Simulator engine

The Axon SDK simulates Spiking Neural Networks (SNNs) built with the STICK framework. This document describes the simulation engine’s architecture, parameters, workflow, and features.

Purpose

The Simulator class provides a discrete-time, event-driven environment to simulate:

Spiking neuron dynamics
Synaptic event propagation
Interval-based input encoding and output decoding
Internal logging of voltages and spikes

It is optimized for symbolic, low-rate temporal computation rather than high-frequency biological modeling.

from axon_sdk.simulator import Simulator

Core Components

The Simulator wraps a SNN, defined as an SpikingNetworkModule, inputs spikes to it and simulates its dynamics.

class Simulator:
    def __init__(self, net: SpikingNetworkModule, encoder: DataEncoder, dt: float = 0.001):
      ...

These are the components needed to instantiate a new simulator:

Component	Description
`net`	The user-defined spiking network (a `SpikingNetworkModule`)
`encoder`	Object for encoding/decoding interval-coded values
`dt`	Simulation timestep in seconds (default: `0.001`)

Calling .simulate(simulation_time) executes the simulation.

Note: It’s the user’s responsability to set an appropriate simulation_time that allows the SNN to finalize its dynamic evolution.

Simulation logs

The simulator logs relevant information during the simulation, which can be retrieved once it’s finished:

Property	Description
`.spike_log`	Dictionary mapping neurons to the timing of their emitted spikes
`.voltage_log`	Dictionary mapping neurons to the evolution of their membrane potentials

Input injection

The Simulator is responsible for inputting values to the SNN:

Property	Description
`.apply_input_value(value, neuron, t0)`	Encodes a value and inputs their corresponding spikes
`.apply_input_spike(neuron, t)`	Inputs an individual spike

Visualization

Axon provides visualization tools for debugging purposes:

Network topology visualization
Chronogram

These visualizations are shown once the simulation is done.

To enable the visualization tools, the environment variable VIS must be set

Environment Variable	Description
`VIS=1`	Opens visualization after simulation

Usage example:

VIS=1 python mul_network.py

Multiplier network topology

Multiplier chronogram

Example usage

from axon_sdk.simulator import Simulator
from axon_sdk.networks import MultiplierNetwork
from axon_sdk.primitives import DataEncoder

encoder = DataEncoder()
net = MultiplierNetwork(encoder)
sim = Simulator(net, encoder, dt=0.001)

a, b = 0.4, 0.25
sim.apply_input_value(a, net.input1, t0=0)
sim.apply_input_value(b, net.input2, t0=0)
sim.simulate(simulation_time=400)

spikes = sim.spike_log.get(net.output.uid, [])
interval = spikes[1] - spikes[0]
decoded_val = encoder.decode_interval(interval)

decoded_val
>> 0.1

Summary

Event-driven, millisecond-resolution simulator
Supports interval-coded STICK networks
Accurate logging of all internal neuron dynamics
Integrates seamlessly with compiler/runtime interfaces

Axon SDK Documentation