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neworderofjamie

#include "definitionsInternal.h"
#include "supportCode.h"

extern "C" __global__ void preNeuronResetKernel() {
    unsigned int id = 32 * blockIdx.x + threadIdx.x;
    if(id == 0) {
        dd_glbSpkCntE[0] = 0;
    }
    else if(id == 1) {
        dd_glbSpkCntI[0] = 0;

neworderofjamie

#include <fstream>
#include <string>
#include <thread>
#include <vector>

void write(unsigned int thread)
{
    std::ofstream stream("test_" + std::to_string(thread) + ".txt");
    stream << "test" << std::endl;
    stream << "thread" << std::endl;

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",

neworderofjamie

neworderofjamie

import os, pathlib
import tensorflow as tf
from tensorflow.keras import (models, layers, datasets, callbacks, optimizers,
                              initializers, regularizers)
from tensorflow.keras.utils import CustomObjectScope
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from glob import glob
import numpy as np

# Learning rate schedule

neworderofjamie

// C++ includes
#include <algorithm>
#include <fstream>

// ------------------------------------------------------------------------
// Helper macro for error-checking CUDA calls
#define CHECK_CUDA_ERRORS(call) {\
    cudaError_t error = call;\
    if (error != cudaSuccess) {\
        throw std::runtime_error(__FILE__": " + std::to_string(__LINE__) + ": cuda error " + std::to_string(error) + ": " + cudaGetErrorString(error));\

neworderofjamie

import numpy as np
import matplotlib.pyplot as plt

spikes = np.loadtxt("spikes.csv", delimiter=",")
voltages = np.loadtxt("voltages.csv", delimiter=",")

fig, axes = plt.subplots(5, sharex=True)

for i in range(4):
    axes[i].plot(voltages[:,0], voltages[:,i+1])

neworderofjamie

import numpy as np
from os import path

from ml_genn import InputLayer, Layer, SequentialModel
from ml_genn.compilers import Compiler
from ml_genn.neurons import IntegrateFire, IntegrateFireInput
from ml_genn.connectivity import Dense

# Load weights
weights = []