What kinds of models can be implemented in Jaxley?

Biophysical neuron models

Jaxley focuses on biophysical, Hodgkin-Huxley-type models, as well as networks thereof. In particular, Jaxley supports:

• single-compartment (point neuron) Hodgkin-Huxley models,

• multi-compartment Hodgkin-Huxley models.

For these models, Jaxley is flexible and accurate. For example, it can flexibly add new channel models, use different kinds of synapses (conductance-based, tanh, …), it can insert different kinds of channels in different branches (or compartments) within single cells, and it can simulate complex ion dynamics (diffusion, pumps,…). You can think of Jaxley like the NEURON simulator written in JAX. Like NEURON, Jaxley implements a backward-Euler solver for stable numerical solution of multi-compartment neurons.

Simplified neuron models

Jaxley can also simulate simplified models, see the tutorial here. In particular, Jaxley supports:

• Leaky-integrate-and-fire (LIF) neurons,

• Izhikevich neurons,

• Rate-based neurons.

Jaxley also supports networks of these neurons.

Note that, for LIF neurons, Jaxley additionally supports surrogate gradient descent as of version 1.0.0, which is required for efficient training due to the discontinuity of spikes in these models. Note that for the Izhikevich model, surrogate gradients are not supported by default, but one can modify the Izhikevich model to include support. See the tutorial linked above.