Biologically plausible learning in artificial neural networks


Mattia Della Vecchia | Ecole Normale Supérieure, France
Leonardo Agueci | Ecole Normale Supérieure, France


Artificial neural networks have been a crucial tool in neuroscience to understand brain functions, and major advances in artificial intelligence have led to methods that excel in a wide range of specific tasks, sometimes outperforming animal capabilities. Yet these systems do not show characteristic elements of animal intelligence, such as adaptability to new situations, transfer of knowledge across different tasks, or generalization from limited observations. On the other hand, our understanding of how the brain acquires these mechanisms is still limited, and this makes unclear the direction that should be taken moving forward. Some researchers argue that it is important to enrich artificial neural networks with biological components to study the underlying processes in the brain, while others are skeptical, highlighting the differences between the two. The debate revolves around questions like what levels of biological abstraction are possible, why we should take biological plausibility in consideration, or which biological elements are the most important to include.

The goal of the workshop is to bring together experts that work on expanding our comprehension of neural computations and learning mechanisms, in order to foster a discussion on how biological constraints in computational models can provide neuroscientists with insights into brain functions, and how these insights could influence future developments of artificial neural networks. The workshop will be divided into two mini-sessions, each one followed by a panel discussion, which themes will be: 

  • neural circuits models and plasticity mechanisms
  • prediction error and prediction error-related plasticity
  • biological plausible components in neural network modeling

Schedule (CEST)

Tuesday, Sept 26


Katharina A. Wilmes | University of Bern, Switzerland
Uncertainty-modulated prediction errors in cortical microcircuits


Naoki Hiratani | Harvard University, USA
Exploring simplicity bias and scalability of actor-critic learning in shallow neural network


Guillaume Bellec | Swiss Federal Institute of Technology in Lausanne, Switzerland
Hypothesis: Change prediction error gates plasticity in sensory cortices


Q&A – Wrap-up


30 min coffee break


Dhruva Raman | University of Sussex, UK 
Neural circuit architectures for learning in unpredictably changing environments: lessons from the mushroom body


Claudia Clopath | Imperial College London, UK
Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity


Round table discussion I


End of first day

Wednesday, Sept 27


Welcome back


Jonathan Cornford | McGill University, Canada
Learning using brain-inspired geometries


Cristina Savin | New York University, USA
Normative models of synaptic plasticity


Joao Sacramento | ETH Zurich, Switzerland
Online learning of long-range dependencies


30 min coffee break


Julijana Gjorgjieva | Technical University of Munich, Germany
Stable and flexible learning with biologically plausible plasticity rules


Rui Ponte Costa | Oxford University, UK
AI-driven brain-wide credit assignment


Round table discussion II