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:
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:
synaptic weight
weight
Usage¶
This model can only be used in combination with the other e-prop models, whereby 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