 2013
BIONB/BME/ECE 4910 Neuronal Simulation Assignments 1, Spring 2013 Tutorial Assignment Page Due Date Week 1/Assignment 1: Introduction to NIA 1 January 28 The Membrane Tutorial 9 Week 2/Assignment 2: Passive
More informationComputational analysis of epilepsy-associated genetic mutations
Computational analysis of epilepsy-associated genetic mutations Alberto De Marchi and Ricardo Cervantes Casiano August 5, 27 Abstract In the central nervous system, the elementary processing unit are the
More informationAdaptive leaky integrator models of cerebellar Purkinje cells can learn the clustering of temporal patterns
Neurocomputing 26}27 (1999) 271}276 Adaptive leaky integrator models of cerebellar Purkinje cells can learn the clustering of temporal patterns Volker Steuber*, David J. Willshaw Centre for Cognitive Science,
More informationTheta sequences are essential for internally generated hippocampal firing fields.
Theta sequences are essential for internally generated hippocampal firing fields. Yingxue Wang, Sandro Romani, Brian Lustig, Anthony Leonardo, Eva Pastalkova Supplementary Materials Supplementary Modeling
More informationLevodopa vs. deep brain stimulation: computational models of treatments for Parkinson's disease
Levodopa vs. deep brain stimulation: computational models of treatments for Parkinson's disease Abstract Parkinson's disease (PD) is a neurodegenerative disease affecting the dopaminergic neurons of the
More information