Hodkin Huxley Models for Cellular Neuroscience

  • Spiking neuron model.
  • Voltage clamp mode steps voltages.
  • Anode break code.
  • Propagating spike.
  • All variables as a function of V.
  • Current ramped up slowly.

    • Computational Neuroscience NBIO 136b Spring 2007

    THIS IS LAST YEAR'S COURSE! --- MAY STILL BE USEFUL THOUGH

    Matlab Primer

    If you are thinking of taking this class, it is worth working through this Matlab primer ahead of time if you can --- that way, you'll have less to learn when class starts.

  • Introductory tutorial for Matlab
  • Matlab Quick Reference sheet.
  • Matlab checklist PDF file.

    • Matlab Tutorial files

  • Basic Matlab tutorial. or Word file of tutorial is better than web page
  • Basic Matlab exercises. or Word document of exercises is better.

    • Links to some neuroscience teaching animations

  • North Harris College
  • Blackwell Publishing
  • Georgia Southwestern
  • Daniel Simons Visual Attention

    • Lecture 1, Jan 17th 2007: Matlab files

  • Exponential growth
  • Exponential decay
  • Integration of a cosine to give a sine then adding noise

    • Lecture 3, Jan 24th 2007

  • PDF file of Leaky-integrate-and-fire neuron

    • Lecture 4, Jan 26th 2007: Matlab files

    Files that produce spike trains (comments to follow soon)

  • Regular spikes with a rate that has two values
  • Homogeneous Poisson train
  • Several trials of Poisson with ramping rate
  • Poisson with oscillating rate

    • Codes that analyze spike trains

  • Calculates ISI distribution, then CV and CV_2 (1 trial)
  • Calculates Fano factor, F(t) needs many trials (ntrials)
  • Calculates autocorrelation function (1 trial)

    • Lecture 5, Jan 30th 2007: Matlab file

  • Connors-Stevens conductance-based model

    • Lecture 6, Feb 2nd 2007: Matlab files

  • Response to voltage steps of Connors-Stevens model
  • Plots of voltage-dependence of conductances for Ca_T and Connors-Stevens model
  • Leaky integrate-and-fire model with spike-rate adaptation and a conductance-based refractory period

    • Lecture 7, Feb 6th 2007 Matlab files and handout

  • Handout on bursting and pacemaker circuits
  • Post-inhibitory rebound (thalamic relay) model
  • Two-compartment alternative to Pinsky-Rinzel model
  • Two-compartment Pinsky-Rinzel model

    • Lecture 9, Feb 13th 2007 Matlab file

  • Phase response curve calculation with the Connors-Stevens model

    • Lecture 10, Feb 16th 2007 Matlab files

  • Pair of post-inhibitory rebound neurons coupled by inhibition to oscillate in out of phase bursts.
  • Regular train of spikes through depressing and facilitating synapses
  • Poisson spike trains with steps in rate through depressing and facilitating synapses

    • Lecture 11, Feb 27th 2007

  • Encoding and spike-triggered average
  • Papers on WebCT

    • Lecture 13, March 6th 2007

  • Decoding and ROC analysis
  • Matlab code to show kernel integration for HW4

    • Lecture 14, March 9th 2007

  • Noisy spike trains and Hidden Markov Models
  • Matlab code to show trial-averaging of Poisson spikes from a ramping rate
  • Matlab code to show trial-averaging of Poisson spikes from a randomly jumping rate

    • Lecture 15, March 13th 2007

  • Firing Rate Models
  • Matlab code to generate f-I curve of a noisy LIF neuron (comments to follow)
  • Matlab code to look at average synaptic input from a group of cells with Poisson spiking (comments to follow)

    • Lecture 16, March 16th 2007

  • Uncommented orientation tuning Matlab code for use with homework 5
  • Papers on orientation tuning can be found in WebCT

    • Lecture 17, March 20th 2007

  • Commented discrete memory code
  • Commented spatial memory code, could be useful for HW5
  • Power point presentation from class on memory

    • Lecture 18, March 23rd 2007

  • Winner-takes-all decision-making code.
  • Noisy decision-making code.

    • Lecture 19, March 27th 2007

  • Simple recurrent feedback continuous memory/integrator rate code.
  • Many bistable units with a readout cell (rate model).
  • Handout on attractors and integrators.

    • Lecture 20, March 30th 2007

  • Code for memory-based decisions needed for HW6.
  • Powerpoint presentation.

    • Lecture 21, April 13th 2007

  • Code for spike-timing dependent plasticity between rate-model cells that undergo sequential, overlapping input
  • Code that segregates inputs from two groups of correlated cells via STDP on an LIF neuron.
  • Handout on Hebbian plasticity.

    • Lecture 22, April 17th 2007

  • Powerpoint presentation on the CaMKII/PP1 model of LTP.
  • Class Handout on CaMKII and stochastic modeling.

    • Lecture 23, April 20th 2007

  • Papers on WebCT.
  • Code that demonstrates the effects of heterogeneity on spatial memory.

    • Lecture 25, April 27th 2007

  • Code to implement eyeblink conditioning.

    • Lecture 26, May 1st 2007

  • Code to implement the weather prediction task by reward-based plasticity.
  • Code to implement the matching task.
    • Last modified April 30th, 2007

    Paul Miller, pmiller@brandeis.edu

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