flyverse
Native Rust MaleCNS v1.0 sim embodied in a virtual room, with a browser visualizer. See also flyverse-core (transmitter-aware core).
Neurons are simulated — leaky integrate-and-fire or better — with documented, tuned parameters.
Dispute this rating →The evidence
No evidence, no level.The engine is leaky integrate-and-fire over the MaleCNS graph with every time constant, threshold, reset, refractory period, delay and synaptic weight stated.
https://github.com/djmango/flyverse/blob/59ff4165178a1ebf8a72c55598a715ce16327f46/src/lif.rs#L1-L13The README separates what is real from what is an engineered surrogate, listing spike dynamics and the loaded connectome as real and the body, room and ventral-cord stimulus as surrogates.
https://github.com/djmango/flyverse/blob/59ff4165178a1ebf8a72c55598a715ce16327f46/README.md#L9-L56
The whole MaleCNS v1.0 graph (166,700 neurons, 24,559,135 signed edges, loaded whole with no pruning) runs as leaky integrate-and-fire in Rust with every constant documented at the top of the engine: 0.1 ms timestep, rest and reset -52 mV, threshold -45 mV, membrane tau 20 ms, synaptic tau 5 ms, refractory 2.2 ms, delay 1.8 ms, 0.275 mV per signed contact. Membrane voltages change under a documented neuron model, so L1. Not L2: there is no plasticity of any kind in the source - a grep across the Rust for plasticity, STDP, dopamine, eligibility, learning rate and any weight increment returns nothing, and the synaptic weights are read from the packed connectome and never written. This is one of the more honest repos in the batch: the README opens with a per-layer accounting of what is measured versus what is an engineered surrogate (the ventral-cord sensory drive is canned replay, ground locomotion is a speed cap, the room is invented), and it reports its own negative result - the haltere perturbation probe bounds any steering response at about plus or minus 0.02 and the authors say outright that the haltere-on/off difference 'is not evidence of anything' because the system is chaotic.
Record
Nearby
All projects →Research code for the Shiu et al. connectome-based leaky integrate-and-fire model, including activation/silencing experiments, notebooks, and FlyWire data configuration.
FlyWire whole-brain LIF implementation based on Shiu et al., with multiple simulation backends and benchmarking tools. This repository supplies the neural model; it is not a complete embodied demo package.
Ratings on thelearningfly.com are proposals, not verdicts. Every one of them is arguable in public.