Biological Modeling of Neural Networks:
|
|
|
- Todd Briggs
- 5 years ago
- Views:
Transcription
1 Week 14 Dynamics and Plasticity 14.1 Reservoir computing - Review:Random Networks - Computing with rich dynamics Biological Modeling of Neural Networks: 14.2 Random Networks - stationary state - chaos 14.3 Stability optimized circuits - application to motor data Week 14 Dynamics and Plasticity Synaptic plasticity - Hebbian Wulfram Gerstner EPFL, Lausanne, Switzerland - Reward-modulated Helping Humans - oscillations - network states
2 Week 14-part 1: Review: The brain is complex Neuronal Dynamics Brain dynamics is complex 1mm neurons 3 km wire motor cortex frontal cortex to motor output
3 Week 14-part 1: Review: The brain is complex Neuronal Dynamics Brain dynamics is complex motor -Complex internal dynamics -Memory -Response to inputs -Decision making -Non-stationary -Movement planning -More than one activity value cortex to motor output frontal cortex
4 Week 14-part 1: Reservoir computing Liquid Computing/Reservoir Computing: exploit rich brain dynamics Maass et al. 2002, Jaeger and Haas, 2004 Review: Maass and Buonomano, Stream of sensory inputs Readout 1 Readout 2
5 Week 14-part 1: Reservoir computing - calculcate 3 4 See Maass et al if-condition on 1
6 Week 14-part 1: Rich dynamics Rich neuronal dynamics Experiments of Churchland et al Churchland et al See also: Shenoy et al Modeling Hennequin et al. 2014, See also: Maass et al. 2002, Sussillo and Abbott, 2009 Laje and Buonomano, 2012 Shenoy et al., 2011
7 Week 14-part 1: Rich neuronal dynamics: a wish list -Long transients -Reliable (non-chaotic) -Rich dynamics (non-trivial) -Compatible with neural data (excitation/inhibition) -Plausible plasticity rules
8 Week 14 Dynamics and Plasticity 14.1 Reservoir computing - Review:Random Networks - Computing with rich dynamics Biological Modeling of Neural Networks: 14.2 Random Networks - rate model - stationary state and chaos 14.3 Hebbian Plasticity - excitatory synapses Week 14 Dynamics and Plasticity - inhibitory synapses Reward-modulated plasticity Wulfram Gerstner EPFL, Lausanne, Switzerland - free solution Helping Humans - time dependent activity - network states
9 Week 14-part 2: Review: microscopic vs. macroscopic A n (t) I(t)
10 Week 14-part 2: Review: Random coupling excitation Homogeneous network: inhibition -each neuron receives input from k neurons in network -each neuron receives the same (mean) external input
11 Week 14-part 2: Review: integrate-and-fire/stochastic spike arrival Stochastic spike arrival: excitation, total rate Re inhibition, total rate Ri Synaptic current pulses d dt u ( u u eq ) R k, f q e ( t t f k ) k', f ' q i ( t t f ' k' ) EPSC IPSC d dt u mean ( u u ) R I ( t) ( t) eq Firing times: Threshold crossing u u 0 Langevin equation, Ornstein Uhlenbeck process Fokker-Planck equation
12 Week 14-part 2: Dynamics in Rate Networks F-I curve of rate neuron d r r F ( w r ) dt i i ij j j Fixed point with F(0)=0 Slope 1 r i 0 Suppose: d 1 F '(0) F( x 0) dx stable unstable Suppose 1 dimension d x x F ( w x ) dt
13 Exercise 1: Stability of fixed point Next lecture: d x x F ( w x ) dt 9h43 Fixed point with F(0)=0 x 0 Suppose: d 1 F '(0) F( x 0) dx Suppose 1 dimension Calculate stability, d x x F ( w x ) dt take w as parameter
14 Week 14-part 2: Dynamics in Rate Networks Blackboard: Two dimensions! d r r F ( w r ) dt i i ij j j Fixed point with F(0)=0 r i 0 Suppose: d 1 F '(0) F( x 0) dx Suppose 1 dimension stable unstable d x x F ( w x ) dt w<1 w>1
15 Week 14-part 2: Dynamics in RANDOM Rate Networks d r r F ( w r ) dt i i ij j j Fixed point: r i 0 Random, 10 percent connectivity d 1 F '(0) F( x 0) dx Chaotic dynamics: stable unstable Sompolinksy et al Re( ) 1 Re( ) 1 (and many others: Amari,...
16 Unstable dynamics and Chaos d r r F ( w r ) ( t ) dt i i ij j i j chaos Rajan and Abbott, 2006 Image: Ostojic, Nat.Neurosci, 2014 Image: Hennequin et al. Neuron, 2014
17 Week 14-part 2: Dynamics in Random SPIKING Networks d u u w ( t t f ) i i ij j dt j f Firing times: Threshold crossing Image: Ostojic, Nat.Neurosci, 2014
18 Week 14-part 2: Stationary activity: two different regimes Switching/bursts long autocorrelations: Stable rate fixed point, microscopic chaos Rate chaos Re( ) 1 Ostojic, Nat.Neurosci, 2014
19 Week 14-part 2: Rich neuronal dynamics: a wish list -Long transients -Reliable (non-chaotic) -Rich dynamics (non-trivial) -Compatible with neural data (excitation/inhibition) -Plausible plasticity rules
20 Week 14 Dynamics and Plasticity 14.1 Reservoir computing - Review:Random Networks - Computing with rich dynamics Biological Modeling of Neural Networks: 14.2 Random Networks - stationary state - chaos 14.3 Stability optimized circuits - application to motor data Week 14 Dynamics and Plasticity Synaptic plasticity - Hebbian Wulfram Gerstner EPFL, Lausanne, Switzerland - Reward-modulated Helping Humans - time dependent activity - network states
21 Week 14-part 3: Plasticity-optimized circuit Re( ) 1 Re( ) 1 Image: Hennequin et al. Neuron, 2014
22 Optimal control of transient dynamics in balanced networks supports generation of complex movements Hennequin et al. 2014,
23 Random stability-optimized circuit (SOC) Random connectivity
24 Week 14-part 3: Random Plasticity-optimized circuit andom
25 Week 14-part 3: Random Plasticity-optimized circuit study slope 1/linear theory duration of transients slope 1/linear theory a1= slowest = most amplified F-I curve of rate neuron
26 Week 14-part 3: Random Plasticity-optimized circuit F-I curve slope 1/linear theory of rate neuron a1= slowest = most amplified
27 Optimal control of transient dynamics in balanced networks supports generation of complex movements Hennequin et al. NEURON 2014,
28 Week 14-part 3: Application to motor cortex: data and model Churchland et al. 2010/2012 Hennequin et al. 2014
29 Week 14-part 3: Random Plasticity-optimized circuit Comparison: weak random Hennequin et al. 2014
30 Week 14-part 3: Stability optimized SPIKING network Classic sparse random connectivity (Brunel 2000) Random connections, fast Stabilizy-optimized random connections Fast AMPA slow NMDA distal connections, slow, (Branco&Hausser, 2011) structured Overall: 20% connectivity excitatory LIF = 200 pools of 60 neurons 3000 inhibitory LIF = 200 pools of 15 neurons
31 Week 14-part 3: Stability optimized SPIKING network Spontaneous Classic sparse random connectivity (Brunel 2000) Neuron 1 firing rate Neuron 2 Neuron 3 Single neuron different initial conditions Hennequin et al. 2014
32 Week 14-part 3: Stability optimized SPIKING network Classic sparse random connectivity (Brunel 2000) Hennequin et al. 2014
33 Week 14-part 3: Rich neuronal dynamics: a result list -Long transients -Reliable (non-chaotic) -Rich dynamics (non-trivial) -Compatible with neural data (excitation/inhibition) -Plausible plasticity rules
34 Week 14 Dynamics and Plasticity 14.1 Reservoir computing - Review:Random Networks - Computing with rich dynamics Biological Modeling of Neural Networks: 14.2 Random Networks - stationary state - chaos 14.3 Stability optimized circuits - application to motor data Week 14 Dynamics and Plasticity Synaptic plasticity - Hebbian Wulfram Gerstner EPFL, Lausanne, Switzerland - Reward-modulated Helping Humans - time dependent activity - network states
35 Hebbian Learning = all inputs/all times are equal pre j post i w ij w ij F ( pre, post )
36 Week 14-part 4: STDP = spike-based Hebbian learning Pre-before post: potentiation of synapse Pre-after-post: depression of synapse
37 Modulation of Learning = Hebb+ confirmation confirmation Functional Postulate Useful for learning the important stuff wij F( pre, post, CONFIRM ) local global Many models (and experiments) of synaptic plasticity do not take into account Neuromodulators. Except: e.g. Schultz et al. 1997, Wickens 2002, Izhikevich, 2007; Reymann+Frey 2007; Moncada 2007, Pawlak+Kerr 2008; Pawlak et al. 2010
38 Consolidation of Learning Success/reward Confirmation -Novel -Interesting -Rewarding -Surprising Neuromodulators dopmaine/serotonin/ach write now to long-term memory Crow (1968), Fregnac et al (2010), Izhikevich (2007)
39 Plasticity Stability-optimized curcuits - here: algorithmically tuned BUT - replace by inhibitory plasticity Vogels et al., Science 2011 avoids chaotic blow-up of network avoids blow-up of single neuron (detailed balance) yields stability optimized circuits
40 Plasticity Readout - here: algorithmically tuned BUT Success signal Izhikevich, 2007 Fremaux et al replace by 3-factor plasticity rules
41 Week 14-part 4: Plasticity modulated by reward Dopamine-emitting neurons: Schultz et al., 1997 Izhikevich, 2007 Success signal Fremaux et al Dopamine encodes success= reward expected reward
42 Week 14-part 4: Plasticity modulated by reward
43 Week 14-part 4: STDP = spike-based Hebbian learning Pre-before post: potentiation of synapse
44 Week 14-part 4: Plasticity modulated by reward STDP with pre-before post: potentiation of synapse
45 Week 14-part 4: from spikes to movement How can the readouts encode movement?
46 Week 14-part 5: Population vector coding Population vector coding of movements Schwartz et al. 1988
47 Week 14-part 4: Learning movement trajectories Population vector coding of movements synapses 1 trial =1 second Output to trajectories via population vector coding Single reward at the END of each trial based on similarity with a target trajectory Fremaux et al., J. Neurosci. 2010
48 Performance Week 14-part 4: Learning movement trajectories Fremaux et al. J. Neurosci Quic ktime and a decompress or are needed to s ee this picture. R-STDP LTPonly
49 Week 14-part 4: Plasticity can tune the network and readout Hebbian STDP - inhibitory connections, tuned by 2-factor STDP, for stabilization Vogels et al. Reward-modulated 2011 Success signal Fremaux et al STDP for movement learning - Readout connections, tuned by 3-factor plasticity rule
50 Last Lecture TODAY Exam: - written exam from 16:15-19:00 - miniprojects counts 1/3 towards final grade For written exam: -bring 1 page A5 of own notes/summary -HANDWRITTEN!
51 Nearly the end: what can I improve for the students next year? Integrated exercises? Quizzes? Miniproject? Overall workload?(4 credit course = 6hrs per week) Background/Prerequisites? -Physics students -SV students -Math students Slides? videos?
Lecture 14 Population dynamics and associative memory; stable learning
Lecture 14 Population dynamics and associative memory; stable learning -Introduction -Associative Memory -Dense networks (mean-ield) -Population dynamics and Associative Memory -Discussion Systems or computing
Biological Modeling of Neural Networks
Week 4 part 2: More Detail compartmental models Biological Modeling of Neural Networks Week 4 Reducing detail - Adding detail 4.2. Adding detail - apse -cable equat Wulfram Gerstner EPFL, Lausanne, Switzerland
Neuronal Dynamics: Computational Neuroscience of Single Neurons
Week 5 part 3a :Three definitions of rate code Neuronal Dynamics: Computational Neuroscience of Single Neurons Week 5 Variability and Noise: The question of the neural code Wulfram Gerstner EPFL, Lausanne,
An Introductory Course in Computational Neuroscience
An Introductory Course in Computational Neuroscience Contents Series Foreword Acknowledgments Preface 1 Preliminary Material 1.1. Introduction 1.1.1 The Cell, the Circuit, and the Brain 1.1.2 Physics of
How do synapses transform inputs?
Neurons to networks How do synapses transform inputs? Excitatory synapse Input spike! Neurotransmitter release binds to/opens Na channels Change in synaptic conductance! Na+ influx E.g. AMA synapse! Depolarization
neural networks Balázs B Ujfalussy 17 october, 2016 idegrendszeri modellezés 2016 október 17.
neural networks Balázs B Ujfalussy 17 october, 2016 Hierarchy of the nervous system behaviour idegrendszeri modellezés 1m CNS 10 cm systems 1 cm maps 1 mm networks 100 μm neurons 1 μm synapses 10 nm molecules
Basic elements of neuroelectronics -- membranes -- ion channels -- wiring. Elementary neuron models -- conductance based -- modelers alternatives
Computing in carbon Basic elements of neuroelectronics -- membranes -- ion channels -- wiring Elementary neuron models -- conductance based -- modelers alternatives Wiring neurons together -- synapses
Neuronal Dynamics: Computational Neuroscience of Single Neurons
Week 4 part 5: Nonlinear Integrate-and-Fire Model 4.1 From Hodgkin-Huxley to 2D Neuronal Dynamics: Computational Neuroscience of Single Neurons Week 4 Recing detail: Two-dimensional neuron models Wulfram
Liquid Computing in a Simplified Model of Cortical Layer IV: Learning to Balance a Ball
Liquid Computing in a Simplified Model of Cortical Layer IV: Learning to Balance a Ball Dimitri Probst 1,3, Wolfgang Maass 2, Henry Markram 1, and Marc-Oliver Gewaltig 1 1 Blue Brain Project, École Polytechnique
Consider the following spike trains from two different neurons N1 and N2:
About synchrony and oscillations So far, our discussions have assumed that we are either observing a single neuron at a, or that neurons fire independent of each other. This assumption may be correct in
Synfire Waves in Small Balanced Networks
Synfire Waves in Small Balanced Networks Yuval Aviel 1,3, David Horn 2 and Moshe Abeles 1 1 Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, Israel. 2 School of Physics and
A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback
A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback Robert Legenstein, Dejan Pecevski, Wolfgang Maass Institute for Theoretical Computer Science Graz
CSE/NB 528 Final Lecture: All Good Things Must. CSE/NB 528: Final Lecture
CSE/NB 528 Final Lecture: All Good Things Must 1 Course Summary Where have we been? Course Highlights Where do we go from here? Challenges and Open Problems Further Reading 2 What is the neural code? What
Linking connectivity, dynamics and computations in low-rank recurrent neural networks
Linking connectivity, dynamics and computations in low-rank recurrent neural networks Francesca Mastrogiuseppe 1,2, Srdjan Ostojic 1 * 1 Laboratoire de Neurosciences Cognitives, INSERM U960 and 2 Laboratoire
Effects of Interactive Function Forms and Refractoryperiod in a Self-Organized Critical Model Based on Neural Networks
Commun. Theor. Phys. (Beijing, China) 42 (2004) pp. 121 125 c International Academic Publishers Vol. 42, No. 1, July 15, 2004 Effects of Interactive Function Forms and Refractoryperiod in a Self-Organized
Sampling-based probabilistic inference through neural and synaptic dynamics
Sampling-based probabilistic inference through neural and synaptic dynamics Wolfgang Maass for Robert Legenstein Institute for Theoretical Computer Science Graz University of Technology, Austria Institute
Modelling stochastic neural learning
Modelling stochastic neural learning Computational Neuroscience András Telcs telcs.andras@wigner.mta.hu www.cs.bme.hu/~telcs http://pattern.wigner.mta.hu/participants/andras-telcs Compiled from lectures
Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail
Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail Eleni Vasilaki 1,2 *, Nicolas Frémaux 1, Robert Urbanczik 3, Walter Senn 3, Wulfram Gerstner 1
Decoding. How well can we learn what the stimulus is by looking at the neural responses?
Decoding How well can we learn what the stimulus is by looking at the neural responses? Two approaches: devise explicit algorithms for extracting a stimulus estimate directly quantify the relationship
Tuning tuning curves. So far: Receptive fields Representation of stimuli Population vectors. Today: Contrast enhancment, cortical processing
Tuning tuning curves So far: Receptive fields Representation of stimuli Population vectors Today: Contrast enhancment, cortical processing Firing frequency N 3 s max (N 1 ) = 40 o N4 N 1 N N 5 2 s max
The Mixed States of Associative Memories Realize Unimodal Distribution of Dominance Durations in Multistable Perception
The Mixed States of Associative Memories Realize Unimodal Distribution of Dominance Durations in Multistable Perception Takashi Kanamaru Department of Mechanical Science and ngineering, School of Advanced
A Model for Real-Time Computation in Generic Neural Microcircuits
A Model for Real-Time Computation in Generic Neural Microcircuits Wolfgang Maass, Thomas Natschläger Institute for Theoretical Computer Science Technische Universitaet Graz A-81 Graz, Austria maass, tnatschl
Lecture 11 : Simple Neuron Models. Dr Eileen Nugent
Lecture 11 : Simple Neuron Models Dr Eileen Nugent Reading List Nelson, Biological Physics, Chapter 12 Phillips, PBoC, Chapter 17 Gerstner, Neuronal Dynamics: from single neurons to networks and models
How do biological neurons learn? Insights from computational modelling of
How do biological neurons learn? Insights from computational modelling of neurobiological experiments Lubica Benuskova Department of Computer Science University of Otago, New Zealand Brain is comprised
Decision-making and Weber s law: a neurophysiological model
European Journal of Neuroscience, Vol. 24, pp. 901 916, 2006 doi:10.1111/j.14-9568.2006.04940.x Decision-making and Weber s law: a neurophysiological model Gustavo Deco 1 and Edmund T. Rolls 2 1 Institucio
Biological Modeling of Neural Networks
Week 3 part 1 : Rection of the Hodgkin-Huxley Model 3.1 From Hodgkin-Huxley to 2D Biological Modeling of Neural Netorks - Overvie: From 4 to 2 dimensions - MathDetour 1: Exploiting similarities - MathDetour
Neurophysiology of a VLSI spiking neural network: LANN21
Neurophysiology of a VLSI spiking neural network: LANN21 Stefano Fusi INFN, Sezione Roma I Università di Roma La Sapienza Pza Aldo Moro 2, I-185, Roma fusi@jupiter.roma1.infn.it Paolo Del Giudice Physics
Learning Spatio-Temporally Encoded Pattern Transformations in Structured Spiking Neural Networks 12
Learning Spatio-Temporally Encoded Pattern Transformations in Structured Spiking Neural Networks 12 André Grüning, Brian Gardner and Ioana Sporea Department of Computer Science University of Surrey Guildford,
High-conductance states in a mean-eld cortical network model
Neurocomputing 58 60 (2004) 935 940 www.elsevier.com/locate/neucom High-conductance states in a mean-eld cortical network model Alexander Lerchner a;, Mandana Ahmadi b, John Hertz b a Oersted-DTU, Technical
Phase Response. 1 of of 11. Synaptic input advances (excitatory) or delays (inhibitory) spiking
Printed from the Mathematica Help Browser 1 1 of 11 Phase Response Inward current-pulses decrease a cortical neuron's period (Cat, Layer V). [Fetz93] Synaptic input advances (excitatory) or delays (inhibitory)
Training and spontaneous reinforcement of neuronal assemblies by spike timing
Training and spontaneous reinforcement of neuronal assemblies by spike timing Gabriel Koch Ocker,3,4, Brent Doiron,3 : Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA : Department
Structured reservoir computing with spatiotemporal chaotic attractors
Structured reservoir computing with spatiotemporal chaotic attractors Carlos Lourenço 1,2 1- Faculty of Sciences of the University of Lisbon - Informatics Department Campo Grande, 1749-016 Lisboa - Portugal
1 Balanced networks: Trading speed for noise
Physics 178/278 - David leinfeld - Winter 2017 (Corrected yet incomplete notes) 1 Balanced networks: Trading speed for noise 1.1 Scaling of neuronal inputs An interesting observation is that the subthresold
REAL-TIME COMPUTING WITHOUT STABLE
REAL-TIME COMPUTING WITHOUT STABLE STATES: A NEW FRAMEWORK FOR NEURAL COMPUTATION BASED ON PERTURBATIONS Wolfgang Maass Thomas Natschlager Henry Markram Presented by Qiong Zhao April 28 th, 2010 OUTLINE
Computational Neuroscience. Structure Dynamics Implementation Algorithm Computation - Function
Computational Neuroscience Structure Dynamics Implementation Algorithm Computation - Function Learning at psychological level Classical conditioning Hebb's rule When an axon of cell A is near enough to
Dynamical Constraints on Computing with Spike Timing in the Cortex
Appears in Advances in Neural Information Processing Systems, 15 (NIPS 00) Dynamical Constraints on Computing with Spike Timing in the Cortex Arunava Banerjee and Alexandre Pouget Department of Brain and
Synaptic Plasticity. Introduction. Biophysics of Synaptic Plasticity. Functional Modes of Synaptic Plasticity. Activity-dependent synaptic plasticity:
Synaptic Plasticity Introduction Dayan and Abbott (2001) Chapter 8 Instructor: Yoonsuck Choe; CPSC 644 Cortical Networks Activity-dependent synaptic plasticity: underlies learning and memory, and plays
Large-scale neural modeling
Large-scale neural modeling We re acquiring brain data at an unprecedented rate Dendritic recording Serial Scanning EM Ca ++ imaging Kwabena Boahen Stanford Bioengineering boahen@stanford.edu Goal: Link
Probabilistic Models in Theoretical Neuroscience
Probabilistic Models in Theoretical Neuroscience visible unit Boltzmann machine semi-restricted Boltzmann machine restricted Boltzmann machine hidden unit Neural models of probabilistic sampling: introduction
Supporting Online Material for
www.sciencemag.org/cgi/content/full/319/5869/1543/dc1 Supporting Online Material for Synaptic Theory of Working Memory Gianluigi Mongillo, Omri Barak, Misha Tsodyks* *To whom correspondence should be addressed.
Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity
The Journal of Neuroscience, September 20, 2006 26(38):9673 9682 9673 Behavioral/Systems/Cognitive Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity Jean-Pascal Pfister and Wulfram Gerstner
Single Neuron Dynamics for Retaining and Destroying Network Information?
Single Neuron Dynamics for Retaining and Destroying Network Information? Michael Monteforte, Tatjana Tchumatchenko, Wei Wei & F. Wolf Bernstein Center for Computational Neuroscience and Faculty of Physics
CISC 3250 Systems Neuroscience
CISC 3250 Systems Neuroscience Systems Neuroscience How the nervous system performs computations How groups of neurons work together to achieve intelligence Professor Daniel Leeds dleeds@fordham.edu JMH
Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network
Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network Aditya Gilra 1,* Wulfram Gerstner 1 1 School of Computer and Communication Sciences, and Brain-Mind Institute,
Synaptic Devices and Neuron Circuits for Neuron-Inspired NanoElectronics
Synaptic Devices and Neuron Circuits for Neuron-Inspired NanoElectronics Byung-Gook Park Inter-university Semiconductor Research Center & Department of Electrical and Computer Engineering Seoul National
Brains and Computation
15-883: Computational Models of Neural Systems Lecture 1.1: Brains and Computation David S. Touretzky Computer Science Department Carnegie Mellon University 1 Models of the Nervous System Hydraulic network
THE LOCUST OLFACTORY SYSTEM AS A CASE STUDY FOR MODELING DYNAMICS OF NEUROBIOLOGICAL NETWORKS: FROM DISCRETE TIME NEURONS TO CONTINUOUS TIME NEURONS
1 THE LOCUST OLFACTORY SYSTEM AS A CASE STUDY FOR MODELING DYNAMICS OF NEUROBIOLOGICAL NETWORKS: FROM DISCRETE TIME NEURONS TO CONTINUOUS TIME NEURONS B. QUENET 1 AND G. HORCHOLLE-BOSSAVIT 2 1 Equipe de
Neural Modeling and Computational Neuroscience. Claudio Gallicchio
Neural Modeling and Computational Neuroscience Claudio Gallicchio 1 Neuroscience modeling 2 Introduction to basic aspects of brain computation Introduction to neurophysiology Neural modeling: Elements
Subthreshold cross-correlations between cortical neurons: Areference model with static synapses
Neurocomputing 65 66 (25) 685 69 www.elsevier.com/locate/neucom Subthreshold cross-correlations between cortical neurons: Areference model with static synapses Ofer Melamed a,b, Gilad Silberberg b, Henry
Neuron, Volume 63. Supplemental Data. Generating Coherent Patterns of Activity. from Chaotic Neural Networks. David Sussillo and L.F.
Neuron, Volume 63 Supplemental Data Generating Coherent Patterns of Activity from Chaotic Neural Networks David Sussillo and L.F. Abbott Supplementary Material FORCE Learning without RLS It is possible
Synaptic dynamics. John D. Murray. Synaptic currents. Simple model of the synaptic gating variable. First-order kinetics
Synaptic dynamics John D. Murray A dynamical model for synaptic gating variables is presented. We use this to study the saturation of synaptic gating at high firing rate. Shunting inhibition and the voltage
Learning at the edge of chaos : Temporal Coupling of Spiking Neurons Controller for Autonomous Robotic
Learning at the edge of chaos : Temporal Coupling of Spiking Neurons Controller for Autonomous Robotic Hédi Soula and Aravind Alwan and Guillaume Beslon ALAB Team of Prisma Lab Computer Science Dept National
Single neuron models. L. Pezard Aix-Marseille University
Single neuron models L. Pezard Aix-Marseille University Biophysics Biological neuron Biophysics Ionic currents Passive properties Active properties Typology of models Compartmental models Differential
Effects of synaptic conductance on the voltage distribution and firing rate of spiking neurons
PHYSICAL REVIEW E 69, 051918 (2004) Effects of synaptic conductance on the voltage distribution and firing rate of spiking neurons Magnus J. E. Richardson* Laboratory of Computational Neuroscience, Brain
Fast and exact simulation methods applied on a broad range of neuron models
Fast and exact simulation methods applied on a broad range of neuron models Michiel D Haene michiel.dhaene@ugent.be Benjamin Schrauwen benjamin.schrauwen@ugent.be Ghent University, Electronics and Information
Sensory Encoding of Smell in the Olfactory System of Drosophila
Sensory Encoding of Smell in the Olfactory System of Drosophila (reviewing Olfactory Information Processing in Drosophila by Masse et al, 2009) Ben Cipollini COGS 160 May 11, 2010 Smell Drives Our Behavior...
arxiv: v1 [q-bio.nc] 13 Apr 2012
![arxiv: v1 [q-bio.nc] 13 Apr 2012](/thumbs/82/86046073.jpg "arxiv: v1 [q-bio.nc] 13 Apr 2012")
onnormal amplification in random balanced neuronal networks Guillaume Hennequin Tim P. Vogels Wulfram Gerstner School of Computer and Communication Sciences and Brain Mind Institute Ecole Polytechnique
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity Bernhard Nessler 1 *, Michael Pfeiffer 1,2, Lars Buesing 1, Wolfgang Maass 1 1 Institute for Theoretical
(Feed-Forward) Neural Networks Dr. Hajira Jabeen, Prof. Jens Lehmann
(Feed-Forward) Neural Networks 2016-12-06 Dr. Hajira Jabeen, Prof. Jens Lehmann Outline In the previous lectures we have learned about tensors and factorization methods. RESCAL is a bilinear model for
Causality and communities in neural networks
Causality and communities in neural networks Leonardo Angelini, Daniele Marinazzo, Mario Pellicoro, Sebastiano Stramaglia TIRES-Center for Signal Detection and Processing - Università di Bari, Bari, Italy
How to read a burst duration code
Neurocomputing 58 60 (2004) 1 6 www.elsevier.com/locate/neucom How to read a burst duration code Adam Kepecs a;, John Lisman b a Cold Spring Harbor Laboratory, Marks Building, 1 Bungtown Road, Cold Spring
Addressing Challenges in Neuromorphic Computing with Memristive Synapses
Addressing Challenges in Neuromorphic Computing with Memristive Synapses Vishal Saxena 1, Xinyu Wu 1 and Maria Mitkova 2 1 Analog Mixed-Signal and Photonic IC (AMPIC) Lab 2 Nanoionic Materials and Devices
Synaptic plasticity in neuromorphic hardware. Stefano Fusi Columbia University
Synaptic plasticity in neuromorphic hardware Stefano Fusi Columbia University The memory problem Several efficient memory models assume that the synaptic dynamic variables are unbounded, or can be modified
Lecture 4: Feed Forward Neural Networks
Lecture 4: Feed Forward Neural Networks Dr. Roman V Belavkin Middlesex University BIS4435 Biological neurons and the brain A Model of A Single Neuron Neurons as data-driven models Neural Networks Training
Fast neural network simulations with population density methods
Fast neural network simulations with population density methods Duane Q. Nykamp a,1 Daniel Tranchina b,a,c,2 a Courant Institute of Mathematical Science b Department of Biology c Center for Neural Science
Activity Driven Adaptive Stochastic. Resonance. Gregor Wenning and Klaus Obermayer. Technical University of Berlin.
Activity Driven Adaptive Stochastic Resonance Gregor Wenning and Klaus Obermayer Department of Electrical Engineering and Computer Science Technical University of Berlin Franklinstr. 8/9, 187 Berlin fgrewe,obyg@cs.tu-berlin.de
of the dynamics. There is a competition between the capacity of the network and the stability of the
Special Issue on the Role and Control of Random Events in Biological Systems c World Scientic Publishing Company LEARNING SYNFIRE CHAINS: TURNING NOISE INTO SIGNAL JOHN HERTZ and ADAM PRUGEL-BENNETT y
STDP Learning of Image Patches with Convolutional Spiking Neural Networks
STDP Learning of Image Patches with Convolutional Spiking Neural Networks Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma College of Information and Computer Sciences University of Massachusetts Amherst
Introduction. Previous work has shown that AER can also be used to construct largescale networks with arbitrary, configurable synaptic connectivity.
Introduction The goal of neuromorphic engineering is to design and implement microelectronic systems that emulate the structure and function of the brain. Address-event representation (AER) is a communication
A gradient descent rule for spiking neurons emitting multiple spikes
A gradient descent rule for spiking neurons emitting multiple spikes Olaf Booij a, Hieu tat Nguyen a a Intelligent Sensory Information Systems, University of Amsterdam, Faculty of Science, Kruislaan 403,
Stationary Bumps in Networks of Spiking Neurons
LETTER Communicated by Misha Tsodyks Stationary Bumps in Networks of Spiking Neurons Carlo R. Laing Carson C. Chow Department of Mathematics, University of Pittsburgh, Pittsburgh PA 1526, U.S.A. We examine
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity Bernhard Nessler 1,, Michael Pfeiffer 2,1, Lars Buesing 1, Wolfgang Maass 1 nessler@igi.tugraz.at,
Machine Learning. Neural Networks. (slides from Domingos, Pardo, others)
Machine Learning Neural Networks (slides from Domingos, Pardo, others) For this week, Reading Chapter 4: Neural Networks (Mitchell, 1997) See Canvas For subsequent weeks: Scaling Learning Algorithms toward
Effects of Interactive Function Forms in a Self-Organized Critical Model Based on Neural Networks
Commun. Theor. Phys. (Beijing, China) 40 (2003) pp. 607 613 c International Academic Publishers Vol. 40, No. 5, November 15, 2003 Effects of Interactive Function Forms in a Self-Organized Critical Model
EXAMINING HETEROGENEOUS WEIGHT PERTURBATIONS IN NEURAL NETWORKS WITH SPIKE-TIMING-DEPENDENT PLASTICITY
EXAMINING HETEROGENEOUS WEIGHT PERTURBATIONS IN NEURAL NETWORKS WITH SPIKE-TIMING-DEPENDENT PLASTICITY by Colin Bredenberg University of Pittsburgh, 2017 Submitted to the Graduate Faculty of the Department
arxiv: v1 [cs.lg] 9 Dec 2016
![arxiv: v1 [cs.lg] 9 Dec 2016](/thumbs/88/114685115.jpg "arxiv: v1 [cs.lg] 9 Dec 2016")
Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity arxiv:1612.03214v1 [cs.lg] 9 Dec 2016 Thomas Mesnard École Normale Supérieure Paris, France thomas.mesnard@ens.fr
Self-organized Criticality and Synchronization in a Pulse-coupled Integrate-and-Fire Neuron Model Based on Small World Networks
Commun. Theor. Phys. (Beijing, China) 43 (2005) pp. 466 470 c International Academic Publishers Vol. 43, No. 3, March 15, 2005 Self-organized Criticality and Synchronization in a Pulse-coupled Integrate-and-Fire
Basic elements of neuroelectronics -- membranes -- ion channels -- wiring
Computing in carbon Basic elements of neuroelectronics -- membranes -- ion channels -- wiring Elementary neuron models -- conductance based -- modelers alternatives Wires -- signal propagation -- processing
Model neurons!!poisson neurons!
Model neurons!!poisson neurons! Suggested reading:! Chapter 1.4 in Dayan, P. & Abbott, L., heoretical Neuroscience, MI Press, 2001.! Model neurons: Poisson neurons! Contents: Probability of a spike sequence
Leo Kadanoff and 2d XY Models with Symmetry-Breaking Fields. renormalization group study of higher order gradients, cosines and vortices
Leo Kadanoff and d XY Models with Symmetry-Breaking Fields renormalization group study of higher order gradients, cosines and vortices Leo Kadanoff and Random Matrix Theory Non-Hermitian Localization in
Patterns, Memory and Periodicity in Two-Neuron Delayed Recurrent Inhibitory Loops
Math. Model. Nat. Phenom. Vol. 5, No. 2, 2010, pp. 67-99 DOI: 10.1051/mmnp/20105203 Patterns, Memory and Periodicity in Two-Neuron Delayed Recurrent Inhibitory Loops J. Ma 1 and J. Wu 2 1 Department of
Information Theory and Neuroscience II
John Z. Sun and Da Wang Massachusetts Institute of Technology October 14, 2009 Outline System Model & Problem Formulation Information Rate Analysis Recap 2 / 23 Neurons Neuron (denoted by j) I/O: via synapses
Temporal whitening by power-law adaptation in neocortical neurons
Temporal whitening by power-law adaptation in neocortical neurons Christian Pozzorini, Richard Naud, Skander Mensi and Wulfram Gerstner School of Computer and Communication Sciences and School of Life
DEVS Simulation of Spiking Neural Networks
DEVS Simulation of Spiking Neural Networks Rene Mayrhofer, Michael Affenzeller, Herbert Prähofer, Gerhard Höfer, Alexander Fried Institute of Systems Science Systems Theory and Information Technology Johannes
How to do backpropagation in a brain
How to do backpropagation in a brain Geoffrey Hinton Canadian Institute for Advanced Research & University of Toronto & Google Inc. Prelude I will start with three slides explaining a popular type of deep
Math in systems neuroscience. Quan Wen
Math in systems neuroscience Quan Wen Human brain is perhaps the most complex subject in the universe 1 kg brain 10 11 neurons 180,000 km nerve fiber 10 15 synapses 10 18 synaptic proteins Multiscale
Stability and amplification in plastic cortical circuits
Stability and amplification in plastic cortical circuits THÈSE N O 5585 (213) PRÉSENTÉE le 1 er mai 213 À LA FACULTÉ INFORMATIQUE ET COMMUNICATIONS LABORATOIRE DE CALCUL NEUROMIMÉTIQUE (IC/SV) PROGRAMME
Dynamical systems in neuroscience. Pacific Northwest Computational Neuroscience Connection October 1-2, 2010
Dynamical systems in neuroscience Pacific Northwest Computational Neuroscience Connection October 1-2, 2010 What do I mean by a dynamical system? Set of state variables Law that governs evolution of state
Plasticity and Learning
Chapter 8 Plasticity and Learning 8.1 Introduction Activity-dependent synaptic plasticity is widely believed to be the basic phenomenon underlying learning and memory, and it is also thought to play a
Computational Neuroscience Introduction Day
Computational Neuroscience Introduction Day 9.30am Introduction (C.Machens) 10am M1 (C.Machens) 10.15am M2 (V. Hakim) 10.40 break 11.00 Matching Law (S. Deneve) 14.00 Student presentations 11.20 Rescorla-Wagner
Analog CMOS Circuits Implementing Neural Segmentation Model Based on Symmetric STDP Learning
Analog CMOS Circuits Implementing Neural Segmentation Model Based on Symmetric STDP Learning Gessyca Maria Tovar, Eric Shun Fukuda,TetsuyaAsai, Tetsuya Hirose, and Yoshihito Amemiya Hokkaido University,
Applied Physics and by courtesy, Neurobiology and EE
Information theoretic limits on the memory capacity of neuronal and synaptic networks. Surya Ganguli Applied Physics and by courtesy, Neurobiology and EE Stanford The Nature and Scope of Theoretical Neuroscience
Effects of refractory periods in the dynamics of a diluted neural network
Effects of refractory periods in the dynamics of a diluted neural network F. A. Tamarit, 1, * D. A. Stariolo, 2, * S. A. Cannas, 2, *, and P. Serra 2, 1 Facultad de Matemática, Astronomía yfísica, Universidad
Mathematical Methods for Neurosciences. ENS - Master MVA Paris 6 - Master Maths-Bio ( )
Mathematical Methods for Neurosciences. ENS - Master MVA Paris 6 - Master Maths-Bio (2014-2015) Etienne Tanré - Olivier Faugeras INRIA - Team Tosca November 26th, 2014 E. Tanré (INRIA - Team Tosca) Mathematical
Neural Coding: Integrate-and-Fire Models of Single and Multi-Neuron Responses
Neural Coding: Integrate-and-Fire Models of Single and Multi-Neuron Responses Jonathan Pillow HHMI and NYU http://www.cns.nyu.edu/~pillow Oct 5, Course lecture: Computational Modeling of Neuronal Systems
Hopfield Neural Network and Associative Memory. Typical Myelinated Vertebrate Motoneuron (Wikipedia) Topic 3 Polymers and Neurons Lecture 5
Hopfield Neural Network and Associative Memory Typical Myelinated Vertebrate Motoneuron (Wikipedia) PHY 411-506 Computational Physics 2 1 Wednesday, March 5 1906 Nobel Prize in Physiology or Medicine.
arxiv: v1 [q-bio.nc] 1 Jun 2014
![arxiv: v1 [q-bio.nc] 1 Jun 2014](/thumbs/92/108475602.jpg "arxiv: v1 [q-bio.nc] 1 Jun 2014")
1 arxiv:1406.0139v1 [q-bio.nc] 1 Jun 2014 Distribution of Orientation Selectivity in Recurrent Networks of Spiking Neurons with Different Random Topologies Sadra Sadeh 1, Stefan Rotter 1, 1 Bernstein Center
Adaptation in the Neural Code of the Retina
Adaptation in the Neural Code of the Retina Lens Retina Fovea Optic Nerve Optic Nerve Bottleneck Neurons Information Receptors: 108 95% Optic Nerve 106 5% After Polyak 1941 Visual Cortex ~1010 Mean Intensity
On the Computational Complexity of Networks of Spiking Neurons
On the Computational Complexity of Networks of Spiking Neurons (Extended Abstract) Wolfgang Maass Institute for Theoretical Computer Science Technische Universitaet Graz A-80lO Graz, Austria e-mail: maass@igi.tu-graz.ac.at
Dendritic computation
Dendritic computation Dendrites as computational elements: Passive contributions to computation Active contributions to computation Examples Geometry matters: the isopotential cell Injecting current I