recurrent_networks/Masse_2019_STP_RNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fefaa3ad",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "# *(Masse, et al., 2019)*: RNN with STP for Working Memory \n",
    "\n",
    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/examples/blob/main/recurrent_networks/Masse_2019_STP_RNN.ipynb)\n",
    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/examples/blob/main/recurrent_networks/Masse_2019_STP_RNN.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11868e2f",
   "metadata": {},
   "source": [
    "Re-implementation of the paper with BrainPy:\n",
    "\n",
    "- Masse, Nicolas Y., Guangyu R. Yang, H. Francis Song,\n",
    "  Xiao-Jing Wang, and David J. Freedman. \"Circuit mechanisms for\n",
    "  the maintenance and manipulation of information in working\n",
    "  memory.\" Nature neuroscience 22, no. 7 (2019): 1159-1167.\n",
    "\n",
    "Thanks the original GitHub code: https://github.com/nmasse/Short-term-plasticity-RNN\n",
    "\n",
    "The code for the implementation of Task please refer to the [Masse_2019_STP_RNN_tasks.py](https://github.com/PKU-NIP-Lab/BrainPyExamples/blob/main/recurrent_networks/Masse_2019_STP_RNN_tasks.py).\n",
    "\n",
    "The analysis methods please refer to the original repository: https://github.com/nmasse/Short-term-plasticity-RNN/blob/master/analysis.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fc28b98e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import brainpy as bp\n",
    "import brainpy.math as bm\n",
    "bp.math.set_platform('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7b1a9279",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "import pickle\n",
    "import numpy as np\n",
    "from Masse_2019_STP_RNN_tasks import Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5177ce22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time parameters\n",
    "dt = 100  # ms\n",
    "dt_sec = dt / 1000\n",
    "time_constant = 100  # ms\n",
    "alpha = dt / time_constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9f4913c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loss parameters\n",
    "spike_regularization = 'L2'  # 'L1' or 'L2'\n",
    "spike_cost = 2e-2\n",
    "weight_cost = 0.\n",
    "clip_max_grad_val = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2e7cb412",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training specs\n",
    "batch_size = 1024\n",
    "learning_rate = 2e-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3ab0625b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize(shape, prob, size):\n",
    "  w = bm.random.gamma(shape, size=size)\n",
    "  w *= (bm.random.random(size) < prob)\n",
    "  return bm.asarray(w, dtype=bm.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f15cb3b",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "091b401f",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model(bp.DynamicalSystem):\n",
    "  def __init__(self, task, num_hidden=100, name=None):\n",
    "    super(Model, self).__init__(name=name)\n",
    "\n",
    "    assert isinstance(task, Task)\n",
    "    self.task = task\n",
    "\n",
    "    # Network configuration\n",
    "    self.exc_inh_prop = 0.8  # excitatory/inhibitory ratio\n",
    "    self.conn_prob = 0.2\n",
    "\n",
    "    # Network shape\n",
    "    self.num_output = task.num_output\n",
    "    self.num_hidden = num_hidden\n",
    "    self.num_input = task.num_input\n",
    "\n",
    "    # EI\n",
    "    self.num_exc = int(self.num_hidden * self.exc_inh_prop)\n",
    "    self.num_inh = self.num_hidden - self.num_exc\n",
    "    self.EI_list = bm.ones(self.num_hidden)\n",
    "    self.EI_list[self.num_exc:] = -1.\n",
    "    self.EI_matrix = bm.diag(self.EI_list)\n",
    "    self.inh_index = bm.arange(self.num_exc, self.num_hidden)\n",
    "\n",
    "    # Input and noise\n",
    "    self.noise_rnn = math.sqrt(2 * alpha) * 0.5\n",
    "\n",
    "    # Synaptic plasticity specs\n",
    "    self.tau_fast = 200  # ms\n",
    "    self.tau_slow = 1500  # ms\n",
    "    self.U_stf = 0.15\n",
    "    self.U_std = 0.45\n",
    "\n",
    "    # Initial hidden values\n",
    "    self.init_h = bm.TrainVar(bm.ones((batch_size, self.num_hidden)) * 0.1)\n",
    "    self.h = bm.Variable(bm.ones((batch_size, self.num_hidden)) * 0.1)\n",
    "\n",
    "    # Input/recurrent/output weights\n",
    "    #   1. w_ir (input => recurrent)\n",
    "    prob = self.conn_prob * task.num_receptive_fields\n",
    "    self.w_ir = bm.TrainVar(initialize(0.2, prob, (self.num_input, self.num_hidden)))\n",
    "    self.w_ir_mask = bm.ones((self.num_input, self.num_hidden))\n",
    "    if task.trial_type == 'location_DMS':\n",
    "      self.w_ir_mask *= 0.\n",
    "      target_ind = [range(0, self.num_hidden, 3), range(1, self.num_hidden, 3), range(2, self.num_hidden, 3)]\n",
    "      for n in range(self.num_input):\n",
    "        u = int(n // (self.num_input / 3))\n",
    "        self.w_ir_mask[n, target_ind[u]] = 1.\n",
    "      self.w_ir *= self.w_ir_mask  # only preserve\n",
    "    #   2. w_rr (recurrent => recurrent)\n",
    "    self.w_rr = bm.TrainVar(initialize(0.1, self.conn_prob, (self.num_hidden, self.num_hidden)))\n",
    "    self.w_rr[:, self.num_exc:] = initialize(0.2, self.conn_prob, (self.num_hidden, self.num_inh))\n",
    "    self.w_rr[self.num_exc:, :] = initialize(0.2, self.conn_prob, (self.num_inh, self.num_hidden))\n",
    "    self.w_rr_mask = bm.ones((self.num_hidden, self.num_hidden)) - bm.eye(self.num_hidden)\n",
    "    self.w_rr *= self.w_rr_mask  # remove self-connections\n",
    "    self.b_rr = bm.TrainVar(bm.zeros((1, self.num_hidden)))\n",
    "    #   3. w_ro (input => recurrent)\n",
    "    self.w_ro = bm.TrainVar(initialize(0.1, self.conn_prob, (self.num_hidden, self.num_output)))\n",
    "    self.w_ro_mask = bm.ones((self.num_hidden, self.num_output))\n",
    "    self.w_ro_mask[self.num_exc:, :] = 0.\n",
    "    self.w_ro *= self.w_ro_mask  # remove inhibitory-to-output connections\n",
    "    #   4. b_ro (bias)\n",
    "    self.b_ro = bm.TrainVar(bm.zeros((1, self.num_output)))\n",
    "\n",
    "    # Synaptic variables\n",
    "    #   - The first row (first half neurons) are facilitating synapses\n",
    "    #   - The second row (last half neurons) are depressing synapses\n",
    "    alpha_stf = bm.ones((2, int(self.num_hidden / 2)))\n",
    "    alpha_stf[0] = dt / self.tau_slow\n",
    "    alpha_stf[1] = dt / self.tau_fast\n",
    "    alpha_std = bm.ones((2, int(self.num_hidden / 2)))\n",
    "    alpha_std[0] = dt / self.tau_fast\n",
    "    alpha_std[1] = dt / self.tau_slow\n",
    "    U = bm.ones((2, int(self.num_hidden / 2)))\n",
    "    U[0] = 0.15\n",
    "    U[1] = 0.45\n",
    "    u = bm.ones((batch_size, 2, int(self.num_hidden / 2))) * 0.3\n",
    "    u[:, 0] = 0.15\n",
    "    u[:, 1] = 0.45\n",
    "    #   - final\n",
    "    self.alpha_stf = alpha_stf.reshape((1, -1))\n",
    "    self.alpha_std = alpha_std.reshape((1, -1))\n",
    "    self.U = U.reshape((1, -1))\n",
    "    self.u = bm.Variable(u.reshape((batch_size, -1)))\n",
    "    self.x = bm.Variable(bm.ones((batch_size, self.num_hidden)))\n",
    "    self.y = bm.Variable(bm.ones((batch_size, self.num_output)))\n",
    "    self.y_hist = bm.Variable(bm.zeros((task.num_steps, batch_size, task.num_output)))\n",
    "\n",
    "    # Loss\n",
    "    self.loss = bm.Variable(bm.zeros(1))\n",
    "    self.perf_loss = bm.Variable(bm.zeros(1))\n",
    "    self.spike_loss = bm.Variable(bm.zeros(1))\n",
    "    self.weight_loss = bm.Variable(bm.zeros(1))\n",
    "\n",
    "  def reset_state(self, batch_size):\n",
    "    u = bm.ones((batch_size, 2, int(self.num_hidden / 2))) * 0.3\n",
    "    u[:, 0] = 0.15\n",
    "    u[:, 1] = 0.45\n",
    "    self.u.value = u.reshape((batch_size, -1))\n",
    "    self.x.value = bm.ones((batch_size, self.num_hidden))\n",
    "    self.loss[:] = 0.\n",
    "    self.perf_loss[:] = 0.\n",
    "    self.spike_loss[:] = 0.\n",
    "    self.weight_loss[:] = 0.\n",
    "\n",
    "  def update(self, input):\n",
    "    # update STP variables\n",
    "    self.x += (self.alpha_std * (1 - self.x) - dt_sec * self.u * self.x * self.h)\n",
    "    self.u += (self.alpha_stf * (self.U - self.u) + dt_sec * self.U * (1 - self.u) * self.h)\n",
    "    self.x.value = bm.minimum(1., bm.relu(self.x))\n",
    "    self.u.value = bm.minimum(1., bm.relu(self.u))\n",
    "    h_post = self.u * self.x * self.h\n",
    "\n",
    "    # Update the hidden state. Only use excitatory projections from input layer to RNN\n",
    "    # All input and RNN activity will be non-negative\n",
    "    state = alpha * (input @ bm.relu(self.w_ir) + h_post @ self.w_rr + self.b_rr)\n",
    "    state += bm.random.normal(0, self.noise_rnn, self.h.shape)\n",
    "    self.h.value = bm.relu(state) + self.h * (1 - alpha)\n",
    "    self.y.value = self.h @ bm.relu(self.w_ro) + self.b_ro\n",
    "\n",
    "  def predict(self, inputs):\n",
    "    self.h[:] = self.init_h\n",
    "    scan = bm.make_loop(body_fun=self.update,\n",
    "                        dyn_vars=[self.x, self.u, self.h, self.y],\n",
    "                        out_vars=[self.y, self.h])\n",
    "    logits, hist_h = scan(inputs)\n",
    "    self.y_hist[:] = logits\n",
    "    return logits, hist_h\n",
    "\n",
    "  def loss_func(self, inputs, targets, mask):\n",
    "    logits, hist_h = self.predict(inputs)\n",
    "\n",
    "    # Calculate the performance loss\n",
    "    perf_loss = bp.losses.cross_entropy_loss(logits, targets, reduction='none') * mask\n",
    "    self.perf_loss[:] = bm.mean(perf_loss)\n",
    "\n",
    "    # L1/L2 penalty term on hidden state activity to encourage low spike rate solutions\n",
    "    n = 2 if spike_regularization == 'L2' else 1\n",
    "    self.spike_loss[:] = bm.mean(hist_h ** n)\n",
    "    self.weight_loss[:] = bm.mean(bm.relu(self.w_rr) ** n)\n",
    "\n",
    "    # final loss\n",
    "    self.loss[:] = self.perf_loss + spike_cost * self.spike_loss + weight_cost * self.weight_loss\n",
    "    return self.loss.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe0b66b0",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ef13c67a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_perf(target, output, mask):\n",
    "  \"\"\"Calculate task accuracy by comparing the actual network output to the desired output\n",
    "    only examine time points when test stimulus is on, e.g. when y[:,:,0] = 0 \"\"\"\n",
    "  target = target.numpy()\n",
    "  output = output.numpy()\n",
    "  mask = mask.numpy()\n",
    "\n",
    "  mask_full = mask > 0\n",
    "  mask_test = mask_full * (target[:, :, 0] == 0)\n",
    "  mask_non_match = mask_full * (target[:, :, 1] == 1)\n",
    "  mask_match = mask_full * (target[:, :, 2] == 1)\n",
    "  target_max = np.argmax(target, axis=2)\n",
    "  output_max = np.argmax(output, axis=2)\n",
    "\n",
    "  match = target_max == output_max\n",
    "  accuracy = np.sum(match * mask_test) / np.sum(mask_test)\n",
    "  acc_non_match = np.sum(match * np.squeeze(mask_non_match)) / np.sum(mask_non_match)\n",
    "  acc_match = np.sum(match * np.squeeze(mask_match)) / np.sum(mask_match)\n",
    "  return accuracy, acc_non_match, acc_match"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47dd6685",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c428bc37",
   "metadata": {},
   "outputs": [],
   "source": [
    "def trial(task_name, save_fn=None, num_iterations=2000, iter_between_outputs=5):\n",
    "  task = Task(task_name, dt=dt, tau=time_constant, batch_size=batch_size)\n",
    "  # trial_info = task.generate_trial(set_rule=None)\n",
    "  # task.plot_neural_input(trial_info)\n",
    "\n",
    "  model = Model(task)\n",
    "  opt = bp.optim.Adam(learning_rate, train_vars=model.train_vars())\n",
    "  grad_f = bm.grad(model.loss_func, \n",
    "                   grad_vars=model.train_vars(),\n",
    "                   return_value=True)\n",
    "\n",
    "  @bm.jit\n",
    "  def train_op(x, y, mask):\n",
    "    grads, _ = grad_f(x, y, mask)\n",
    "    capped_gs = dict()\n",
    "    for key, grad in grads.items():\n",
    "      if 'w_rr' in key: grad *= model.w_rr_mask\n",
    "      elif 'w_ro' in key: grad *= model.w_ro_mask\n",
    "      elif 'w_ri' in key: grad *= model.w_ir_mask\n",
    "      capped_gs[key] = bm.clip_by_norm(grad, clip_max_grad_val)\n",
    "    opt.update(grads=capped_gs)\n",
    "\n",
    "  # keep track of the model performance across training\n",
    "  model_performance = {'accuracy': [], 'loss': [], 'perf_loss': [],\n",
    "                       'spike_loss': [], 'weight_loss': [], 'iteration': []}\n",
    "\n",
    "  for i in range(num_iterations):\n",
    "    model.reset_state(batch_size)\n",
    "    # generate batch of batch_train_size\n",
    "    trial_info = task.generate_trial(set_rule=None)\n",
    "    inputs = bm.array(trial_info['neural_input'], dtype=bm.float32)\n",
    "    targets = bm.array(trial_info['desired_output'], dtype=bm.float32)\n",
    "    mask = bm.array(trial_info['train_mask'], dtype=bm.float32)\n",
    "\n",
    "    # Run the model\n",
    "    train_op(inputs, targets, mask)\n",
    "\n",
    "    # get metrics\n",
    "    accuracy, _, _ = get_perf(targets, model.y_hist, mask)\n",
    "    model_performance['accuracy'].append(accuracy)\n",
    "    model_performance['loss'].append(model.loss)\n",
    "    model_performance['perf_loss'].append(model.perf_loss)\n",
    "    model_performance['spike_loss'].append(model.spike_loss)\n",
    "    model_performance['weight_loss'].append(model.weight_loss)\n",
    "    model_performance['iteration'].append(i)\n",
    "\n",
    "    # Save the network model and output model performance to screen\n",
    "    if i % iter_between_outputs == 0:\n",
    "      print(task_name +\n",
    "            f' Iter {i:4d}' +\n",
    "            f' | Accuracy {accuracy:0.4f}' +\n",
    "            f' | Perf loss {model.perf_loss[0]:0.4f}' +\n",
    "            f' | Spike loss {model.spike_loss[0]:0.4f}' +\n",
    "            f' | Weight loss {model.weight_loss[0]:0.4f}' +\n",
    "            f' | Mean activity {bm.mean(model.h):0.4f}')\n",
    "\n",
    "  if save_fn:\n",
    "    if not os.path.exists(os.path.dirname(save_fn)):\n",
    "      os.makedirs(os.path.dirname(save_fn))\n",
    "\n",
    "    # Save model and results\n",
    "    weights = model.train_vars().unique().dict()\n",
    "    results = {'weights': weights, 'parameters': {}}\n",
    "    for k, v in model_performance.items():\n",
    "      results[k] = v\n",
    "    pickle.dump(results, open(save_fn, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "95b3e0fd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DMS Iter    0 | Accuracy 0.5211 | Perf loss 2.7038 | Spike loss 7.8653 | Weight loss 0.0315 | Mean activity 3.3380\n",
      "DMS Iter    5 | Accuracy 0.4184 | Perf loss 0.4879 | Spike loss 5.5533 | Weight loss 0.0315 | Mean activity 1.6523\n",
      "DMS Iter   10 | Accuracy 0.4473 | Perf loss 0.2892 | Spike loss 4.3892 | Weight loss 0.0312 | Mean activity 1.2179\n",
      "DMS Iter   15 | Accuracy 0.4654 | Perf loss 0.2386 | Spike loss 2.8176 | Weight loss 0.0308 | Mean activity 1.0014\n",
      "DMS Iter   20 | Accuracy 0.5000 | Perf loss 0.2102 | Spike loss 2.4600 | Weight loss 0.0309 | Mean activity 0.8940\n",
      "DMS Iter   25 | Accuracy 0.4783 | Perf loss 0.2018 | Spike loss 2.2587 | Weight loss 0.0311 | Mean activity 0.8501\n",
      "DMS Iter   30 | Accuracy 0.5119 | Perf loss 0.1815 | Spike loss 2.0880 | Weight loss 0.0313 | Mean activity 0.7721\n",
      "DMS Iter   35 | Accuracy 0.5340 | Perf loss 0.1697 | Spike loss 1.7911 | Weight loss 0.0318 | Mean activity 0.7324\n",
      "DMS Iter   40 | Accuracy 0.5469 | Perf loss 0.1640 | Spike loss 1.7178 | Weight loss 0.0324 | Mean activity 0.6913\n",
      "DMS Iter   45 | Accuracy 0.5764 | Perf loss 0.1524 | Spike loss 1.6305 | Weight loss 0.0332 | Mean activity 0.6684\n",
      "DMS Iter   50 | Accuracy 0.6229 | Perf loss 0.1436 | Spike loss 1.5921 | Weight loss 0.0339 | Mean activity 0.6497\n",
      "DMS Iter   55 | Accuracy 0.6301 | Perf loss 0.1382 | Spike loss 1.5635 | Weight loss 0.0346 | Mean activity 0.6360\n",
      "DMS Iter   60 | Accuracy 0.6254 | Perf loss 0.1392 | Spike loss 1.4905 | Weight loss 0.0353 | Mean activity 0.6193\n",
      "DMS Iter   65 | Accuracy 0.6494 | Perf loss 0.1325 | Spike loss 1.4990 | Weight loss 0.0360 | Mean activity 0.6177\n",
      "DMS Iter   70 | Accuracy 0.6910 | Perf loss 0.1232 | Spike loss 1.4614 | Weight loss 0.0365 | Mean activity 0.6192\n",
      "DMS Iter   75 | Accuracy 0.6977 | Perf loss 0.1206 | Spike loss 1.4657 | Weight loss 0.0372 | Mean activity 0.6092\n",
      "DMS Iter   80 | Accuracy 0.7135 | Perf loss 0.1178 | Spike loss 1.4238 | Weight loss 0.0377 | Mean activity 0.6133\n",
      "DMS Iter   85 | Accuracy 0.7240 | Perf loss 0.1133 | Spike loss 1.4442 | Weight loss 0.0383 | Mean activity 0.6201\n",
      "DMS Iter   90 | Accuracy 0.7273 | Perf loss 0.1093 | Spike loss 1.4717 | Weight loss 0.0390 | Mean activity 0.6170\n",
      "DMS Iter   95 | Accuracy 0.7404 | Perf loss 0.1055 | Spike loss 1.4073 | Weight loss 0.0395 | Mean activity 0.6214\n",
      "DMS Iter  100 | Accuracy 0.7670 | Perf loss 0.0974 | Spike loss 1.4685 | Weight loss 0.0403 | Mean activity 0.6321\n",
      "DMS Iter  105 | Accuracy 0.7639 | Perf loss 0.0996 | Spike loss 1.4525 | Weight loss 0.0410 | Mean activity 0.6321\n",
      "DMS Iter  110 | Accuracy 0.7947 | Perf loss 0.0879 | Spike loss 1.4711 | Weight loss 0.0416 | Mean activity 0.6412\n",
      "DMS Iter  115 | Accuracy 0.7881 | Perf loss 0.0893 | Spike loss 1.4721 | Weight loss 0.0426 | Mean activity 0.6400\n",
      "DMS Iter  120 | Accuracy 0.7902 | Perf loss 0.0853 | Spike loss 1.4998 | Weight loss 0.0433 | Mean activity 0.6495\n",
      "DMS Iter  125 | Accuracy 0.8094 | Perf loss 0.0775 | Spike loss 1.4819 | Weight loss 0.0440 | Mean activity 0.6519\n",
      "DMS Iter  130 | Accuracy 0.8127 | Perf loss 0.0780 | Spike loss 1.4739 | Weight loss 0.0446 | Mean activity 0.6535\n",
      "DMS Iter  135 | Accuracy 0.8295 | Perf loss 0.0725 | Spike loss 1.4526 | Weight loss 0.0452 | Mean activity 0.6481\n",
      "DMS Iter  140 | Accuracy 0.8303 | Perf loss 0.0702 | Spike loss 1.4571 | Weight loss 0.0458 | Mean activity 0.6508\n",
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      "DMS Iter 1790 | Accuracy 0.9719 | Perf loss 0.0138 | Spike loss 0.8086 | Weight loss 0.1273 | Mean activity 0.1952\n",
      "DMS Iter 1795 | Accuracy 0.9672 | Perf loss 0.0151 | Spike loss 0.8010 | Weight loss 0.1276 | Mean activity 0.1906\n",
      "DMS Iter 1800 | Accuracy 0.9680 | Perf loss 0.0148 | Spike loss 0.7924 | Weight loss 0.1277 | Mean activity 0.1936\n",
      "DMS Iter 1805 | Accuracy 0.9699 | Perf loss 0.0137 | Spike loss 0.7918 | Weight loss 0.1275 | Mean activity 0.1901\n",
      "DMS Iter 1810 | Accuracy 0.9672 | Perf loss 0.0152 | Spike loss 0.7841 | Weight loss 0.1272 | Mean activity 0.1907\n",
      "DMS Iter 1815 | Accuracy 0.9684 | Perf loss 0.0147 | Spike loss 0.7764 | Weight loss 0.1266 | Mean activity 0.1949\n",
      "DMS Iter 1820 | Accuracy 0.9641 | Perf loss 0.0170 | Spike loss 0.7545 | Weight loss 0.1262 | Mean activity 0.1955\n",
      "DMS Iter 1825 | Accuracy 0.9654 | Perf loss 0.0175 | Spike loss 0.7483 | Weight loss 0.1261 | Mean activity 0.1876\n",
      "DMS Iter 1830 | Accuracy 0.9680 | Perf loss 0.0169 | Spike loss 0.7479 | Weight loss 0.1263 | Mean activity 0.1868\n",
      "DMS Iter 1835 | Accuracy 0.9631 | Perf loss 0.0177 | Spike loss 0.7709 | Weight loss 0.1277 | Mean activity 0.1898\n",
      "DMS Iter 1840 | Accuracy 0.9662 | Perf loss 0.0155 | Spike loss 0.8514 | Weight loss 0.1294 | Mean activity 0.1925\n",
      "DMS Iter 1845 | Accuracy 0.9658 | Perf loss 0.0191 | Spike loss 0.8877 | Weight loss 0.1302 | Mean activity 0.1952\n",
      "DMS Iter 1850 | Accuracy 0.9572 | Perf loss 0.0198 | Spike loss 0.8889 | Weight loss 0.1305 | Mean activity 0.1948\n",
      "DMS Iter 1855 | Accuracy 0.9645 | Perf loss 0.0170 | Spike loss 0.8962 | Weight loss 0.1304 | Mean activity 0.1962\n",
      "DMS Iter 1860 | Accuracy 0.9641 | Perf loss 0.0163 | Spike loss 0.9061 | Weight loss 0.1308 | Mean activity 0.1955\n",
      "DMS Iter 1865 | Accuracy 0.9652 | Perf loss 0.0161 | Spike loss 0.9028 | Weight loss 0.1311 | Mean activity 0.1890\n",
      "DMS Iter 1870 | Accuracy 0.9613 | Perf loss 0.0167 | Spike loss 0.8960 | Weight loss 0.1311 | Mean activity 0.1936\n",
      "DMS Iter 1875 | Accuracy 0.9631 | Perf loss 0.0168 | Spike loss 0.8865 | Weight loss 0.1312 | Mean activity 0.1906\n",
      "DMS Iter 1880 | Accuracy 0.9688 | Perf loss 0.0154 | Spike loss 0.8781 | Weight loss 0.1314 | Mean activity 0.1909\n",
      "DMS Iter 1885 | Accuracy 0.9648 | Perf loss 0.0166 | Spike loss 0.8425 | Weight loss 0.1312 | Mean activity 0.1904\n",
      "DMS Iter 1890 | Accuracy 0.9715 | Perf loss 0.0149 | Spike loss 0.8410 | Weight loss 0.1314 | Mean activity 0.1846\n",
      "DMS Iter 1895 | Accuracy 0.9643 | Perf loss 0.0153 | Spike loss 0.8335 | Weight loss 0.1312 | Mean activity 0.1834\n",
      "DMS Iter 1900 | Accuracy 0.9684 | Perf loss 0.0146 | Spike loss 0.8169 | Weight loss 0.1310 | Mean activity 0.1786\n",
      "DMS Iter 1905 | Accuracy 0.9646 | Perf loss 0.0166 | Spike loss 0.8116 | Weight loss 0.1310 | Mean activity 0.1794\n",
      "DMS Iter 1910 | Accuracy 0.9715 | Perf loss 0.0149 | Spike loss 0.8409 | Weight loss 0.1313 | Mean activity 0.1780\n",
      "DMS Iter 1915 | Accuracy 0.9611 | Perf loss 0.0183 | Spike loss 0.8462 | Weight loss 0.1313 | Mean activity 0.1881\n",
      "DMS Iter 1920 | Accuracy 0.9627 | Perf loss 0.0215 | Spike loss 0.8177 | Weight loss 0.1311 | Mean activity 0.1832\n",
      "DMS Iter 1925 | Accuracy 0.9631 | Perf loss 0.0203 | Spike loss 0.8071 | Weight loss 0.1312 | Mean activity 0.1819\n",
      "DMS Iter 1930 | Accuracy 0.9697 | Perf loss 0.0135 | Spike loss 0.7942 | Weight loss 0.1309 | Mean activity 0.1781\n",
      "DMS Iter 1935 | Accuracy 0.9674 | Perf loss 0.0156 | Spike loss 0.7923 | Weight loss 0.1307 | Mean activity 0.1771\n",
      "DMS Iter 1940 | Accuracy 0.9699 | Perf loss 0.0130 | Spike loss 0.7848 | Weight loss 0.1304 | Mean activity 0.1755\n",
      "DMS Iter 1945 | Accuracy 0.9660 | Perf loss 0.0151 | Spike loss 0.7687 | Weight loss 0.1304 | Mean activity 0.1756\n",
      "DMS Iter 1950 | Accuracy 0.9717 | Perf loss 0.0134 | Spike loss 0.7760 | Weight loss 0.1305 | Mean activity 0.1797\n",
      "DMS Iter 1955 | Accuracy 0.9658 | Perf loss 0.0149 | Spike loss 0.7787 | Weight loss 0.1305 | Mean activity 0.1841\n",
      "DMS Iter 1960 | Accuracy 0.9670 | Perf loss 0.0184 | Spike loss 0.7772 | Weight loss 0.1306 | Mean activity 0.1826\n",
      "DMS Iter 1965 | Accuracy 0.9641 | Perf loss 0.0193 | Spike loss 0.7668 | Weight loss 0.1308 | Mean activity 0.1853\n",
      "DMS Iter 1970 | Accuracy 0.9609 | Perf loss 0.0163 | Spike loss 0.7810 | Weight loss 0.1319 | Mean activity 0.1779\n",
      "DMS Iter 1975 | Accuracy 0.9707 | Perf loss 0.0163 | Spike loss 0.7959 | Weight loss 0.1327 | Mean activity 0.1697\n",
      "DMS Iter 1980 | Accuracy 0.9623 | Perf loss 0.0163 | Spike loss 0.8059 | Weight loss 0.1326 | Mean activity 0.1691\n",
      "DMS Iter 1985 | Accuracy 0.9680 | Perf loss 0.0154 | Spike loss 0.7976 | Weight loss 0.1324 | Mean activity 0.1717\n",
      "DMS Iter 1990 | Accuracy 0.9697 | Perf loss 0.0140 | Spike loss 0.7889 | Weight loss 0.1322 | Mean activity 0.1747\n",
      "DMS Iter 1995 | Accuracy 0.9635 | Perf loss 0.0159 | Spike loss 0.7953 | Weight loss 0.1324 | Mean activity 0.1737\n"
     ]
    }
   ],
   "source": [
    "trial('DMS')"
   ]
  }
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