Computational model of rodent somatosensory cortical column

About

Simulation of one or more barrel cortical columns. The model is a biologically inspired, computationally efficient network model of somatosensory cortical columns. It was created by (1) reconstructing the barrel cortex in soma resolution using multi-channel mosaic scanning confocal microscopy, (2) defining a mathematical model (Izhikevich model) of cortical neurons whose action potential threshold adapts to the rate of ongoing network activity impinging onto the postsynaptic neuron and (3) connecting each neuron in the network using statistical rules of pair-wise connectivity based on experimental observations. The input consists of whisker data (here: angle and curvature, but other metrics such as deviation from baseline are also possible). Based on this, thalamic spike trains are generated (thalamic neurons are considered simple filter-and-fire Poisson neurons), that then form the input to the cortical model.

Installation

Download the .zip-file and unzip, preserving the folder structure. The model will run when this structure is added to the path (addpath(genpath([‘.’])), as is done in run_sim.m).

General

A simple example simulation can be run by executing the script quick_example, which uses the function ‘run_sim’ (this function controls the simulation, and can be used as an example for doing user-defined simulations). A 3x1 grid of barrels in L4 and L23 will be constructed, with corresponding thalamic barreloids. Thalamic input spike trains will be generated in reaction whisker input (in this case: whisker base angles and curvatures from the lab Karel Svoboda, publicly available on Data Sets — CRCNS.org), and the cortical model will respond to these thalamic spike trains.

Input

As input, data files (whisker base angle and curvature) of the lab Karel Svoboda were used (publicly available on Data Sets — CRCNS.org). The specified whisker data files are expected to be present in folder ‘Input data’. The function ‘make_thalamic_spike_trains_svoboda_recordings’ in folder ‘Make_New_Thinput’ makes spike trains from the whisker data. For the use of any other input, make a file similar to this example.

Simulation

A full simulation includes:

1. generating new input spike trains (make_new_thalamic_input = 1), relevant functions in ‘Make_New_Thinput’
2. making new thalamic filter neurons (make_new_thalamic_kernels = 1), relevant functions in ‘Make_New_Thinput’
3. Make a new realisation of the network connectivity (make_new_connectivity=1), relevant functions in ‘Make_New_Connectivity’
4. Initialising and running the network, relevant functions in ‘Network Simulations’

Results

This will result in the following files in folder ‘Simulation Results -> (user defined name)

• cellinfo: information about all cells: (Number of Cells-by 6) matrix, with
  • the first 3 columns are the location of each cell
    • first column: position from the middle of the barrel (~rostral-caudal)
    • second column: position from the middle of the barrel (~dorsal-ventral)
    • third column: depth, distance from pia
  • 4th column is the cell type
    • 1 - L4 spiny stallet
    • 2 - L4 pyramidal
    • 3 - L4 Fast spike
    • 4 - L4 low-threshold spike
    • 5 - L2/3 pyramidal
    • 6 - L2/3 PV+ fast spike
    • 7 - L2/3 PV+ chandler
    • 8 - L2/3 PV+ bursting
    • 9 - L2/3 SOM+ martinotti
    • 10 - L2/3 SOM+ bitufted
    • 11 - VIP+ double bouquet
    • 12 - VIP+ bipolar
    • 13 - CR+ bipolar
    • 14 - CR+ multipolar/basket
    • 15 - neurogliaform Note: some L2/3 types have been merged with other types so in the matrix the numbers are 0);
  • 5th column is the barrel identification (different number indication different barrels).
  • 6th column is Exe/Inh indicator (1 for excitatory and -1 for inhibitory neurons)
• CMDMs: all connectivity data
• CMDMs_(…)_ConData
• CMDMs_(…)_ParaMat
• CMDMs_(…)_ParaMat_reduced: a reduced version of the previous set, to speed up simulations
• (…)_initialsettings: the initial settings to each simulation
• (…)Simcolumn(…)_(simulation number): spike times and membrane potentials of each simuation
• (…)_Thalamic_Kernels: thalamic filters for each thalamic neuron
• (…)_Thalamic_Spike_Trains: all input spike trains and whisker traces