According to Hebbian theory, synaptic plasticity is the ability of neurons to strengthen or weaken the synapses among them in response to stimuli. It plays a fundamental role in the processes of learning and memory of biological neural networks. With plasticity, biological agents can adapt on multiple timescales and outclass artificial agents, the majority of which still rely on static Artificial Neural Network (ANN) controllers. In this work, we focus on Voxel-based Soft Robots (VSRs), a class of simulated artificial agents, composed as aggregations of elastic cubic blocks. We propose a Hebbian ANN controller where every synapse is associated with a Hebbian rule that controls the way the weight is adapted during the VSR lifetime. For a given task and morphology, we optimize the controller for the task of locomotion by evolving, rather than the weights, the parameters of the Hebbian rules. Our results show that the Hebbian controller is comparable, often better than a non-Hebbian baseline and that it is more adaptable to damages. We also provide novel insights into the inner workings of plasticity and demonstrate that “true” learning does take place, as the evolved controllers improve over the lifetime and generalize well.

Evolving Hebbian Learning Rules in Voxel-based Soft Robots / Ferigo, Andrea; Iacca, Giovanni; Medvet, Eric; Pigozzi, Federico. - In: IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS. - ISSN 2379-8920. - 2022:(2022), pp. 1-11. [10.1109/TCDS.2022.3226556]

Evolving Hebbian Learning Rules in Voxel-based Soft Robots

Ferigo, Andrea;Iacca, Giovanni;
2022-01-01

Abstract

According to Hebbian theory, synaptic plasticity is the ability of neurons to strengthen or weaken the synapses among them in response to stimuli. It plays a fundamental role in the processes of learning and memory of biological neural networks. With plasticity, biological agents can adapt on multiple timescales and outclass artificial agents, the majority of which still rely on static Artificial Neural Network (ANN) controllers. In this work, we focus on Voxel-based Soft Robots (VSRs), a class of simulated artificial agents, composed as aggregations of elastic cubic blocks. We propose a Hebbian ANN controller where every synapse is associated with a Hebbian rule that controls the way the weight is adapted during the VSR lifetime. For a given task and morphology, we optimize the controller for the task of locomotion by evolving, rather than the weights, the parameters of the Hebbian rules. Our results show that the Hebbian controller is comparable, often better than a non-Hebbian baseline and that it is more adaptable to damages. We also provide novel insights into the inner workings of plasticity and demonstrate that “true” learning does take place, as the evolved controllers improve over the lifetime and generalize well.
2022
Ferigo, Andrea; Iacca, Giovanni; Medvet, Eric; Pigozzi, Federico
Evolving Hebbian Learning Rules in Voxel-based Soft Robots / Ferigo, Andrea; Iacca, Giovanni; Medvet, Eric; Pigozzi, Federico. - In: IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS. - ISSN 2379-8920. - 2022:(2022), pp. 1-11. [10.1109/TCDS.2022.3226556]
File in questo prodotto:
File Dimensione Formato  
preprint.pdf

accesso aperto

Tipologia: Pre-print non referato (Non-refereed preprint)
Licenza: Creative commons
Dimensione 5.23 MB
Formato Adobe PDF
5.23 MB Adobe PDF Visualizza/Apri
Evolving_Hebbian_Learning_Rules_in_Voxel-based_Soft_Robots.pdf

Solo gestori archivio

Tipologia: Post-print referato (Refereed author’s manuscript)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 3.47 MB
Formato Adobe PDF
3.47 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/329508
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 0
social impact