Using OxRAM arrays to mimic bio-inspired short and long term synaptic plasticity Leti Memory Workshop Thilo Werner 27/06/2017
|
|
|
- Imogene Copeland
- 5 years ago
- Views:
Transcription
1 Using OxRAM arrays to mimic bio-inspired short and long term synaptic plasticity Leti Memory Workshop Thilo Werner 27/06/2017 Collaboration by: T. Werner, E. Vianello, O. Bichler, A. Grossi, E. Nowak, J.-F. Nodin, B. Yvert, B. DeSalvo, L. Perniola
2 Why Neuromorphic? Number of devices 20 B von-neumann 15 B non-von-neumann Computation = Memory 10 B Out In Low power devices Abundant unstructured data Real-time response 5B Computation Memory Moore s law 1B? Execution of NVN on VN $$ hardware 2
3 Neural Networks Synapse Neuron OxRAM to implement HW synapses: high density online learning low power consumption 3
4 Synaptic plasticity in biology Pre-synaptic terminal Transmitters Receptor channels Post-synaptic terminal Transmitters Short Term Plasticity Channels Long Term Plasticity 4
5 Simplified plasticity models Synapse Pre-synaptic neuron i j Post-synaptic neuron Long Term Plasticity w w Short Term Plasticity w w time time time time Emerging NVM demonstrated only for LTP 5
6 State-of-the-art: RRAM synapses Long Term Plasticity (LTP) Spike-Timing-Dependent Plasticity i j t pre t t post STDP: correlation of pre- and postsynaptic activities 6
7 Objective 1. How to implement Short Term Plasticity with non-volatile OxRAM? 2. Is STP (i.e. volatile ) useful for learning? 7
8 Outline Introduction OxRAM synapses and STP Co-implementation of STP & LTP STP impact in Spiking Neural Networks Summary 8
9 Multilevel OxRAM synapses Synapse input Probabilistic programming Driver circuit WL BL Binary OxRAM cells in parallel (N+1) levels w Switching probability gradual tuning 1 2 N Read more: D. Garbin et al., T-ED, 2015 V BL,Set/Reset, V WL,Set/Reset, t Set/Reset, Synapse output # 9
10 OxRAM test vehicle 64kbit HfO 2 OxRAM array integration SEM CC~100uA Electrical data from Leti MAD wafers: A. Grossi et al., IEDM
11 Set: HRSLRS Set probability ~ V Set 11
12 Short Term Plasticity (STP) Low spiking frequency i j High spiking frequency Biological model Time STP depends only on presynaptic activity and is volatile Impact for high spiking frequencies 12
13 STP programming scheme pre spikes Normalized weight 1 0 T Time Set Reset Set pulse every T Reset pulse every pre-synaptic spike 13
14 STP implemented in OxRAM Good fit btw data and 10 OxRAM device/syn 14
15 Impact of p Reset p Reset controls strength of STP depression 15
16 Impact of p Set p Set controls relaxation time 16
17 Outline Introduction OxRAM synapses and STP Co-implementation of STP & LTP Spiking Neural Networks Summary 17
18 Connecting STP with LTP g ij ~ y i (t) x w ij g ij ~ y i (t) x w ij 18
19 Circuit for co-implementation i V y i (t) I - A 1/y + - V, w ij g ij I A I (t)= V y i(t) y max w j If y (t)=y V, t =V (no STP impact) If y t =y min V, t =V ( (highest STP impact) ( Read current of y i (t) modulates I out 19
20 Array implementation y i (t) w ij j 1 i 1 N input neurons i 1 i 2 i N j 2 j 3 j M M output neurons i 2 i N N y i (t) synaptic weights (STP) j 1 j 2 j M N*M w ji synaptic weights (LTP) LTP: NxM weights STP: N weights 20
21 Outline Introduction to Synaptic Plasticity OxRAM synapses Co-implementation of STP & LTP STP impact on Spiking Neural Networks Summary 21
22 Visual pattern extraction Cars on freeway O. Bichler, T-ED, 2012 Retina inspired DVS Fully Connected Neural Network 128x128 px OxRAM synapses 60 neurons Online learning unsupervised Competitive winner-takesall Neurons become selective to lanes 22
23 Quantification Output Missed event (ME) False Positive (FP) Detection Rate (DR) 01= Truth Nondetected car Falsely detected car False Positive Rate (FPR) 891=
24 Noise Additional noise introduced by random spiking activity of retina +20 %Noise 24
25 Noise impact Noisy input signal tends to decrease DR and increase FP 25
26 STP effect Thanks to STP: DR and FPR strongly improve in case of highly noisy data 26
27 Decoding of neural activity Band-pass filters Fully Connected Neural Network Spike detection Neurons Microelectrode OxRAM synapses V Online learning unsupervised Competitive winner-takesall 27
28 Noisy input data Signal-Noise-Ratio of artificial data varied Noise level SNR=80 SNR=27 SNR=4.6 SNR=3 28
29 Noise impact Invalid! Very high FPR due to noise! 29
30 STP effect STP enables spike detection in noisy signals 30
31 Outline Introduction to Synaptic Plasticity OxRAM synapses Co-implementation of STP & LTP Spiking Neural Networks Summary 31
32 Summary Bio-inspired system with non-volatile OxRAM synapses to reproduce Long and Short Term Plasticity Short Term Plasticity achieved with: 1 additional synapse per input neuron 10 binary OxRAM cells per synapse Adding STP to LTP allows to suppress noise and improves learning strongly reduced False Positive Rate 32
33 Thank you for your attention. Leti, technology research institute Commissariat à l énergie atomique et aux énergies alternatives Minatec Campus 17 rue des Martyrs Grenoble Cedex France
34 References REFERENCES M. V. Tsodyks et al., Proc. Natl. Acad. Sci. vol.94, G. Q. Bi et al., J. Neuroscience, vol.18, A. Grossi et al., Proc. IEDM, D. Garbin et al., T-ED,
Neuromorphic 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.
Basics 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
Neural Networks. Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity
Neural Networks 32 (2012) 339 348 Contents lists available at SciVerse ScienceDirect Neural Networks journal homepage: www.elsevier.com/locate/neunet 2012 Special Issue Extraction of temporally correlated
Course Introduction. Neural Information Processing: Introduction. Notes. Administration
3 / 17 4 / 17 Course Introduction Neural Information Processing: Introduction Matthias Hennig and Mark van Rossum School of Informatics, University of Edinburgh Welcome and administration Course outline
Signal detection in networks of spiking neurons with dynamical synapses
Published in AIP Proceedings 887, 83-88, 7. Signal detection in networks of spiking neurons with dynamical synapses Jorge F. Mejías and Joaquín J. Torres Dept. of Electromagnetism and Physics of the Matter
Neural Information Processing: Introduction
1 / 17 Neural Information Processing: Introduction Matthias Hennig School of Informatics, University of Edinburgh January 2017 2 / 17 Course Introduction Welcome and administration Course outline and context
VS : 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
How we study the brain: a survey of methods used in neuroscience
How we study the brain: a survey of methods used in neuroscience Preparing living neurons for recording Large identifiable neurons in a leech Rohon-Beard neurons in a frog spinal cord Living slice of a
Dynamic Stochastic Synapses as Computational Units
Dynamic Stochastic Synapses as Computational Units Wolfgang Maass Institute for Theoretical Computer Science Technische Universitat Graz A-B01O Graz Austria. email: maass@igi.tu-graz.ac.at Anthony M. Zador
Reach and grasp by people with tetraplegia using a neurally controlled robotic arm
Leigh R. Hochberg et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm Nature, 17 May 2012 Paper overview Ilya Kuzovkin 11 April 2014, Tartu etc How it works?
Realization of Visual Representation Task on a Humanoid Robot
Istanbul Technical University, Robot Intelligence Course Realization of Visual Representation Task on a Humanoid Robot Emeç Erçelik May 31, 2016 1 Introduction It is thought that human brain uses a distributed
Introduction 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
Rolls,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,
Reading Neuronal Synchrony with Depressing Synapses
NOTE Communicated by Laurence Abbott Reading Neuronal Synchrony with Depressing Synapses W. Senn Department of Neurobiology, Hebrew University, Jerusalem 4, Israel, Department of Physiology, University
Mike Davies Director, Neuromorphic Computing Lab Intel Labs
Mike Davies Director, Neuromorphic Computing Lab Intel Labs Loihi at a Glance Key Properties Integrated Memory + Compute Neuromorphic Architecture 128 neuromorphic cores supporting up to 128k neurons and
Intro. Comp. NeuroSci. Ch. 9 October 4, The threshold and channel memory
9.7.4 The threshold and channel memory The action potential has a threshold. In figure the area around threshold is expanded (rectangle). A current injection that does not reach the threshold does not
How Synapses Integrate Information and Change
How Synapses Integrate Information and Change Rachel Stewart class of 2016 http://neuroscience.uth.tmc.edu/s1/chapter06.html http://neuroscience.uth.tmc.edu/s1/chapter07.html Chris Cohan, Ph.D. Dept. of
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
Cell 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
Modeling Depolarization Induced Suppression of Inhibition in Pyramidal Neurons
Modeling Depolarization Induced Suppression of Inhibition in Pyramidal Neurons Peter Osseward, Uri Magaram Department of Neuroscience University of California, San Diego La Jolla, CA 92092 possewar@ucsd.edu
Why do we have a hippocampus? Short-term memory and consolidation
Why do we have a hippocampus? Short-term memory and consolidation So far we have talked about the hippocampus and: -coding of spatial locations in rats -declarative (explicit) memory -experimental evidence
Sparse Coding in Sparse Winner Networks
Sparse Coding in Sparse Winner Networks Janusz A. Starzyk 1, Yinyin Liu 1, David Vogel 2 1 School of Electrical Engineering & Computer Science Ohio University, Athens, OH 45701 {starzyk, yliu}@bobcat.ent.ohiou.edu
Implementing Spike-Timing-Dependent Plasticity on SpiNNaker Neuromorphic Hardware
Implementing Spike-Timing-Dependent Plasticity on SpiNNaker Neuromorphic Hardware Xin Jin, Alexander Rast, Francesco Galluppi, Sergio Davies, and Steve Furber Abstract This paper presents an efficient
Bioscience in the 21st century
Bioscience in the 21st century Neurons, Synapses, and Signaling Dr. Michael Burger Outline: 1. Why neuroscience? 2. The neuron 3. Action potentials 4. Synapses 5. Organization of the nervous system 6.
Beyond Vanilla LTP. Spike-timing-dependent-plasticity or STDP
Beyond Vanilla LTP Spike-timing-dependent-plasticity or STDP Hebbian learning rule asn W MN,aSN MN Δw ij = μ x j (v i - φ) learning threshold under which LTD can occur Stimulation electrode Recording electrode
Racing to Learn: Statistical Inference and Learning in a Single Spiking Neuron with Adaptive Kernels
Racing to Learn: Statistical Inference and Learning in a Single Spiking Neuron with Adaptive Kernels Saeed Afshar 1, Libin George 2, Jonathan Tapson 1, André van Schaik 1, Tara Julia Hamilton 1,2 1 Bioelectronics
Information 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
VHDL implementation of Neuron based classification for future artificial intelligence applications
VHDL implementation of Neuron based classification for future artificial intelligence applications 1Reasearch Scholar, Deptof ECEAURORAEngineering College, Hyderabad,India. 2Assoc Prof,Dept of ECE AURORA
Applied Neuroscience. Conclusion of Science Honors Program Spring 2017
Applied Neuroscience Conclusion of Science Honors Program Spring 2017 Review Circle whichever is greater, A or B. If A = B, circle both: I. A. permeability of a neuronal membrane to Na + during the rise
Neural Information Processing: between synchrony and chaos
1 Neural Information Processing: between synchrony and chaos Josep L. Rosselló, V. Canals and A. Morro Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Spain Summary The brain is
Tunable Low Energy, Compact and High Performance Neuromorphic Circuit for Spike-Based Synaptic Plasticity
Tunable Low Energy, Compact and High Performance Neuromorphic Circuit for Spike-Based Synaptic Plasticity Mostafa Rahimi Azghadi*, Nicolangelo Iannella, Said Al-Sarawi, Derek Abbott School of Electrical
The 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
Computational 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
A general error-based spike-timing dependent learning rule for the Neural Engineering Framework
A general error-based spike-timing dependent learning rule for the Neural Engineering Framework Trevor Bekolay Monday, May 17, 2010 Abstract Previous attempts at integrating spike-timing dependent plasticity
Artificial 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
Memory Systems II How Stored: Engram and LTP. Reading: BCP Chapter 25
Memory Systems II How Stored: Engram and LTP Reading: BCP Chapter 25 Memory Systems Learning is the acquisition of new knowledge or skills. Memory is the retention of learned information. Many different
Part 11: Mechanisms of Learning
Neurophysiology and Information: Theory of Brain Function Christopher Fiorillo BiS 527, Spring 2012 042 350 4326, fiorillo@kaist.ac.kr Part 11: Mechanisms of Learning Reading: Bear, Connors, and Paradiso,
Evaluating 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
Synchrony Detection by Analogue VLSI Neurons with Bimodal STDP Synapses
Synchrony Detection by Analogue VLSI Neurons with Bimodal STDP Synapses Adria Bofill-i-Petit The University of Edinburgh Edinburgh, EH9 JL Scotland adria.bofill@ee.ed.ac.uk Alan F. Murray The University
Observational Learning Based on Models of Overlapping Pathways
Observational Learning Based on Models of Overlapping Pathways Emmanouil Hourdakis and Panos Trahanias Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH) Science and Technology
The synaptic Basis for Learning and Memory: a Theoretical approach
Theoretical Neuroscience II: Learning, Perception and Cognition The synaptic Basis for Learning and Memory: a Theoretical approach Harel Shouval Phone: 713-500-5708 Email: harel.shouval@uth.tmc.edu Course
STDP-based spiking deep convolutional neural networks for object recognition
arxiv:1611.01421v3 [cs.cv] 25 Dec 2017 This manuscript is published in Neural Networks. Please cite it as: Kheradpisheh, S.R., Ganjtabesh, M., Thorpe, S.J., Masquelier, T., STDP-based spiking deep convolutional
Efficient Emulation of Large-Scale Neuronal Networks
Efficient Emulation of Large-Scale Neuronal Networks BENG/BGGN 260 Neurodynamics University of California, San Diego Week 6 BENG/BGGN 260 Neurodynamics (UCSD) Efficient Emulation of Large-Scale Neuronal
Input-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
Part 3. Synaptic models
Part 3 Synaptic models Purpose of synap/c modeling To capture the following facts: 1. Some neurons have stronger and more las/ng influences over a given neuron than others 2. Some of these influences are
International 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
BIOLOGICAL PROCESSES
BIOLOGICAL PROCESSES CHAPTER 3 1 LEARNING GOALS Discuss how the nervous system communicates internally. Describe the structure and function of neurons Describe how the neuron transmits information Describe
In Vitro Analog of Operant Conditioning in Aplysia
The Journal of Neuroscience, March 15, 1999, 19(6):2261 2272 In Vitro Analog of Operant Conditioning in Aplysia. II. Modifications of the Functional Dynamics of an Identified Neuron Contribute to Motor
Temporally 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
Spiking 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
Dynamic 3D Clustering of Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network Architecture on a Case Study of fmri Data
Dynamic 3D Clustering of Spatio-Temporal Brain Data in the NeuCube Spiking Neural Network Architecture on a Case Study of fmri Data Maryam Gholami Doborjeh (B) and Nikola Kasabov Knowledge Engineering
Synaptic 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
Introduction 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
Synapses and synaptic plasticity. Lubica Benuskova Lecture 8 How neurons communicate How do we learn and remember
Synapses and synaptic plasticity Lubica Benuskova Lecture 8 How neurons communicate How do we learn and remember 1 Brain is comprised of networks of neurons connected and communicating via synapses ~10
How Synapses Integrate Information and Change
How Synapses Integrate Information and Change Rachel Stewart class of 2016 https://nba.uth.tmc.edu/neuroscience/s1/chapter06.html https://nba.uth.tmc.edu/neuroscience/s1/chapter07.html Chris Cohan, Ph.D.
Task 1: Machine Learning with Spike-Timing-Dependent Plasticity (STDP)
DARPA Report Task1 for Year 1 (Q1-Q4) Task 1: Machine Learning with Spike-Timing-Dependent Plasticity (STDP) 1. Shortcomings of the deep learning approach to artificial intelligence It has been established
Long-term synaptic plasticity. N500, 6 Sept., 2016
Long-term synaptic plasticity N500, 6 Sept., 2016 We just finished describing short-term synaptic changes What about long-term changes? How does the brain store information? Outline Origins of the synaptic
Signal Processing by Multiplexing and Demultiplexing in Neurons
Signal Processing by Multiplexing and Demultiplexing in Neurons DavidC. Tam Division of Neuroscience Baylor College of Medicine Houston, TX 77030 dtam@next-cns.neusc.bcm.tmc.edu Abstract Signal processing
A 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
Recurrently Connected Silicon Neurons with Active Dendrites for One-Shot Learning
Recurrently Connected Silicon Neurons with Active Dendrites for One-Shot Learning John V. Arthur and Kwabena Boahen Department of Bioengineering, University of Pennsylvania Philadelphia, PA 19104, U.S.A.
Axon initial segment position changes CA1 pyramidal neuron excitability
Axon initial segment position changes CA1 pyramidal neuron excitability Cristina Nigro and Jason Pipkin UCSD Neurosciences Graduate Program Abstract The axon initial segment (AIS) is the portion of the
LEARNING ARBITRARY FUNCTIONS WITH SPIKE-TIMING DEPENDENT PLASTICITY LEARNING RULE
LEARNING ARBITRARY FUNCTIONS WITH SPIKE-TIMING DEPENDENT PLASTICITY LEARNING RULE Yefei Peng Department of Information Science and Telecommunications University of Pittsburgh Pittsburgh, PA 15260 ypeng@mail.sis.pitt.edu
Brain-inspired Balanced Tuning for Spiking Neural Networks
Brain-inspired Balanced Tuning for Spiking Neural Networks Tielin Zhang 1,3, Yi Zeng 1,2,3,4,5,, Dongcheng Zhao 1,2,3 and Bo Xu 1,2,3,5 1 Institute of Automation, Chinese Academy of Sciences (CAS), China
Copyright Dr. Franklin B. Krasne, Swimmy
Copyright Dr. Franklin B. Krasne, 2008 Swimmy Advantages of Swimmy 1) Very faithful simulation of neural activity 2) Flawless electrode placement. 3) No extraneous noise issues--including extraneous biological
Lecture 22: A little Neurobiology
BIO 5099: Molecular Biology for Computer Scientists (et al) Lecture 22: A little Neurobiology http://compbio.uchsc.edu/hunter/bio5099 Larry.Hunter@uchsc.edu Nervous system development Part of the ectoderm
Indirect Training of a Spiking Neural Network for Flight Control via Spike-Timing-Dependent Synaptic Plasticity
49th IEEE Conference on Decision and Control December 15-17, 2010 Hilton Atlanta Hotel, Atlanta, GA, USA Indirect Training of a Spiking Neural Network for Flight Control via Spike-Timing-Dependent Synaptic
Synaptic theory of gradient learning with empiric inputs. Ila Fiete Kavli Institute for Theoretical Physics. Types of learning
Synaptic theory of gradient learning with empiric inputs Ila Fiete Kavli Institute for Theoretical Physics Types of learning Associations among events (and reinforcements). e.g., classical conditioning
Stable Spike-Timing Dependent Plasticity Rule for Multilayer Unsupervised and Supervised Learning
Stable Spike-Timing Dependent Plasticity Rule for Multilayer Unsupervised and Supervised Learning Amar Shrestha, Khadeer Ahmed, Yanzhi Wang, Qinru Qiu Department of Electrical Engineering and Computer
Temporal 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
Modeling the interplay of short-term memory and the basal ganglia in sequence processing
Neurocomputing 26}27 (1999) 687}692 Modeling the interplay of short-term memory and the basal ganglia in sequence processing Tomoki Fukai* Department of Electronics, Tokai University, Hiratsuka, Kanagawa
Introduction to Electrophysiology
Introduction to Electrophysiology Dr. Kwangyeol Baek Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School 2018-05-31s Contents Principles in Electrophysiology Techniques
Cellular Neurobiology BIPN140
Cellular Neurobiology BIPN140 1st Midterm Exam Ready for Pickup By the elevator on the 3 rd Floor of Pacific Hall (waiver) Exam Depot Window at the north entrance to Pacific Hall (no waiver) Mon-Fri, 10:00
Computational Neuropharmacology
Computational Neuropharmacology Schizophrenia: Symptoms & Pathologies Computational Models of Mental Illness: Neurons, synapses and modulators Example: Working Memory Model Pharmaceutical Industry: Progress
CS 182 Midterm review
CS 182 Midterm review Near far Image schemas What is a schema? a pattern capturing structure in the world What is an image schema? a schema about interaction with the world Can you name some? Containment
COMPLEX NEURAL COMPUTATION WITH SIMPLE DIGITAL NEURONS. Andrew Thomas Nere. A dissertation submitted in partial fulfillment of
COMPLEX NEURAL COMPUTATION WITH SIMPLE DIGITAL NEURONS by Andrew Thomas Nere A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Electrical Engineering)
Cellular mechanisms of information transfer: neuronal and synaptic plasticity
Cellular mechanisms of information transfer: neuronal and synaptic plasticity Ivan Pavlov (UCL Institute of Neurology, UK) Anton Chizhov (Ioffe Physical Technical Institute) Pavel Zykin (St.-Petersburg
arxiv: v1 [q-bio.nc] 25 Apr 2017
![arxiv: v1 [q-bio.nc] 25 Apr 2017](/thumbs/86/93358251.jpg "arxiv: v1 [q-bio.nc] 25 Apr 2017")
Neurogenesis and multiple plasticity mechanisms enhance associative memory retrieval in a spiking network model of the hippocampus arxiv:1704.07526v1 [q-bio.nc] 25 Apr 2017 Yansong, Chua and Cheston, Tan
A neural circuit model of decision making!
A neural circuit model of decision making! Xiao-Jing Wang! Department of Neurobiology & Kavli Institute for Neuroscience! Yale University School of Medicine! Three basic questions on decision computations!!
Project SpikeFORCE: Real-time Spiking Networks for Robot Control
IST-2001 2001-35271 Project SpikeFORCE: Real-time Spiking Networks for Robot Control Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Life Life-like Perception Systems
Reactive, Proactive, and Inductive Agents: An evolutionary path for biological and artificial spiking networks
: An evolutionary path for biological and artificial spiking networks Lana Sinapayen 1, Atsushi Masumori 2, and Ikegami Takashi 2 1 Sony Computer Science Laboratories, Inc., Shinagawa-ku, Tokyo, Japan
Synaptic Plasticity and the NMDA Receptor
Synaptic Plasticity and the NMDA Receptor Lecture 4.2 David S. Touretzky November, 2015 Long Term Synaptic Plasticity Long Term Potentiation (LTP) Reversal of LTP Long Term Depression (LTD) Reversal of
Activity-Dependent Development II April 25, 2007 Mu-ming Poo
Activity-Dependent Development II April 25, 2007 Mu-ming Poo 1. The neurotrophin hypothesis 2. Maps in somatic sensory and motor cortices 3. Development of retinotopic map 4. Reorganization of cortical
AND BIOMEDICAL SYSTEMS Rahul Sarpeshkar
ULTRA-LOW-POWER LOW BIO-INSPIRED INSPIRED AND BIOMEDICAL SYSTEMS Rahul Sarpeshkar Research Lab of Electronics Massachusetts Institute of Technology Electrical Engineering and Computer Science FOE Talk
LECTURE 2. C. Reason correlation and synaptic delay not enough to prove direct connection. D. Underlying mechanism behind oscillations possibilities
LECTURE 2 A. Identifying Swimmy neurons B. Finding E and I inputs to cells 1 and 2 C. Reason correlation and synaptic delay not enough to prove direct connection D. Underlying mechanism behind oscillations
11/2/2011. Basic circuit anatomy (the circuit is the same in all parts of the cerebellum)
11/2/2011 Neuroscientists have been attracted to the puzzle of the Cerebellum ever since Cajal. The orderly structure, the size of the cerebellum and the regularity of the neural elements demands explanation.
A hardware implementation of a network of functional spiking neurons with hebbian learning
A hardware implementation of a network of functional spiking neurons with hebbian learning Andrés Upegui, Carlos Andrés Peña-Reyes, Eduardo Sánchez Swiss Federal Institute of Technology, Logic Systems
BIPN 140 Problem Set 6
BIPN 140 Problem Set 6 1) The hippocampus is a cortical structure in the medial portion of the temporal lobe (medial temporal lobe in primates. a) What is the main function of the hippocampus? The hippocampus
CSE 599E Lecture 2: Basic Neuroscience
CSE 599E Lecture 2: Basic Neuroscience 1 Today s Roadmap The neuron doctrine (or dogma) Neuronal signaling The electrochemical dance of ions Action Potentials (= spikes) Synapses and Synaptic Plasticity
VISUAL CORTICAL PLASTICITY
VISUAL CORTICAL PLASTICITY OCULAR DOMINANCE AND OTHER VARIETIES REVIEW OF HIPPOCAMPAL LTP 1 when an axon of cell A is near enough to excite a cell B and repeatedly and consistently takes part in firing
Computing with Spikes in Recurrent Neural Networks
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 Outline Introduction Neurons,
BIPN 140 Problem Set 6
BIPN 140 Problem Set 6 1) Hippocampus is a cortical structure in the medial portion of the temporal lobe (medial temporal lobe in primates. a) What is the main function of the hippocampus? The hippocampus
Recognition 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
Introduction to Computational Neuroscience
Introduction to Computational Neuroscience Lecture 10: Brain-Computer Interfaces Ilya Kuzovkin So Far Stimulus So Far So Far Stimulus What are the neuroimaging techniques you know about? Stimulus So Far
Different 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
Shadowing 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
Running 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
Evolution of Spiking Neural Controllers for Autonomous Vision-Based Robots
Evolution of Spiking Neural Controllers for Autonomous Vision-Based Robots Dario Floreano and Claudio Mattiussi Evolutionary & Adaptive Systems, Institute of Robotics Swiss Federal Institute of Technology,
ERA: Architectures for Inference
ERA: Architectures for Inference Dan Hammerstrom Electrical And Computer Engineering 7/28/09 1 Intelligent Computing In spite of the transistor bounty of Moore s law, there is a large class of problems
Multi compartment model of synaptic plasticity
Multi compartment model of synaptic plasticity E. Paxon Frady We introduce a biophysical model of a neuronal network that can accurately replicate many classical plasticity experiments. The model uses