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:
