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Training Monitor

The Training Monitor provides live SNN training from the Studio web IDE. Configure network architecture, surrogate gradients, and training hyperparameters, then watch loss curves, accuracy, and per-layer spike rates update in real time via Server-Sent Events.

Quick Start

  1. Switch to the Train tab
  2. Select dataset (Synthetic for fast demo, MNIST for real training)
  3. Choose surrogate gradient function
  4. Set epochs, batch size, learning rate, timesteps
  5. Optionally enable learnable beta and threshold
  6. Click Train — charts update live as epochs complete
  7. Click Stop to abort early

Features

Live Metric Streaming

Training metrics stream from backend to frontend via SSE (Server-Sent Events). Each epoch emits:

  • train_loss, val_loss — spike count cross-entropy
  • train_accuracy, val_accuracy — classification accuracy
  • layer_spike_rates — mean firing rate per spiking layer
  • param_snapshot — current beta and threshold values (if learnable)

Configurable Architecture

Parameter Default Range Description
Dataset synthetic synthetic, mnist Input data source
Epochs 10 1–100 Training epochs
Batch Size 64 8–512 Mini-batch size
Learning Rate 0.001 0.0001–0.1 Adam optimizer LR
Timesteps 25 5–100 SNN temporal unrolling steps
Hidden [128] — Hidden layer sizes
Surrogate atan 6 options Surrogate gradient function
Learn Beta off on/off Learnable membrane leak
Learn Threshold off on/off Learnable spike threshold

Surrogate Gradient Functions

Six surrogate gradient approximations for the non-differentiable Heaviside spike function:

Function Reference Formula (backward)
atan_surrogate Fang et al. 2021 alpha / (2(1 + (pialphax/2)^2))
fast_sigmoid Zenke & Vogels 2021 slope / (1 + slope*
superspike Zenke & Ganguli 2018 1 / (1 + beta*
sigmoid_surrogate standard sigmoid'(slope*x)
straight_through Bengio et al. 2013 identity
triangular Esser et al. 2016 max(0, 1 -

Neuron Cell Types

The training backend uses sc-neurocore's 11 PyTorch-compatible spiking neuron models:

  • LIFCell — leaky integrate-and-fire (default)
  • IFCell — integrate-and-fire (no leak)
  • ALIFCell — adaptive LIF (Bellec et al. 2020)
  • ExpIFCell — exponential IF (Fourcaud-Trocme et al. 2003)
  • AdExCell — adaptive exponential IF (Brette & Gerstner 2005)
  • LapicqueCell — classical Lapicque neuron
  • AlphaCell — alpha synaptic conductance
  • SecondOrderLIFCell — LIF with second-order dynamics
  • RecurrentLIFCell — LIF with within-layer recurrence
  • ConvSpikingNet — convolutional SNN (2D spatiotemporal)
  • SpikingNet — multi-layer feedforward SNN

Visualisations

The monitor displays four live panels:

  1. Loss Curve — train loss (blue) and val loss (red) per epoch
  2. Accuracy Curve — train accuracy (green) and val accuracy (purple)
  3. Layer Spike Rates — horizontal bar per spiking layer, showing mean firing rate as percentage
  4. Parameter Evolution — current values of learnable beta and threshold parameters

Job Lifecycle

Training runs in a background thread. The lifecycle is:

idle → starting → running → completed | stopped | failed

Multiple training jobs can run concurrently. Each job has a unique ID used for status queries and SSE stream subscription.

API Endpoints

Method Endpoint Description
GET /api/training/surrogates List available surrogate functions
GET /api/training/cell-types List available neuron cell types
POST /api/training/start Start a training job
POST /api/training/stop Stop a running job
GET /api/training/status/{job_id} Query job status
GET /api/training/stream/{job_id} SSE metric stream
GET /api/training/jobs List all jobs

POST /api/training/start

{
  "dataset": "synthetic",
  "epochs": 10,
  "batch_size": 64,
  "lr": 0.001,
  "hidden": [128],
  "timesteps": 25,
  "surrogate": "atan_surrogate",
  "learn_beta": false,
  "learn_threshold": false
}

Returns:

{"job_id": "job_1711504200000", "status": "running"}

GET /api/training/{job_id}/stream

Server-Sent Events stream. Each message is a JSON object:

data: {"event": "config", "data": {"job_id": "...", "device": "cuda", ...}}
data: {"event": "batch", "data": {"epoch": 0, "batch": 10, "loss": 2.31, "accuracy": 0.12}}
data: {"event": "epoch", "data": {"epoch": 0, "train_loss": 2.28, "val_loss": 2.30, "train_accuracy": 0.15, "val_accuracy": 0.13, "layer_spike_rates": {"lifs.0": 0.08}, "param_snapshot": {}}}
data: {"event": "completed", "data": {"train_loss": 1.85, "val_accuracy": 0.42}}

Event types: config, batch, epoch, completed, stopped, error, heartbeat.

Requirements

Training requires PyTorch:

pip install sc-neurocore[research]

For MNIST, torchvision is also needed. If unavailable, synthetic data is used as fallback.