An accelerated event-driven spiking neural network simulator for neurmorphic computing and research in Python. Suitable for large-scale simulations. Achieves up to 100 million synaptic transmissions (including STP and R-STDP) per second on a desktop computer. Its core engine is event-driven, parallel and written in Rust.

• LIF (leaky integrate-and-fire) neurons with optionally adaptive threshold
• Axonal conduction delays
• Short term plasticity (STP)
• Reward-modulated spike-timing-dependent plasticity (R-STDP) with a short term (decaying) and long term variant
• Parametrization options: JSON, YAML, Python Dictionary
• Pandas-based user interface
• Linux is currently the only supported OS

Complete in functionality but lacks user documentation. I am currently using it for my own research and experiments. I plan to write documentation at some point, perhaps in form of a Jupyter notebook. If you are interested in trying this tool out, please message me on LinkedIn or create an issue.

Install it:

pip install morphyne

Run example:

import morphyne as mp
import pandas as pd
import matplotlib.pyplot as plt
import json

params = mp.get_example_params()
print(json.dumps(params, indent=4))

instance = mp.create(params)
instance.set_non_coherent_stimulation_rate(0.0005)
instance.set_reward_rate(0.1)

stimulus_df = pd.DataFrame({"t": [0, 5, 10], "in_channel_id": [0, 1, 2]})
stimulus = mp.create_stimulus(in_channel_spikes=stimulus_df)

instance.apply_stimulus(stimulus)
tick_result = instance.tick_until(1000)

plt.scatter(tick_result.neuron_spikes.t, tick_result.neuron_spikes.nid, s=1)
plt.xlabel("t")
plt.ylabel("neuron ID")
plt.show()

Output Spike Raster: