HyperNCA: Growing Developmental Networks with Neural Cellular Automata
- URL: http://arxiv.org/abs/2204.11674v1
- Date: Mon, 25 Apr 2022 14:08:50 GMT
- Title: HyperNCA: Growing Developmental Networks with Neural Cellular Automata
- Authors: Elias Najarro, Shyam Sudhakaran, Claire Glanois, Sebastian Risi
- Abstract summary: We propose a new hypernetwork approach to grow artificial neural networks based on neural cellular automata.
Inspired by self-organising systems and information-theoretic approaches to developmental biology, we show that our HyperNCA method can grow neural networks capable of solving common reinforcement learning tasks.
- Score: 10.798252487541694
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In contrast to deep reinforcement learning agents, biological neural networks
are grown through a self-organized developmental process. Here we propose a new
hypernetwork approach to grow artificial neural networks based on neural
cellular automata (NCA). Inspired by self-organising systems and
information-theoretic approaches to developmental biology, we show that our
HyperNCA method can grow neural networks capable of solving common
reinforcement learning tasks. Finally, we explore how the same approach can be
used to build developmental metamorphosis networks capable of transforming
their weights to solve variations of the initial RL task.
Related papers
- Contrastive Learning in Memristor-based Neuromorphic Systems [55.11642177631929]
Spiking neural networks have become an important family of neuron-based models that sidestep many of the key limitations facing modern-day backpropagation-trained deep networks.
In this work, we design and investigate a proof-of-concept instantiation of contrastive-signal-dependent plasticity (CSDP), a neuromorphic form of forward-forward-based, backpropagation-free learning.
arXiv Detail & Related papers (2024-09-17T04:48:45Z) - Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
We propose a biologically-informed framework for enhancing artificial neural networks (ANNs)
Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors.
We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
arXiv Detail & Related papers (2024-07-05T14:11:28Z) - Brain-Inspired Machine Intelligence: A Survey of
Neurobiologically-Plausible Credit Assignment [65.268245109828]
We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology.
We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors.
The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
arXiv Detail & Related papers (2023-12-01T05:20:57Z) - Towards Self-Assembling Artificial Neural Networks through Neural
Developmental Programs [10.524752369156339]
Biological nervous systems are created in a fundamentally different way than current artificial neural networks.
By contrast, biological nervous systems are grown through a dynamic self-organizing process.
arXiv Detail & Related papers (2023-07-17T01:58:52Z) - Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks.
Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
arXiv Detail & Related papers (2023-03-30T02:40:28Z) - Spiking neural network for nonlinear regression [68.8204255655161]
Spiking neural networks carry the potential for a massive reduction in memory and energy consumption.
They introduce temporal and neuronal sparsity, which can be exploited by next-generation neuromorphic hardware.
A framework for regression using spiking neural networks is proposed.
arXiv Detail & Related papers (2022-10-06T13:04:45Z) - A multi-agent model for growing spiking neural networks [0.0]
This project has explored rules for growing the connections between the neurons in Spiking Neural Networks as a learning mechanism.
Results in a simulation environment showed that for a given set of parameters it is possible to reach topologies that reproduce the tested functions.
This project also opens the door to the usage of techniques like genetic algorithms for obtaining the best suited values for the model parameters.
arXiv Detail & Related papers (2020-09-21T15:11:29Z) - Neural Cellular Automata Manifold [84.08170531451006]
We show that the neural network architecture of the Neural Cellular Automata can be encapsulated in a larger NN.
This allows us to propose a new model that encodes a manifold of NCA, each of them capable of generating a distinct image.
In biological terms, our approach would play the role of the transcription factors, modulating the mapping of genes into specific proteins that drive cellular differentiation.
arXiv Detail & Related papers (2020-06-22T11:41:57Z) - Equilibrium Propagation for Complete Directed Neural Networks [0.0]
Most successful learning algorithm for artificial neural networks, backpropagation, is considered biologically implausible.
We contribute to the topic of biologically plausible neuronal learning by building upon and extending the equilibrium propagation learning framework.
arXiv Detail & Related papers (2020-06-15T22:12:30Z) - Training spiking neural networks using reinforcement learning [0.0]
We propose biologically-plausible alternatives to backpropagation to facilitate the training of spiking neural networks.
We focus on investigating the candidacy of reinforcement learning rules in solving the spatial and temporal credit assignment problems.
We compare and contrast the two approaches by applying them to traditional RL domains such as gridworld, cartpole and mountain car.
arXiv Detail & Related papers (2020-05-12T17:40:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.