Leaky integrate-and-fire neuron circuit based on floating-gate - PowerPoint PPT Presentation

Mar 24, 2024 •142 likes •220 views

Leaky integrate-and-fire neuron circuit based on floating-gate integrator Vladimir Kornijcuk, Hyungkwang Lim, and Doo Seok Jeong Korea Institute of Science and Technology dsjeong@kist.re.kr 1 Stochastic spiking neural network abstracts features

Leaky integrate-and-fire neuron circuit based on floating-gate integrator Vladimir Kornijcuk, Hyungkwang Lim, and Doo Seok Jeong Korea Institute of Science and Technology dsjeong@kist.re.kr 1
Stochastic spiking neural network abstracts features essential to recognition from raw input . . Out . In . P ( In | Out ) Out ∈ M Nl . . In ∈ M N 1 ( Nl < N1 ) P Voltage Time 2
Stochasticity in neuronal response is essential to stochastic SNN Output Time … t 1 t 2 t N Input Time T Stochasticity: variability in interspike interval (ISI) 3
Floating-gate leaky integrate-and-fire (FGLIF) neuron circuit Integrator Amplifier1 Amplifier2 Polarity inverter (reset) Circuit simulation: LTSpice (BSIM 4 model: 65 nm technology) Kornijcuk et al., Front. Neurosci. 10, 212, 2016 4
FGLIF neuron spikes upon incident spikes 5
Stochasticity is on the ground of noise, e.g. thermal, burst, and flicker noise 6
Thank you very much! 7

Recommend

Generalized Leaky Integrate-and-Fire Model Building blocks of models for cortical computation

Generalized Leaky Integrate-and-Fire Model Building blocks of models for cortical computation Stefan Mihalas Assistant Investigator Affiliate Assistant Professor Lecture plan 1. Motivation: why we study mouse cortex 2. Single neuron

441 views • 32 slides

Modelling Membrane Potentials by Diffusion Leaky Integrate-and-Fire Models Patrick Jahn

Diffusion Leaky Integrate-and-Fire Neuronal Models Modelling by time-inhomogeneous Diffusion Models Modelling Membrane Potentials by Diffusion Leaky Integrate-and-Fire Models Patrick Jahn DEPARTMENT OF MATHEMATICAL SCIENCES UNIVERSITY OF

938 views • 40 slides

Neurons (nerve cells) Faculty of Science The Morris Lecar neuron model gives rise to the

u n i v e r s i t y o f c o p e n h a g e n Neurons (nerve cells) Faculty of Science The Morris Lecar neuron model gives rise to the Ornstein-Uhlenbeck leaky integrate-and-fire model = Susanne Ditlevsen Cindy Greenwood

252 views • 8 slides

A Scalable Time-based Integrate-and-Fire Neuromorphic Core with Brain-Inspired Leak and Local

A Scalable Time-based Integrate-and-Fire Neuromorphic Core with Brain-Inspired Leak and Local Lateral Inhibition Capabilities Muqing Liu, Luke R. Everson, Chris H. Kim Dept. of ECE, University of Minnesota, Minneapolis, MN liux3300@umn.edu 1

570 views • 23 slides

Course Summary (thus far) F Neural Encoding What makes a neuron fire? (STA, covariance analysis)

CSE/NEUBEH 528 Modeling Synapses and Networks (Chapter 7) Image from Wikimedia Commons R. Rao, 528: Lecture 8 1 Lecture figures are from Dayan & Abbotts book Course Summary (thus far) F Neural Encoding What makes a neuron fire? (STA,

273 views • 12 slides

Fire & Inspection Integration Why integrate Opportunity to Consolidate Services around

Fire & Inspection Integration Why integrate Opportunity to Consolidate Services around Common Theme of Safety Need to Enhance Customer Service Structure needed for effective service delivery Efficiency of shared

216 views • 10 slides

Chimera-like states in two interacting populations of heterogeneous quadratic integrate-and-fire

Chimera-like states in two interacting populations of heterogeneous quadratic integrate-and-fire neurons Irmantas Ratas, Kstutis Pyragas Center for Physical Sciences and Technology, Lithuania Loughborough 2018 09 04 irmantas.ratas@gmail.com

389 views • 20 slides

Myra Dioquino Ne Neuron Ne Neuron ons ons ~30x out ~30x in 20 000 20,000x out o t ~30x

Myra Dioquino Ne Neuron Ne Neuron ons ons ~30x out ~30x in 20 000 20,000x out o t ~30x out Describes the equilibrium potential of the cell for a particular ion R Universal gas constant (8.314472 J K ) T

455 views • 9 slides

Leaky Processors and the RISE of Hardware-Based Trusted Computing Jo Van Bulck imec-DistriNet,

Leaky Processors and the RISE of Hardware-Based Trusted Computing Jo Van Bulck imec-DistriNet, KU Leuven jo.vanbulck@cs.kuleuven.be jovanbulck 1st RISE Annual Conference, November 14, 2018 A primer on software security Secure program:

952 views • 79 slides

1 Electrical Signaling in the Nervous System is The Bulk Solution Remains Electroneutral Caused

Equivalent Circuit Model of the Neuron Generator Potentials, Synaptic Potentials and Action Potentials All Can Be Described by the Equivalent Circuit Model of the Membrane The Nerve (or Muscle) Cell can be Represented by a Collection of

457 views • 8 slides

INTEGRATE-HTA Ansgar Gerhardus for the INTEGRATE-HTA project team INTEGRATE-HTA Aim of

Department of Health Service Research Head: Prof. Dr. Ansgar Gerhardus Integrated health technology assessment for the evaluation of complex technologies INTEGRATE-HTA Ansgar Gerhardus for the INTEGRATE-HTA project team INTEGRATE-HTA Aim of

962 views • 23 slides

On the Impact of Structural Circuit Partitioning on SAT-based Combinational Circuit Verification

On the Impact of Structural Circuit Partitioning on SAT-based Combinational Circuit Verification (Presentation at IEEE MTV 2004) Marc Herbstritt (joint work with Thomas Kmieciak and Bernd Becker) http://ira.informatik.uni-freiburg.de

351 views • 18 slides

STEP 5. Slides of a neuron, nerve, and spinal cord #1 Neuron slide This step can wait until

STEP 5. Slides of a neuron, nerve, and spinal cord #1 Neuron slide This step can wait until later, if youd like. But make sure you look at these Read images! Me First, we will look at the motor neuron. Note that it is difficult to

370 views • 3 slides

CS 472 - Perceptron 1 Basic Neuron CS 472 - Perceptron 2 Expanded Neuron CS 472 - Perceptron

CS 472 - Perceptron 1 Basic Neuron CS 472 - Perceptron 2 Expanded Neuron CS 472 - Perceptron 3 Perceptron Learning Algorithm l First neural network learning model in the 1960s l Simple and limited (single layer models) l Basic concepts

571 views • 53 slides

Mathematical modeling in biology. D. Salort, LBCQ, Sorbonne University, Paris 03-07 september

Mod` ele Leaky-Integrate and Fire. one extension : kinetic model Mathematical modeling in biology. D. Salort, LBCQ, Sorbonne University, Paris 03-07 september 2018 D. Salort, LBCQ, Sorbonne University, Paris Mathematical modeling in biology.

511 views • 32 slides

Lecture 3 Floating Point Representations 1 Floating-point arithmetic We often incur

ECE 0142 Computer Organization Lecture 3 Floating Point Representations 1 Floating-point arithmetic We often incur floating-point programming. Floating point greatly simplifies working with large (e.g., 2 70 ) and small (e.g., 2 -17 )

638 views • 30 slides

Neuron Technologies Company Profile Dr. Ismayil Alakbarov Founder & CEO, Neuron Technologies

Success Story of Neuron Technologies Company Profile Dr. Ismayil Alakbarov Founder & CEO, Neuron Technologies State of the Art Technologies Our solutions are developed at the latest technology platforms. We keep abreast of the

249 views • 22 slides

Fire Service Based EMS Transport and First Responder Services Option 1 * Presented to the

Fire Service Based EMS Transport and First Responder Services Option 1 * Presented to the Pinellas County EMS and Fire Administration EMS and Fire Reconfiguration Committee December 2008 * (During the presentation of this paper to the

278 views • 23 slides

A supervised learning approach based on STDP and polychronization in spiking neuron networks

A supervised learning approach based on STDP and polychronization in spiking neuron networks Hlne Paugam-Moisy 1 , Rgis Martinez 1 and Samy Bengio 2 1 LIRIS - CNRS - Universit Lumire Lyon 2 Lyon, France http://liris.cnrs.fr 2 IDIAP

624 views • 58 slides

Fire Occurrence in Side Crashes Based on NASS/CDS Kennerly H. Digges Motor Vehicle Fire Research

Fire Occurrence in Side Crashes Based on NASS/CDS Kennerly H. Digges Motor Vehicle Fire Research Institute MVFRI 2009-01-0008 Data Sources The Fatality Analysis Reporting System (FARS) years 1979 to 2005 The National Automotive Sampling

356 views • 34 slides

Biomedicine Enrico Grisan enrico.grisan@dei.unipd.it Neuron basics Neuron: real and simulated

Applied Machine Learning in Biomedicine Enrico Grisan enrico.grisan@dei.unipd.it Neuron basics Neuron: real and simulated A bit of history From biology to models Biological models? Careful with brain analogies: Many different types of

359 views • 34 slides

MistFire a fire made of water Where We Started Large Array of Fire Pixelated Design Changes

MistFire a fire made of water Where We Started Large Array of Fire Pixelated Design Changes Color MistFire A water vapor-based fire candle that: Looks realistic Is safe User Needs & Background 7.9% fire injuries 8,200 annual house

624 views • 13 slides

Floating-point numbers Fractional binary numbers IEEE floating-point standard Floating-point

Floating-point numbers Fractional binary numbers IEEE floating-point standard Floating-point operations and rounding Lessons for programmers Many more details we will skip (its a 58-page standard) See CSAPP 2.4 for more detail. 1

381 views • 15 slides

Sedona Fire Fleet and Equipment Needs Citizen Committee Briefing April 17, 2017 Fire Vehicles

Sedona Fire Fleet and Equipment Needs Citizen Committee Briefing April 17, 2017 Fire Vehicles Fire Engine Types Based on Usage Plan for a 15-Year Replacement Cycle Type 1 = Large/Urban/Suburban Fire Engine for Structure Response

116 views • 8 slides