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MetaLearnNCA: Few-Shot Offline Meta-Learning via Interacting Neural Cellular Automata
Etienne Guichard, Stefano Nichele
Few-shot meta-learning traditionally formulates task adaptation either as analytical gradient descent through unrolled computational graphs or as metric-based distance comparisons…
Benchmarking the Connectomes of Caenorhabditis elegans within the Reservoir Computing Framework
Felix S. Reimers, Ola Huse Ramstad, Aliaksandr Hubin +1
The aim of this work is to examine the connectomes of Caenorhabditis elegans through a computational lens using the reservoir computing framework. Connectomes are mappings of biolo…
Neural Cellular Automata Learn General Features in their Hidden Channels
Etienne Guichard, Stefano Nichele
Modern deep learning models achieve impressive generalization through over-parameterization, but this paradigm often struggles with overfitting and memorization in few-shot regimes…
Self-Replicating Neural Cellular Automata: Quantifying Emergent Phenotypic and Genotypic Diversity in an OpenEnded Substrate
Sanyam Jain, Felix Simon Reimers, Stefano Nichele
We study an in-silico substrate in which every pixel of a two-channel cellular-automata grid carries a tiny neural network (an agent) that senses its Moore neighborhood. A cell per…
Exploring napping paradigm for Recurrent Spiking Neural Networks
Andreas Massey, Stefano Nichele, Aliaksandr Hubin
Biological organisms minimize free energy by balancing two competing demands on their internal world model: it must be accurate enough to predict sensory input, yet simple enough t…
Mass Conservation as an Inductive Bias for Self-Organized Criticality in NCA Reservoirs
Tong Zhang, Etienne Guichard, Sidney Pontes-Filho +1
Self-organized criticality (SOC), a dynamical regime associated with maximal information processing, offers a promising foundation for reservoir computing. Recent work has shown th…
Sleep-Based Homeostatic Regularization for Stabilizing Spike-Timing-Dependent Plasticity in Recurrent Spiking Neural Networks
Andreas Massey, Aliaksandr Hubin, Stefano Nichele +1
Spike-timing-dependent plasticity (STDP) provides a biologically-plausible learning mechanism for spiking neural networks (SNNs); however, Hebbian weight updates in architectures w…