29 Jupyter Notebooks
From first spike to proven silicon. Progressive difficulty levels. Available with source access.
Quickstart
01
HDC Symbolic Query
02
Fault-Tolerant Logic
03
End-to-End Pipeline
Complete SC inference: bitstream encode → AND-gate synapses → popcount → LIF neuron → multi-neuron layer
04
Neuron Explorer
Interactive exploration of 117+ models: voltage traces, spike rasters, phase portraits, F-I curves
Intermediate
05
NIR Bridge
06
Network Engine
07
Identity Substrate
08
Equation to Verilog
09
Topology & Dynamics
10
Spike Train Analysis
Advanced
11
Biological Circuits
12
Learning Rules
13
Quantisation Pipeline
14
SC Arithmetic Theory
15
Fault Tolerance
16
Neuron Atlas
17
Reservoir Computing
18
Mixed Precision SC
19
Compression & Pruning
20
Power Analysis
Specialist
21
Spike ALU (Turing-Complete)
22
IR Type Safety
23
Topological Observables
24
Identity Lazarus
25
Cortical Column Dynamics
26
Spike Codec Benchmark
27
Python to Proven Silicon
Complete: ODE string → Python sim → IR type check → Q8.8 Verilog → formal verification → resource estimate
28
Domain Bridge (Quantum + Probabilistic)
quickstart_colab.ipynb — run in Google Colab, zero install
28 Python Examples
01_basic_neuron.py
02_parameter_sweep.py
03_network_simulation.py
04_stochastic_computing.py
05_plasticity_rules.py
06_analysis_pipeline.py
07_compiler_demo.py
08_hardware_synthesis.py
09_chip_emulators.py
10_compression.py
11_nir_bridge.py
12_training_loop.py
ann_to_snn_demo.py
codec_benchmark.py
jax_training_demo.py
mnist_conv_train.py
mnist_fpga_demo.py
nir_roundtrip_demo.py
train_mnist.py