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Found 11 papers, 0 papers with code
Implementation of Lenia as a Reaction-Diffusion System
Stemming from these insights, we establish that asymptotic Lenia can be replicated by an RD system composed solely of diffusion and spatially local reaction terms, resulting in the simulated asymptotic Lenia based on an RD system, or "RD Lenia".
Organization of a Latent Space structure in VAE/GAN trained by navigation data
We present a novel artificial cognitive mapping system using generative deep neural networks, called variational autoencoder/generative adversarial network (VAE/GAN), which can map input images to latent vectors and generate temporal sequences internally.
Neural Autopoiesis: Organizing Self-Boundary by Stimulus Avoidance in Biological and Artificial Neural Networks
In this paper, as a result of our experiments using embodied cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: if the agent cannot learn an action to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes, as if to ignore the uncontrollable-input.
Predictive Coding as Stimulus Avoidance in Spiking Neural Networks
Predictive coding can be regarded as a function which reduces the error between an input signal and a top-down prediction.
An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue
Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving.
How to Make Swarms Open-Ended? Evolving Collective Intelligence Through a Constricted Exploration of Adjacent Possibles
We propose an approach of open-ended evolution via the simulation of swarm dynamics.
Learning by Stimulation Avoidance: A Principle to Control Spiking Neural Networks Dynamics
We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot.
Towards information based spatiotemporal patterns as a foundation for agent representation in dynamical systems
We present some arguments why existing methods for representing agents fall short in applications crucial to artificial life.
Short-Term Memory Through Persistent Activity: Evolution of Self-Stopping and Self-Sustaining Activity in Spiking Neural Networks
Memories in the brain are separated in two categories: short-term and long-term memories.
Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model
In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems.
Motility at the origin of life: Its characterization and a model
On this view, self-movement, adaptive behavior and morphological changes could have already been present at the origin of life.
