Hello everybody,
I am trying to model the IO nucleus of the cerebellum as weakly coupled oscillators. With respect to this example I would like to add the gap junctions. Their job is to provide a simple coupling conductance of the type gC (V2 - V1) with gc a fixed number and V2 V1 the potential of the neuron.
Does anybody have any idea of how to implement it?
While nengo-bio supports conductance-based synapses and purely excitatory/inhibitory neuron populations, it unfortunately doesn’t really help with gap junctions (yet).
The closest thing it supports are two-compartment LIF neurons. In those neurons, two compartments (a passive “dendritic” compartment, and an active “somatic” compartment) are coupled via a “gap junction” with an adjustable gC.
Support for more sophisticated channel types is on my list of things to eventually implement, but low-priority right now.
Thank you so much for the help, I will use two-compartment LIF neurons if I manage to code it inside the recurrent IO connections (since gap junctions are between each of the olivary neurons with a sparsity percentage). In case I do not succeed, are the conduc. based synapses in nengo-bio compatible with the simulators in nengo and nengo dl?
@astoecke can correct me if I’m wrong, but I do not believe that they will work in the nengo-dl simulator. The network may run, but it won’t be differentiable/trainable, as that requires the nengo objects to be defined in a native tensorflow implementation. For example, if a synapse is a LinearSynapse tensorflow subclass, then it will work in the nengo-dl sim, and be trainable.
Pawel is correct, the conductance-based synapses in nengo-bio, as well as the two-compartment LIF neurons are not compatible with nengo-dl; nengo-bio does use the default nengo simulator though.
The fundamental problem is that nengo has no support for neurons with multiple input channels (excitatory, inhibitory) and nengo-bio jumps through quite a few hoops to hack that into the nengo-internal operator graph. Also, as Pawel points out, the gradient for the nengo-bio objects is not exposed to nengo-dl.