russelljjarvis

using SparseArrays
using StaticArrays
using ProgressMeter
using LinearAlgebra

"""
This file contains a function stack that creates a network with Potjans and Diesmann wiring likeness in Julia using SpikingNeuralNetworks.jl to simulate
electrical neural network dynamics.
This code draws heavily on the PyNN OSB Potjans implementation code found here:
https://github.com/OpenSourceBrain/PotjansDiesmann2014/blob/master/PyNN/network_params.py#L139-L146

russelljjarvis

"""
I destroyed the generalness of the previous typing system which only worked for mutable kwarg structs

This approach works but with unsophisticated typing.
"""


abstract type AbstractIFParameter end
struct IFParameter# <: AbstractIFParameter
    τm::Float32# = 20ms

russelljjarvis

Hi Raf

For a while I have been trying to massage this Python model into Julia, in part unsuccessfully.

https://portal.brain-map.org/explore/models/mv1-all-layers

The model is interesting for your grid simulator, as the neurons are arranged in pixel like grids (cells have both cartesian pixel indexs, and spatial coordinates).

russelljjarvis

Hi @rafaqz

I reimplemented the Potjan's Diesmann synaptic wiring rules from Python (https://github.com/nest/nest-simulator/blob/master/pynest/examples/Potjans_2014/network_params.py) to Julia below.

The result actually works with the SNN package, we were talking about in Signal.

Its supposed to wire up to millions of cells, it comfortably wires 500,000 cells in less than 0.4seconds, but for 5,000,000 it takes an hour to create the matrix on a machine with 64GB of RAM.

russelljjarvis

Hi @ali-mehrabi,

To automatic docker building in github. Click on the tab "actions" in your repository.

Then you can create an action similar to this:

https://github.com/pabloabur/brian2-sims/blob/main/.github/workflows/docker-image.yml#L18-L20

lines 18-20 build and run the dockerfile.

russelljjarvis

include("src/genWeightsPotjans.jl")

ccu = Dict("23E"=>20683,
            "4E"=>21915, 
            "5E"=>4850, 
            "6E"=>14395, "6I"=>2948, "23I"=>5834,"5I"=>1065,"4I"=>5479)
ccu = Dict((k,ceil(Int64,v)) for (k,v) in pairs(ccu))

Ncells = sum([i for i in values(ccu)])+1

russelljjarvis

sing LinearAlgebra, Random, Statistics, StatsBase, ArgParse, SymmetricFormats
using Plots
using StatsBase
using Random
using ProgressMeter
using StatsPlots
using Plots
#using UnicodePlots
unicodeplots()

russelljjarvis

using SparseArrays
#using ProgressMeter
using UnicodePlots
function potjans_params(scale_N_cells=1.0)
    """
    This code draws heavily on the PyNN OSB Potjans implementation code found here:
    https://github.com/OpenSourceBrain/PotjansDiesmann2014/blob/master/PyNN/network_params.py#L139-L146
    """
    conn_probs = [[0.1009,  0.1689, 0.0437, 0.0818, 0.0323, 0.,     0.0076, 0.    ],
                [0.1346,   0.1371, 0.0316, 0.0515, 0.0755, 0.,     0.0042, 0.    ],

russelljjarvis

https://github.com/scidash/sciunit/blob/master/docker/Dockerfile
```
FROM jupyter/datascience-notebook
RUN apt-get update && \
    apt-get upgrade -y

# Install Python, pip and swig
RUN apt-get install -yq --no-install-recommends python3-dev python3-pip swig gosu nano

russelljjarvis

import numpy as np
import pandas as pd
import json
import pickle
from pathlib import Path
from sonata.circuit import File
from sonata.reports.spike_trains import SpikeTrains
import pygenn
import matplotlib.pyplot as plt
from GLIF_models import GLIF1