eprop_learning_signal_connection_bsshslm_2020 – Synapse model transmitting feedback learning signals for e-prop plasticity

Description

eprop_learning_signal_connection_bsshslm_2020 is an implementation of a feedback connector from eprop_readout_bsshslm_2020 readout neurons to eprop_iaf_bsshslm_2020 or eprop_iaf_adapt_bsshslm_2020 recurrent neurons that transmits the learning signals \(L_j^t\) for eligibility propagation (e-prop) plasticity and has a static weight \(B_{jk}\).

E-prop plasticity was originally introduced and implemented in TensorFlow in [1].

The suffix _bsshslm_2020 follows the NEST convention to indicate in the model name the paper that introduced it by the first letter of the authors’ last names and the publication year.

For more information on e-prop plasticity, see the documentation on the other e-prop models:

• eprop_iaf_bsshslm_2020

• eprop_iaf_adapt_bsshslm_2020

• eprop_readout_bsshslm_2020

• eprop_synapse_bsshslm_2020

Details on the event-based NEST implementation of e-prop can be found in [2].

Parameters

The following parameters can be set in the status dictionary.

Individual synapse parameters
Parameter	Unit	Math equivalent	Default	Description
delay	ms	\(d_{jk}\)	1.0	Dendritic delay
weight	pA	\(B_{jk}\)	1.0	Synaptic weight

Recordables

The following variables can be recorded. Note that since this connection lacks a plasticity mechanism the weight does not evolve over time.

Synapse recordables
State variable	Unit	Math equivalent	Initial value	Description
weight	pA	\(B_{jk}\)	1.0	Synaptic weight

Usage

This model can only be used in combination with the other e-prop models and the network architecture requires specific wiring, input, and output. The usage is demonstrated in several supervised regression and classification tasks reproducing among others the original proof-of-concept tasks in [1].

Transmits

LearningSignalConnectionEvent

References

[1] (1,2)

Bellec G, Scherr F, Subramoney F, Hajek E, Salaj D, Legenstein R, Maass W (2020). A solution to the learning dilemma for recurrent networks of spiking neurons. Nature Communications, 11:3625. https://doi.org/10.1038/s41467-020-17236-y

[2]

Korcsak-Gorzo A, Stapmanns J, Espinoza Valverde JA, Plesser HE, Dahmen D, Bolten M, Van Albada SJ, Diesmann M. Event-based implementation of eligibility propagation (in preparation)

See also

Examples using this model

• Tutorial on learning to accumulate evidence with e-prop after Bellec et al. (2020)

• Tutorial on learning to generate a lemniscate with e-prop after Bellec et al. (2020)

• Tutorial on learning to generate handwritten text with e-prop after Bellec et al. (2020)

• Tutorial on learning to generate sine waves with e-prop after Bellec et al. (2020)