<p>I've done a lot of thinking and some casual quantitative exploration with computational neuroscience – like running PCA on some neural activity data I obtained, some analysis of the static connectome of the organism I'm studying, etc. and now I want to develop the hard skills necessary in computational neuroscience. </p>

<p>I'd like to begin playing around with parameterizing neurons, specifically I'm looking to find the minimal parameters for oscillatory dynamics in a neuronal network. If I'm using the hodkgin huxley model, how would I parameterize a self-connected neuron to exhibit oscillatory dynamics at its membrane potential? I imagine some form of bifurcation theory is relevant but don't know how to put it into practice. </p>

CC BY-SA 3.0
2016-03-30T15:18:28.513Z
do you have dynamical systems in neuroscience by izhikevich?  it goes into depth with single-neuron oscillations.
honi
CC BY-SA 3.0
2016-03-30T15:19:38.897Z
additionally, oscillations are generally thought to come from circuit dynamics, such as the pyramidal-interneuron loop in nancy kopell's PING model of gamma oscillations.
honi
CC BY-SA 3.0
2016-03-30T21:51:06.110Z
@honi (1) I believe you've answered another one of my questions and I really appreciate it. (2) I'm going to check those two things out. I'm thinking to begin I'll use an optimization algorithm to minimize the error between the time derivative of the neural data to a circle around the axis. Then I'll get more complicated but I want to try simple things out myself.
theideasmith
<agent-based-modeling>
How would I train a self-connected hodgkin huxley neuron to exhibit oscillatory dynamics
theideasmith
# How would I train a self-connected hodgkin huxley neuron to exhibit oscillatory dynamics

(Asked by: username_0 on 2016-03-29)

I've done a lot of thinking and some casual quantitative exploration with computational neuroscience – like running PCA on some neural activity data I obtained, some analysis of the static connectome of the organism I'm studying, etc. and now I want to develop the hard skills necessary in computational neuroscience.

I'd like to begin playing around with parameterizing neurons, specifically I'm looking to find the minimal parameters for oscillatory dynamics in a neuronal network. If I'm using the hodkgin huxley model, how would I parameterize a self-connected neuron to exhibit oscillatory dynamics at its membrane potential? I imagine some form of bifurcation theory is relevant but don't know how to put it into practice.

**Tags:** agent-based-modeling

> **Comments:**
> **username_1**: do you have dynamical systems in neuroscience by izhikevich?  it goes into depth with single-neuron oscillations.  
> **username_1**: additionally, oscillations are generally thought to come from circuit dynamics, such as the pyramidal-interneuron loop in nancy kopell's PING model of gamma oscillations.  
> **username_0**: @username_1 (1) I believe you've answered another one of my questions and I really appreciate it. (2) I'm going to check those two things out. I'm thinking to begin I'll use an optimization algorithm to minimize the error between the time derivative of the neural data to a circle around the axis. Then I'll get more complicated but I want to try simple things out myself.  

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## 0 answers

