SUPPLEMENTARY INFORMATION

Size: px
Start display at page:

Download "SUPPLEMENTARY INFORMATION"

Transcription

1 Figure S1 Multiplicative scaling of the granule cell input-output relation is not dependent on input rate. Input-output relations with short-term depression (STD) from Fig. 1d after normalizing by the maximum firing rate from the respective fits and subtracting the mossy fibre (MF) input stimulation rate at the half maximal value. The scale factor between the control (solid line) and inhibition (dashed line, +inh) was 1.8. Error bars indicate s.e.m. 1

2 Figure S2 Effects of synaptic plasticity and tonic inhibition on gain modulation in a conductance-based integrate-and-fire model. a, Relative peak amplitude of steady-state synaptic AMPAR conductance versus excitatory input rate. Solid line shows model with parameters (δ = 0.5, τ d = 40 ms) that best fit the MF- GC synaptic data (circles). Inset: model synaptic trains with and without STD (δ = 0.5 and 1.0) at 86 Hz. b, Model input-output relations with and without STD, in the absence (solid lines) and presence (dashed lines) of tonic inhibition (G inh = ps). Bold dashed lines denote G inh = 500 ps, the experimental value. c, Model G exc f relations for various levels of depression (+STD; δ = ), facilitation (+STF; δ = 1.1, 1.2) and no plasticity (-STD; δ = 1.0). d, Change in gain ( Gain) due to tonic inhibition (±inh) computed from fits (equation (5), Methods) to the model input-output relations, plotted versus G inh and δ. Colour scale shown at top. Bottom graph shows Gain for G inh = 500 ps, which increases for increasing levels of STD. 2

3 Figure S3 Gain modulation by phasic inhibition in an integrate-and-fire model. a, Input-output relations of the integrate-and-fire model driven with AMPAR synaptic trains with and without STD (δ = 0.5 and 1.0; Fig S2c) in the absence (solid lines) and presence (dashed lines) of various levels of phasic inhibitory input (inh; 50, 100, 150 Hz; peak conductance = 663 ps, τ d2 = 6.9 ms). b, Change in gain and offset as a function of phasic inhibition rate determined from fits (equation (5), Methods) to the input-output relations in a. 3

4 Figure S4 Voltage dependence of synaptic NMDAR conductance. a, Voltage dependence of the synaptic NMDAR conductance recorded in GCs (circles) computed from peak of average NMDAR synaptic currents (inset). NMDA currents were recorded in the presence of 5 µm NBQX at different voltages (+20 to -80 mv) as indicated by colours (n=6). Stimulation transients were blanked 1 ms from their onset. Solid line (black) shows conductance generated by SM-1 amplifier after tuning its characteristics to best match the data. Dashed lines denote the range of the average GC membrane potential during stimulation, as in b. Error bars indicate s.e.m. b, Average membrane potential recorded during the dynamic clamp experiments in Fig. 3 as a function of MF stimulation rate with and without AMPAR STD (blue and red; n=11). 4

5 Figure S5 Predicted input-output relations for single MF stimulation assuming a purely additive shift in the relationship between GC firing and mean excitatory conductance. Predicted input-output relations (blue lines) calculated from equation (5) in Methods, using the G exc f curve in Fig. 4d and FG exc curves in inset. Inset: solid line shows FG exc curve derived from the curve fit to the control (+STD) data in Fig. 4c data. Dashed line is the same curve shifted 0.2 ns along the G exc axis. The change in the input-output relation predicted from a purely additive shift in the FG exc relation match the data well. Error bars indicate s.e.m. 5

6 Table S1. Fit parameters. Fig. Eq. Data F 0 (Hz) F max (Hz) H 50 n m (ps/hz) λ (Hz) 1d 5 -STD 0* * 1d 5 -STD + inh 0* * 1d 5 +STD 0* * 105.1* 1d 5 +STD + inh 0* * 105.1* 2b 3 -STD b 4 +STD c 5 -STD 0* c 5 -STD + inh 0* d inset 5 -STD * 3b 3 -STD b 4 +STD c 5 -STD 0* * 3c 5 -STD + inh 0* * 3c 5 +STD 0* * 138.0* 3c 5 +STD + inh 0* * 138.0* 4c 5 MF stim 0* 313.8* * 181.3* 4c 5 MF stim+inh 0* 313.8* * 181.3* 4d 4 MF stim b 5 -STD b 5 -STD + inh 50 Hz b 5 -STD + inh b 5 -STD + inh d 5 +STD 0* d 5 +STD + inh 30 0* d 5 +STD + inh 45 0* d 5 +STD + inh 60 0* H 50 denotes G exc 50 (ns), except for the fits in Fig. 5, where it denotes the half-maximal output firing rate (Hz). * Values fixed during the curve fit. Mossy fiber stimulation (MF stim). Inhibition (inh). AMPAR Short-term depression (STD). For the fits in Fig. 4c, F max was not well-constrained, and therefore fixed to the largest F max in our data set; this produced little change in Gain and Offset values reported in Fig. 4e. 6

How do synapses transform inputs?

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

More information

Synaptic dynamics. John D. Murray. Synaptic currents. Simple model of the synaptic gating variable. First-order kinetics

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

More information

Phase Response. 1 of of 11. Synaptic input advances (excitatory) or delays (inhibitory) spiking

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)

More information

This script will produce a series of pulses of amplitude 40 na, duration 1ms, recurring every 50 ms.

This script will produce a series of pulses of amplitude 40 na, duration 1ms, recurring every 50 ms. 9.16 Problem Set #4 In the final problem set you will combine the pieces of knowledge gained in the previous assignments to build a full-blown model of a plastic synapse. You will investigate the effects

More information

Biological Modeling of Neural Networks

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

More information

Adaptation in the Neural Code of the Retina

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

More information

Supporting Online Material for

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.

More information

arxiv: v1 [q-bio.nc] 1 Jun 2014

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

More information

Consider the following spike trains from two different neurons N1 and N2:

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

More information

Neurophysiology of a VLSI spiking neural network: LANN21

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

More information

1 Balanced networks: Trading speed for noise

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

More information

An Introductory Course in Computational Neuroscience

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

More information

Emergence of resonances in neural systems: the interplay between adaptive threshold and short-term synaptic plasticity

Emergence of resonances in neural systems: the interplay between adaptive threshold and short-term synaptic plasticity Emergence of resonances in neural systems: the interplay between adaptive threshold and short-term synaptic plasticity Jorge F. Mejias 1,2 and Joaquín J. Torres 2 1 Department of Physics and Center for

More information

Frequency (Hz) Amplitude (pa) D1 WT D1 KO D2 WT D2 KO D1 WT D1 KO D2 WT D2 KO

Frequency (Hz) Amplitude (pa) D1 WT D1 KO D2 WT D2 KO D1 WT D1 KO D2 WT D2 KO A D1 MSNs B D2 MSNs C Frequency (Hz) 4 3 2 1 D Amplitude (pa) 5 4 3 2 1 D1 D1 D2 D2 D1 D1 D2 D2 Supplemental Figure 1. B deletion did not alter GABA-mIPSCs in D1 or D2 MSNs. (A,B) Representative recording

More information

LGN Input to Simple Cells and Contrast-Invariant Orientation Tuning: An Analysis

LGN Input to Simple Cells and Contrast-Invariant Orientation Tuning: An Analysis J Neurophysiol 87: 2741 2752, 2002; 10.1152/jn.00474.2001. LGN Input to Simple Cells and Contrast-Invariant Orientation Tuning: An Analysis TODD W. TROYER, 1 ANTON E. KRUKOWSKI, 2 AND KENNETH D. MILLER

More information

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 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

More information

Spike-Frequency Adaptation of a Generalized Leaky Integrate-and-Fire Model Neuron

Spike-Frequency Adaptation of a Generalized Leaky Integrate-and-Fire Model Neuron Journal of Computational Neuroscience 10, 25 45, 2001 c 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. Spike-Frequency Adaptation of a Generalized Leaky Integrate-and-Fire Model Neuron

More information

Coordination of Cellular Pattern-Generating Circuits that Control Limb Movements: The Sources of Stable Differences in Intersegmental Phases

Coordination of Cellular Pattern-Generating Circuits that Control Limb Movements: The Sources of Stable Differences in Intersegmental Phases The Journal of Neuroscience, April 15, 2003 23(8):3457 3468 3457 Coordination of Cellular Pattern-Generating Circuits that Control Limb Movements: The Sources of Stable Differences in Intersegmental Phases

More information

Synaptic Input. Linear Model of Synaptic Transmission. Professor David Heeger. September 5, 2000

Synaptic Input. Linear Model of Synaptic Transmission. Professor David Heeger. September 5, 2000 Synaptic Input Professor David Heeger September 5, 2000 The purpose of this handout is to go a bit beyond the discussion in Ch. 6 of The Book of Genesis on synaptic input, and give some examples of how

More information

Broadband coding with dynamic synapses

Broadband coding with dynamic synapses Broadband coding with dynamic synapses Benjamin Lindner Max-Planck-Institute for the Physics of Complex Systems, Nöthnitzer Str. 38 1187 Dresden, Germany André Longtin Department of Physics and Center

More information

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

/639 Final Solutions, Part a) Equating the electrochemical potentials of H + and X on outside and inside: = RT ln H in 580.439/639 Final Solutions, 2014 Question 1 Part a) Equating the electrochemical potentials of H + and X on outside and inside: RT ln H out + zf 0 + RT ln X out = RT ln H in F 60 + RT ln X in 60 mv =

More information

Structure and Measurement of the brain lecture notes

Structure and Measurement of the brain lecture notes Structure and Measurement of the brain lecture notes Marty Sereno 2009/2010!"#$%&'(&#)*%$#&+,'-&.)"/*"&.*)*-'(0&1223 Neurons and Models Lecture 1 Topics Membrane (Nernst) Potential Action potential/voltage-gated

More information

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

80% of all excitatory synapses - at the dendritic spines. Dendritic Modelling Dendrites (from Greek dendron, tree ) are the branched projections of a neuron that act to conduct the electrical stimulation received from other cells to and from the cell body, or

More information

LGN Input to Simple Cells and Contrast-Invariant Orientation Tuning: An Analysis

LGN Input to Simple Cells and Contrast-Invariant Orientation Tuning: An Analysis LGN Input to Simple Cells and Contrast-Invariant Orientation Tuning: An Analysis Todd W. Troyer 1, Anton E. Krukowski 2 and Kenneth D. Miller 3 Dept. of Psychology Neuroscience and Cognitive Science Program

More information

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

Effects of Betaxolol on Hodgkin-Huxley Model of Tiger Salamander Retinal Ganglion Cell Effects of Betaxolol on Hodgkin-Huxley Model of Tiger Salamander Retinal Ganglion Cell 1. Abstract Matthew Dunlevie Clement Lee Indrani Mikkilineni mdunlevi@ucsd.edu cll008@ucsd.edu imikkili@ucsd.edu Isolated

More information

Spike-Frequency Adaptation: Phenomenological Model and Experimental Tests

Spike-Frequency Adaptation: Phenomenological Model and Experimental Tests Spike-Frequency Adaptation: Phenomenological Model and Experimental Tests J. Benda, M. Bethge, M. Hennig, K. Pawelzik & A.V.M. Herz February, 7 Abstract Spike-frequency adaptation is a common feature of

More information

arxiv: v1 [q-bio.nc] 13 Feb 2018

arxiv: v1 [q-bio.nc] 13 Feb 2018 Gain control with A-type potassium current: I A as a switch between divisive and subtractive inhibition Joshua H Goldwyn 1*, Bradley R Slabe 2, Joseph B Travers 3, David Terman 2 arxiv:182.4794v1 [q-bio.nc]

More information

Neuronal Firing Sensitivity to Morphologic and Active Membrane Parameters

Neuronal Firing Sensitivity to Morphologic and Active Membrane Parameters Neuronal Firing Sensitivity to Morphologic and Active Membrane Parameters Christina M. Weaver 1,2,3*, Susan L. Wearne 1,2,3* 1 Laboratory of Biomathematics, Mount Sinai School of Medicine, New York, New

More information

Spatiotemporal Response Properties of Optic-Flow Processing Neurons

Spatiotemporal Response Properties of Optic-Flow Processing Neurons Article Spatiotemporal Response Properties of Optic-Flow Processing Neurons Franz Weber, 1,3, * Christian K. Machens, 2 and Alexander Borst 1 1 Department of Systems and Computational Neurobiology, Max-Planck-Institute

More information

Nervous Tissue. Neurons Electrochemical Gradient Propagation & Transduction Neurotransmitters Temporal & Spatial Summation

Nervous Tissue. Neurons Electrochemical Gradient Propagation & Transduction Neurotransmitters Temporal & Spatial Summation Nervous Tissue Neurons Electrochemical Gradient Propagation & Transduction Neurotransmitters Temporal & Spatial Summation What is the function of nervous tissue? Maintain homeostasis & respond to stimuli

More information

لجنة الطب البشري رؤية تنير دروب تميزكم

لجنة الطب البشري رؤية تنير دروب تميزكم 1) Hyperpolarization phase of the action potential: a. is due to the opening of voltage-gated Cl channels. b. is due to prolonged opening of voltage-gated K + channels. c. is due to closure of the Na +

More information

Fast neural network simulations with population density methods

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

More information

Exercises. Chapter 1. of τ approx that produces the most accurate estimate for this firing pattern.

Exercises. Chapter 1. of τ approx that produces the most accurate estimate for this firing pattern. 1 Exercises Chapter 1 1. Generate spike sequences with a constant firing rate r 0 using a Poisson spike generator. Then, add a refractory period to the model by allowing the firing rate r(t) to depend

More information

Regulation of interneuron excitability by gap junction coupling with principal cells

Regulation of interneuron excitability by gap junction coupling with principal cells Regulation of interneuron excitability by gap junction coupling with principal cells Pierre F Apostolides 1,2 & Laurence O Trussell 2 Electrical coupling of inhibitory interneurons can synchronize activity

More information

Kinetics of both synchronous and asynchronous quantal release during trains of action potential-evoked EPSCs at the rat calyx of Held

Kinetics of both synchronous and asynchronous quantal release during trains of action potential-evoked EPSCs at the rat calyx of Held J Physiol 585.2 (27) pp 361 381 361 Kinetics of both synchronous and asynchronous quantal release during trains of action potential-evoked EPSCs at the rat calyx of Held V. Scheuss, H. Taschenberger and

More information

Single-Compartment Neural Models

Single-Compartment Neural Models Single-Compartment Neural Models BENG/BGGN 260 Neurodynamics University of California, San Diego Week 2 BENG/BGGN 260 Neurodynamics (UCSD) Single-Compartment Neural Models Week 2 1 / 18 Reading Materials

More information

Integration of synaptic inputs in dendritic trees

Integration of synaptic inputs in dendritic trees Integration of synaptic inputs in dendritic trees Theoretical Neuroscience Fabrizio Gabbiani Division of Neuroscience Baylor College of Medicine One Baylor Plaza Houston, TX 77030 e-mail:gabbiani@bcm.tmc.edu

More information

Lecture 11 : Simple Neuron Models. Dr Eileen Nugent

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

More information

Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity

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

More information

3 Detector vs. Computer

3 Detector vs. Computer 1 Neurons 1. The detector model. Also keep in mind this material gets elaborated w/the simulations, and the earliest material is often hardest for those w/primarily psych background. 2. Biological properties

More information

Balance of Electric and Diffusion Forces

Balance of Electric and Diffusion Forces Balance of Electric and Diffusion Forces Ions flow into and out of the neuron under the forces of electricity and concentration gradients (diffusion). The net result is a electric potential difference

More information

Activity Driven Adaptive Stochastic. Resonance. Gregor Wenning and Klaus Obermayer. Technical University of Berlin.

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

More information

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

Membrane Potentials, Action Potentials, and Synaptic Transmission. Membrane Potential Cl Cl - - + K + K+ K + K Cl - 2/2/15 Membrane Potentials, Action Potentials, and Synaptic Transmission Core Curriculum II Spring 2015 Membrane Potential Example 1: K +, Cl - equally permeant no charge

More information

Stochastic Synchronization in Purkinje Cells with Feedforward Inhibition Could Be Studied with Equivalent Phase-Response Curves

Stochastic Synchronization in Purkinje Cells with Feedforward Inhibition Could Be Studied with Equivalent Phase-Response Curves Journal of Mathematical Neuroscience (2015) 5:13 DOI 10.1186/s13408-015-0025-6 RESEARCH OpenAccess Stochastic Synchronization in Purkinje Cells with Feedforward Inhibition Could Be Studied with Equivalent

More information

The firing of an excitable neuron in the presence of stochastic trains of strong synaptic inputs

The firing of an excitable neuron in the presence of stochastic trains of strong synaptic inputs The firing of an excitable neuron in the presence of stochastic trains of strong synaptic inputs Jonathan Rubin Department of Mathematics University of Pittsburgh Pittsburgh, PA 15260 USA Krešimir Josić

More information

Switched-Capacitor Circuits David Johns and Ken Martin University of Toronto

Switched-Capacitor Circuits David Johns and Ken Martin University of Toronto Switched-Capacitor Circuits David Johns and Ken Martin University of Toronto (johns@eecg.toronto.edu) (martin@eecg.toronto.edu) University of Toronto 1 of 60 Basic Building Blocks Opamps Ideal opamps usually

More information

Application of density estimation methods to quantal analysis

Application of density estimation methods to quantal analysis Application of density estimation methods to quantal analysis Koichi Yoshioka Tokyo Medical and Dental University Summary There has been controversy for the quantal nature of neurotransmission of mammalian

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION doi:10.1038/nature11492 Figure S1 Short-period Seismic Energy Release Pattern Imaged by F-net. (a) Locations of broadband seismograph stations in Japanese F-net used for the 0.5-2.0 Hz P wave back-projection

More information

Biological Modeling of Neural Networks:

Biological Modeling of Neural Networks: 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

More information

THE TRANSFER AND PROPAGATION OF CORRELATED NEURONAL ACTIVITY

THE TRANSFER AND PROPAGATION OF CORRELATED NEURONAL ACTIVITY THE TRANSFER AND PROPAGATION OF CORRELATED NEURONAL ACTIVITY A Dissertation Presented to the Faculty of the Department of Mathematics University of Houston In Partial Fulfillment of the Requirements for

More information

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

Mathematical Foundations of Neuroscience - Lecture 3. Electrophysiology of neurons - continued Mathematical Foundations of Neuroscience - Lecture 3. Electrophysiology of neurons - continued Filip Piękniewski Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Toruń, Poland

More information

Voltage-clamp and Hodgkin-Huxley models

Voltage-clamp and Hodgkin-Huxley models Voltage-clamp and Hodgkin-Huxley models Read: Hille, Chapters 2-5 (best Koch, Chapters 6, 8, 9 See also Hodgkin and Huxley, J. Physiol. 117:500-544 (1952. (the source Clay, J. Neurophysiol. 80:903-913

More information

Dynamics of populations and networks of neurons with voltage-activated and calcium-activated currents

Dynamics of populations and networks of neurons with voltage-activated and calcium-activated currents PHYSIAL REVIEW E 8, 21928 29 Dynamics of populations and networks of neurons with voltage-activated and calcium-activated currents Magnus J. E. Richardson* Warwick Systems Biology entre, University of

More information

Characterizing the firing properties of an adaptive

Characterizing the firing properties of an adaptive Characterizing the firing properties of an adaptive analog VLSI neuron Daniel Ben Dayan Rubin 1,2, Elisabetta Chicca 2, and Giacomo Indiveri 2 1 Department of Bioengineering, Politecnico di Milano, P.zza

More information

Voltage-clamp and Hodgkin-Huxley models

Voltage-clamp and Hodgkin-Huxley models Voltage-clamp and Hodgkin-Huxley models Read: Hille, Chapters 2-5 (best) Koch, Chapters 6, 8, 9 See also Clay, J. Neurophysiol. 80:903-913 (1998) (for a recent version of the HH squid axon model) Rothman

More information

Patterns, Memory and Periodicity in Two-Neuron Delayed Recurrent Inhibitory Loops

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

More information

Subthreshold cross-correlations between cortical neurons: Areference model with static synapses

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

More information

9 Generation of Action Potential Hodgkin-Huxley Model

9 Generation of Action Potential Hodgkin-Huxley Model 9 Generation of Action Potential Hodgkin-Huxley Model (based on chapter 2, W.W. Lytton, Hodgkin-Huxley Model) 9. Passive and active membrane models In the previous lecture we have considered a passive

More information

The firing of an excitable neuron in the presence of stochastic trains of strong synaptic inputs

The firing of an excitable neuron in the presence of stochastic trains of strong synaptic inputs The firing of an excitable neuron in the presence of stochastic trains of strong synaptic inputs Jonathan Rubin Department of Mathematics University of Pittsburgh Pittsburgh, PA 15260 USA Krešimir Josić

More information

CSE/NB 528 Final Lecture: All Good Things Must. CSE/NB 528: Final Lecture

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

More information

Effects of synaptic conductance on the voltage distribution and firing rate of spiking neurons

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

More information

Supplementary Figure 1 Change of the Tunnelling Transmission Coefficient from the Bulk to the Surface as a result of dopant ionization Colour-map of

Supplementary Figure 1 Change of the Tunnelling Transmission Coefficient from the Bulk to the Surface as a result of dopant ionization Colour-map of Supplementary Figure 1 Change of the Tunnelling Transmission Coefficient from the Bulk to the Surface as a result of dopant ionization Colour-map of change of the tunnelling transmission coefficient through

More information

Chapter 5 Experimentally Estimating Phase Response Curves of Neurons: Theoretical and Practical Issues

Chapter 5 Experimentally Estimating Phase Response Curves of Neurons: Theoretical and Practical Issues Chapter 5 Experimentally Estimating Phase Response Curves of Neurons: Theoretical and Practical Issues Theoden Netoff, Michael A. Schwemmer, and Timothy J. Lewis Abstract Phase response curves (PRCs) characterize

More information

Neuron. Detector Model. Understanding Neural Components in Detector Model. Detector vs. Computer. Detector. Neuron. output. axon

Neuron. Detector Model. Understanding Neural Components in Detector Model. Detector vs. Computer. Detector. Neuron. output. axon Neuron Detector Model 1 The detector model. 2 Biological properties of the neuron. 3 The computational unit. Each neuron is detecting some set of conditions (e.g., smoke detector). Representation is what

More information

Neurophysiology. Danil Hammoudi.MD

Neurophysiology. Danil Hammoudi.MD Neurophysiology Danil Hammoudi.MD ACTION POTENTIAL An action potential is a wave of electrical discharge that travels along the membrane of a cell. Action potentials are an essential feature of animal

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION Supplementary Information: Photocurrent generation in semiconducting and metallic carbon nanotubes Maria Barkelid 1*, Val Zwiller 1 1 Kavli Institute of Nanoscience, Delft University of Technology, Delft,

More information

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

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

More information

Introduction and summary of the chapters

Introduction and summary of the chapters Introduction and summary of the chapters 1. Electroreception Electroreception is the ability of animal species to detect weak electric fields. It is mediated by a sensory system that occurs in some aquatic

More information

CORRELATION TRANSFER FROM BASAL GANGLIA TO THALAMUS IN PARKINSON S DISEASE. by Pamela Reitsma. B.S., University of Maine, 2007

CORRELATION TRANSFER FROM BASAL GANGLIA TO THALAMUS IN PARKINSON S DISEASE. by Pamela Reitsma. B.S., University of Maine, 2007 CORRELATION TRANSFER FROM BASAL GANGLIA TO THALAMUS IN PARKINSON S DISEASE by Pamela Reitsma B.S., University of Maine, 27 Submitted to the Graduate Faculty of the Department of Mathematics in partial

More information

Information Maximization in Single Neurons

Information Maximization in Single Neurons nformation Maximization in Single Neurons Martin Stemmler and Christof Koch Computation and Neural Systems Program Caltech 139-74 Pasadena CA 91 125 Email: stemmler@klab.caltech.edu.koch@klab.caltech.edu

More information

9 Generation of Action Potential Hodgkin-Huxley Model

9 Generation of Action Potential Hodgkin-Huxley Model 9 Generation of Action Potential Hodgkin-Huxley Model (based on chapter 12, W.W. Lytton, Hodgkin-Huxley Model) 9.1 Passive and active membrane models In the previous lecture we have considered a passive

More information

Distribution of Orientation Selectivity in Recurrent Networks of Spiking Neurons with Different Random Topologies

Distribution of Orientation Selectivity in Recurrent Networks of Spiking Neurons with Different Random Topologies RESEARCH ARTICLE Distribution of Orientation Selectivity in Recurrent Networks of Spiking Neurons with Different Random Topologies Sadra Sadeh, Stefan Rotter* Bernstein Center Freiburg & Faculty of Biology,

More information

Membrane Currents in Mammalian Ventricular Heart Muscle Fibers Using a Voltage-Clamp Technique

Membrane Currents in Mammalian Ventricular Heart Muscle Fibers Using a Voltage-Clamp Technique Membrane Currents in Mammalian Ventricular Heart Muscle Fibers Using a Voltage-Clamp Technique GERHARD GIEBISCH and SILVIO WEIDMANN From the Department of Physiology, University of Berne, Berne, Switzerland.

More information

CELL BIOLOGY - CLUTCH CH. 9 - TRANSPORT ACROSS MEMBRANES.

CELL BIOLOGY - CLUTCH CH. 9 - TRANSPORT ACROSS MEMBRANES. !! www.clutchprep.com K + K + K + K + CELL BIOLOGY - CLUTCH CONCEPT: PRINCIPLES OF TRANSMEMBRANE TRANSPORT Membranes and Gradients Cells must be able to communicate across their membrane barriers to materials

More information

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 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

More information

Neuroscience 201A Exam Key, October 7, 2014

Neuroscience 201A Exam Key, October 7, 2014 Neuroscience 201A Exam Key, October 7, 2014 Question #1 7.5 pts Consider a spherical neuron with a diameter of 20 µm and a resting potential of -70 mv. If the net negativity on the inside of the cell (all

More information

CISC 3250 Systems Neuroscience

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

More information

Nature Neuroscience: doi: /nn Supplementary Figure 1. Amygdaloid complex and evoked synaptic currents recorded in CeM amygdala neurons.

Nature Neuroscience: doi: /nn Supplementary Figure 1. Amygdaloid complex and evoked synaptic currents recorded in CeM amygdala neurons. Supplementary Figure 1 Amygdaloid complex and evoked synaptic currents recorded in CeM amygdala neurons. (a) Left: Schematic representation (modified from: Allen Brain Atlas) of coronal sections containing

More information

Neuron, volume 68 Supplemental Data

Neuron, volume 68 Supplemental Data 1 Neuron, volume 68 Supplemental Data Bassoon Speeds Vesicle Reloading at a Central Excitatory Synapse Stefan Hallermann, Anna Fejtova, Hartmut Schmidt, Annika Weyhersmüller, R. Angus Silver, Eckart D.

More information

Patterns of Synchrony in Neural Networks with Spike Adaptation

Patterns of Synchrony in Neural Networks with Spike Adaptation Patterns of Synchrony in Neural Networks with Spike Adaptation C. van Vreeswijky and D. Hanselz y yracah Institute of Physics and Center for Neural Computation, Hebrew University, Jerusalem, 9194 Israel

More information

Mathematical Foundations of Neuroscience - Lecture 9. Simple Models of Neurons and Synapses.

Mathematical Foundations of Neuroscience - Lecture 9. Simple Models of Neurons and Synapses. Mathematical Foundations of Neuroscience - Lecture 9. Simple Models of and. Filip Piękniewski Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Toruń, Poland Winter 2009/2010

More information

Outline. Neural dynamics with log-domain integrator circuits. Where it began Biophysics of membrane channels

Outline. Neural dynamics with log-domain integrator circuits. Where it began Biophysics of membrane channels Outline Neural dynamics with log-domain integrator circuits Giacomo Indiveri Neuromorphic Cognitive Systems group Institute of Neuroinformatics niversity of Zurich and ETH Zurich Dynamics of Multi-function

More information

Nature Methods: doi: /nmeth Supplementary Figure 1. In vitro screening of recombinant R-CaMP2 variants.

Nature Methods: doi: /nmeth Supplementary Figure 1. In vitro screening of recombinant R-CaMP2 variants. Supplementary Figure 1 In vitro screening of recombinant R-CaMP2 variants. Baseline fluorescence compared to R-CaMP1.07 at nominally zero calcium plotted versus dynamic range ( F/F) for 150 recombinant

More information

Integrator Windup

Integrator Windup 3.5.2. Integrator Windup 3.5.2.1. Definition So far we have mainly been concerned with linear behaviour, as is often the case with analysis and design of control systems. There is, however, one nonlinear

More information

Localized activity patterns in excitatory neuronal networks

Localized activity patterns in excitatory neuronal networks Localized activity patterns in excitatory neuronal networks Jonathan Rubin Amitabha Bose February 3, 2004 Abstract. The existence of localized activity patterns, or bumps, has been investigated in a variety

More information

Quantitative Electrophysiology

Quantitative Electrophysiology ECE 795: Quantitative Electrophysiology Notes for Lecture #4 Wednesday, October 4, 2006 7. CHEMICAL SYNAPSES AND GAP JUNCTIONS We will look at: Chemical synapses in the nervous system Gap junctions in

More information

Connecting Epilepsy and Alzheimer s Disease: A Computational Modeling Framework

Connecting Epilepsy and Alzheimer s Disease: A Computational Modeling Framework Connecting Epilepsy and Alzheimer s Disease: A Computational Modeling Framework Péter Érdi perdi@kzoo.edu Henry R. Luce Professor Center for Complex Systems Studies Kalamazoo College http://people.kzoo.edu/

More information

PH213 Chapter 24 Solutions

PH213 Chapter 24 Solutions PH213 Chapter 24 Solutions 24.12. IDENTIFY and S ET UP: Use the expression for derived in Example 24.4. Then use Eq. (24.1) to calculate Q. E XECUTE: (a) From Example 24.4, The conductor at higher potential

More information

Ch. 5. Membrane Potentials and Action Potentials

Ch. 5. Membrane Potentials and Action Potentials Ch. 5. Membrane Potentials and Action Potentials Basic Physics of Membrane Potentials Nerve and muscle cells: Excitable Capable of generating rapidly changing electrochemical impulses at their membranes

More information

Label-Free Single-Molecule Thermoscopy Using a Laser-Heated Nanopore

Label-Free Single-Molecule Thermoscopy Using a Laser-Heated Nanopore Supporting information for Label-Free Single-Molecule Thermoscopy Using a Laser-Heated Nanopore By Hirohito Yamazaki, Rui Hu,, Robert Y. Henley, Justin Halman, Kirill A. Afonin, Dapeng Yu, Qing Zhao, and

More information

Synaptic input statistics tune the variability and reproducibility of neuronal responses

Synaptic input statistics tune the variability and reproducibility of neuronal responses CHAOS 16, 06105 006 Synaptic input statistics tune the variability and reproducibility of neuronal responses Alan D. Dorval II a and John A. White Department of Biomedical Engineering, Center for BioDynamics,

More information

A Generalized Linear Integrate-And-Fire Neural. Model Produces Diverse Spiking Behaviors

A Generalized Linear Integrate-And-Fire Neural. Model Produces Diverse Spiking Behaviors A Generalized Linear Integrate-And-Fire Neural Model Produces Diverse Spiking Behaviors Ştefan Mihalaş and Ernst Niebur Zanvyl Krieger Mind/Brain Institute and Department of Neuroscience Johns Hopkins

More information

The Spike Response Model: A Framework to Predict Neuronal Spike Trains

The Spike Response Model: A Framework to Predict Neuronal Spike Trains The Spike Response Model: A Framework to Predict Neuronal Spike Trains Renaud Jolivet, Timothy J. Lewis 2, and Wulfram Gerstner Laboratory of Computational Neuroscience, Swiss Federal Institute of Technology

More information

Mathematical Models of Cochlear Nucleus Onset Neurons: II. Model with Dynamic Spike-Blocking State

Mathematical Models of Cochlear Nucleus Onset Neurons: II. Model with Dynamic Spike-Blocking State Journal of Computational Neuroscience 14, 91 110, 2003 c 2003 Kluwer Academic Publishers. Manufactured in The Netherlands. Mathematical Models of Cochlear Nucleus Onset Neurons: II. Model with Dynamic

More information

Single Neuron Dynamics for Retaining and Destroying Network Information?

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

More information

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 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

More information

Mathematical Models of Dynamic Behavior of Individual Neural Networks of Central Nervous System

Mathematical Models of Dynamic Behavior of Individual Neural Networks of Central Nervous System Mathematical Models of Dynamic Behavior of Individual Neural Networks of Central Nervous System Dimitra-Despoina Pagania,*, Adam Adamopoulos,2, and Spiridon D. Likothanassis Pattern Recognition Laboratory,

More information

Electrophysiological Modeling of Membranes and Cells

Electrophysiological Modeling of Membranes and Cells Bioeng 6460 Electrophysiology and Bioelectricity Electrophysiological Modeling of Membranes and Cells Frank B. Sachse fs@cvrti.utah.edu Overview Recapitulation Electrical Modeling of Membranes Cardiac

More information

Activity types in a neural mass model

Activity types in a neural mass model Master thesis Activity types in a neural mass model Jurgen Hebbink September 19, 214 Exam committee: Prof. Dr. S.A. van Gils (UT) Dr. H.G.E. Meijer (UT) Dr. G.J.M. Huiskamp (UMC Utrecht) Contents 1 Introduction

More information

PHYSICS. Downloaded From: Time Allowed : 3 Hrs. Max. Marks 60

PHYSICS. Downloaded From:   Time Allowed : 3 Hrs. Max. Marks 60 PHYSICS Time Allowed : Hrs. Max. Marks 60 * Candidates are required to give their answers in their own words as far as practicable. * Marks allotted to each question are indicated against it. Special Instructions

More information