Fun with conductance based synapses in Nengo

The nengo_conductance_synapses package provides a fairly generic function transform which transforms a Nengo model into a Nengo model in which all LIF ensembles are converted to LIF ensembles with inhibitory and excitatory conductance based synapses.

How to use

Create and populate a Nengo Network instance which you want to transform. Then simply call

import nengo_conductance_synapses as conductance_synapses
net_out = conductance_synapses.transform(net_in, dt)

where net_in is the network you want to transform, net_out is the target network, and dt is the timstep used for the internal neuron simulation. When simulating the network, exactly the same timestep as specified here must be passed to the simulator. A recommended value is dt = 1e-4.

Other options that can be passed to the transform function include

• e_rev_E Excitatory synapse reversal potential (default: 4.33, eqiv. 0mV)
• e_rev_I Inhibitory synapse reversal potential (default: -0.33, equiv. -70mV)
• use_linear_avg_pot Use a simplified linear approximation to the average membrane potential (default: False)
• use_conductance_synapses If set to false, uses normal current based synapses. Network transformation should not change the result (if use_factorised_weights and use_jbias are set to False as well). This is useful for testing. (default: True)
• use_factorised_weights Factorises the internal weight matrix in order to speed up the simulation. (default: False)
• use_jbias If false, decodes the bias current from the pre-population of each ensemble, except for those which receive input from nodes only. (default: False)
• seed Random seed to be used for the transformation.

Note that all membrane potentials are normalised to a range from 0 to 1, where 0 is the resting and reset potential and 1 is the threshold potential.

Preventing transformation

You can manually set the attribute use_conductance_synapses to False on an ensemble in order to suppress the conversion of this particular LIF ensemble to an LIF ensemble with conductance based synapses.

with nengo.Network() as net_in:
	ens = nengo.Ensemble(N, D)
	set_attr(ens, 'use_conductance_synapses', False)

net_out = conductance_synapses.transform(net_in, dt)

Unsupported Nengo features

The following Nengo features are currently not supported by the script

• Semantic Pointer Architecture networks (SPA). Theoretically this /should/ work, but for now there are some mysterious crashes.
• Modulatory Learning Rule connections. This will be quite hard to implement since the script operates on weight matrices and does not preserve the individual decoding vectors which are e.g. modified by the PES rule.