Search Results for author: Takashi Ikegami

Found 14 papers, 0 papers with code

A Simulation Environment for the Neuroevolution of Ant Colony Dynamics

no code implementations19 Jun 2024 Michael Crosscombe, Ilya Horiguchi, Norihiro Maruyama, Shigeto Dobata, Takashi Ikegami

We introduce a simulation environment to facilitate research into emergent collective behaviour, with a focus on replicating the dynamics of ant colonies.

Adaptability and Homeostasis in the Game of Life interacting with the evolved Cellular Automata

no code implementations9 May 2024 Keisuke Suzuki, Takashi Ikegami

In this paper we study the emergence of homeostasis in a two-layer system of the Game of Life, in which the Game of Life in the first layer couples with another system of cellular automata in the second layer.

Self-Replicating and Self-Employed Smart Contract on Ethereum Blockchain

no code implementations7 May 2024 Atsushi Masumori, Norihiro Maruyama, Takashi Ikegami

In order to run any program on Ethereum, Ether (currency on Ethereum) is required.

Implementation of Lenia as a Reaction-Diffusion System

no code implementations23 May 2023 Hiroki Kojima, Takashi Ikegami

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

no code implementations3 Feb 2021 Hiroki Kojima, Takashi Ikegami

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.

Generative Adversarial Network Hippocampus +1

Neural Autopoiesis: Organizing Self-Boundary by Stimulus Avoidance in Biological and Artificial Neural Networks

no code implementations27 Jan 2020 Atsushi Masumori, Lana Sinapayen, Norihiro Maruyama, Takeshi Mita, Douglas Bakkum, Urs Frey, Hirokazu Takahashi, Takashi Ikegami

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

no code implementations21 Nov 2019 Atsushi Masumori, Lana Sinapayen, Takashi Ikegami

Predictive coding can be regarded as a function which reduces the error between an input signal and a top-down prediction.

Temporal Sequences

An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue

no code implementations10 Sep 2019 Norman Packard, Mark A. Bedau, Alastair Channon, Takashi Ikegami, Steen Rasmussen, Kenneth O. Stanley, Tim Taylor

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.

Artificial Life Diversity

Learning by Stimulation Avoidance: A Principle to Control Spiking Neural Networks Dynamics

no code implementations25 Sep 2016 Lana Sinapayen, Atsushi Masumori, Takashi Ikegami

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

no code implementations18 May 2016 Martin Biehl, Takashi Ikegami, Daniel Polani

We present some arguments why existing methods for representing agents fall short in applications crucial to artificial life.

Artificial Life counterfactual

Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model

no code implementations1 Sep 2014 Alexander Woodward, Tom Froese, Takashi Ikegami

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

no code implementations11 Nov 2013 Tom Froese, Nathaniel Virgo, Takashi Ikegami

On this view, self-movement, adaptive behavior and morphological changes could have already been present at the origin of life.

Artificial Life

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