Models | Description | |

1. | Astrocyte and Blood Vessel Calcium Imaging Tracking code (Haidey et al 2021) | |

Code for tracking Astrocytes/Blood vessels in Calcium Imaging based recordings from ... Data files should be loaded as .tiff stacks with several options for processing (see matlab file). The main-script is tracking_script.m which can be used to track multiple .tiff stacks simultaneously The sub-function distance finder outputs various metrics computed from the blood vessel tracking, (e.g. blood vessel width measured in several ways, etc.) These metrics, among others (e.g. cross sectional area, etc.) are then recorded into an .xls file as the final output. For further details, please see Haidey JN, Peringod G, Institoris A, Gorzo KA, Nicola W, Vandal M, Ito K, Liu S, Fielding C, Visser F, Nguyen MD. Astrocytes regulate ultra-slow arteriole oscillations via stretch-mediated TRPV4-COX-1 feedback. Cell Reports. 2021 Aug 3;36(5):109405. https://doi.org/10.1016/j.celrep.2021.109405 | ||

2. | Mean Field Equations for Two-Dimensional Integrate and Fire Models (Nicola and Campbell, 2013) | |

The zip file contains the files used to perform numerical simulation and bifurcation studies of large networks of two-dimensional integrate and fire neurons and of the corresponding mean field models derived in our paper. The neural models used are the Izhikevich model and the Adaptive Exponential model. | ||

3. | Mean-field systems and small scale neural networks (Ferguson et al. 2015) | |

We explore adaptation induced bursting as a mechanism for theta oscillations in hippocampal area CA1. To do this, we have developed a mean-field system for a network of fitted Izhikevich neurons with sparse coupling and heterogeneity. The code contained here runs the mean-field system pointwise or on a two-parameter mesh, in addition to networks of neurons that are smaller then those considered in the paper. The file README.pdf contains instructions on use. Note that the following file (peakfinder): http://www.mathworks.com/matlabcentral/fileexchange/25500-peakfinder-x0--sel--thresh--extrema--includeendpoints--interpolate- is required to compute burst frequencies in the mean-field system and must be downloaded and placed in the same root folder as MFSIMULATOR.mat | ||

4. | SHOT-CA3, RO-CA1 Training, & Simulation CODE in models of hippocampal replay (Nicola & Clopath 2019) | |

In this code, we model the interaction between the medial septum and hippocampus as a FORCE trained, dual oscillator model. One oscillator corresponds to the medial septum and serves as an input, while a FORCE trained network of LIF neurons acts as a model of the CA3. We refer to this entire model as the Septal Hippocampal Oscillator Theta (or SHOT) network. The code contained in this upload allows a user to train a SHOT network, train a population of reversion interneurons, and simulate the SHOT-CA3 and RO-CA1 networks after training. The code scripts are labeled to correspond to the figure from the manuscript. | ||

5. | Supervised learning in spiking neural networks with FORCE training (Nicola & Clopath 2017) | |

The code contained in the zip file runs FORCE training for various examples from the paper: Figure 2 (Oscillators and Chaotic Attractor) Figure 3 (Ode to Joy) Figure 4 (Song Bird Example) Figure 5 (Movie Example) Supplementary Figures 10-12 (Classifier) Supplementary Ode to Joy Example Supplementary Figure 2 (Oscillator Panel) Supplementary Figure 17 (Long Ode to Joy) Note that due to file size limitations, the supervisors for Figures 4/5 are not included. See Nicola, W., & Clopath, C. (2016). Supervised Learning in Spiking Neural Networks with FORCE Training. arXiv preprint arXiv:1609.02545. for further details. | ||

6. | Wilson-Cowan Network with Homeostatic Plasticity (Nicola and Campbell 2021) | |

We investigate the problem of inter-region synchronization in networks of Wilson--Cowan/neural field equations with homeostatic plasticity, each of which acts as a model for an isolated brain region. We consider arbitrary connection profiles with only one constraint: the rows of the connection matrices are all identically normalized. We found that these systems often synchronize to the solution obtained from a single, self-coupled neural region. We analyze the stability of this solution through a straightforward modification of the master stability function (MSF) approach and found that synchronized solutions lose stability for connectivity matrices when the second largest positive eigenvalue is sufficiently large for values of the global coupling parameter that are not too large. This result was numerically confirmed for ring systems and lattices and was also robust to small amounts of heterogeneity in the homeostatic set points in each node. Finally, we tested this result on connectomes obtained from 196 subjects over a broad age range (4--85 years) from the Human Connectome Project. |