Speaker
Description
Recent advances in neural plasticity research have broadened the foundational Hebbian concept by integrating additional modulating factors. Among these, eligibility propagation (e-prop) stands out as a novel approach, initially devised as an online approximation to backpropagation through time (BPTT) [1]. In this study, we present a series of novel strategies that introduce additional bio-inspired features to e-prop. Our modifications not only contribute to the realism with which e-prop mimics biological processes but also facilitate its implementation in large-scale spiking neural network simulations, thereby establishing its significance for computational neuroscience.
Our study demonstrates that the learning performance achieved with the modified e-prop method is on par with the original e-prop approach. We highlight the seamless integration of e-prop into NEST's event-driven framework for synapses, contrasting it with the original time-driven implementation. This adaptation expands e-prop's applicability for studying learning processes across biological and artificial neural networks, suggesting a broader utility in the field.
We delineate our methodological adaptations and their scalability for large-scale network simulations. Through strong- and weak-scaling analyses, we demonstrate how e-prop in NEST scales effectively for larger networks.
References
[1] Bellec, G., Scherr, F., Subramoney, A., Hajek, E., Salaj, D., Legenstein, R., & Maass, W. (2020). A solution to the learning dilemma for recurrent networks of spiking neurons. Nature communications, 11(1), 3625.
| Preferred form of presentation | Poster & advertising flash talk |
|---|---|
| Topic area | Models and applications |
| Keywords | event-based simulation, voltage-based plasticity rules, spiking neural network simulator, eligibility propagation, scalability |
| Speaker time zone | UTC+2 |
| I agree to the copyright and license terms | Yes |
| I agree to the declaration of honor | Yes |
