resistingrhythm

Code for Homeostatic mechanisms may shape the type and duration of oscillatory modulation bioArxiv 615450v1 (2019)

A computational study of modulation, oscillation, and homeostasis.

Abstract

Neural oscillations are observed ubiquitously in the mammalian brain. However the stability of oscillations is highly variable. Some oscillations are tonic, lasting for seconds or even minutes; others are unstable, appearing only as a single-cycle burst. In a model of hippocampal neurons, we use numerical simulations to show how these different forms of rhythm stability can interact with activity-dependent homeostasis to profoundly alter the modulatory effect of neural oscillations. Under homeostasis, tonic oscillations that are synaptically excitatory have a paradoxical effect; they decrease excitability and desynchronizing firing. Tonic oscillations that are synaptically inhibitory–like those in a real hippocampus–fail to generate new action potentials and so provoke no homeostatic response. This may explain why the theta rhythm in hippocampus is synaptically inhibitory: inhibitory oscillations don’t raise the firing threshold, as excitatory oscillations do, and so can preserve each cell’s dynamic range. Based on these simulations, we also speculate that homeostasis may explain why excitatory intra-cortical and intra-layer oscillations often appear as bursts. In our model bursts minimally interact with the slow homeostasis time constant and so retain typical excitatory effects.

Other publications

This work was presented at SFN2018. A copy of the poster can be found here. To re-run all experiments:

1. Install this package and its dependencies (see below).

2. At the command line, and from \exp directory, run

   • make stim3 stim4 osc100 burst100 then

   • make exp210 exp211 exp212 exp213 exp214 exp215 exp216 exp217 exp218.

   • Note: you may need to adjust the $DATA_PATH variable in the Makefile.

   • The experimental recipes rely on gnu parallel, and are configured to a 40 core machine. If you have more or fewer cores, adjust the -j 38 argument in each recipe accordingly. The Makefile is here.

   • Note: with the current configuration these simulations take about 4 days.

3. Once (2) is complete open papers_figures_v3.Rmd (found here), adjust the data_path, and execute all cells.

4. To generate the example traces, open testing_HHH.ipynb (found here) and re-run all cells. This should take half an hour or so. Step (4) can be completed anytime; it is not dependent on 1-3.

Installation

From the command line (on linux or macOS) run,

• git clone git@github.com:voytekresearch/resistingrhythm.git

then

• cd resistingrhythm; pip install -e .

Dependencies

• A standard Python >3.6 anaconda install (https://www.anaconda.com/download)
• brian2 (https://brian2.readthedocs.io/en/stable/)
• fire (https://github.com/google/python-fire)
• fakespikes (https://github.com/voytekresearch/fakespikes)
• make (the standard unix utility)
• GNU parallel (https://www.gnu.org/software/parallel/)