no code implementations • 23 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".
no code implementations • 3 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.
no code implementations • 27 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.
no code implementations • 21 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.
no code implementations • 10 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.
no code implementations • 19 Mar 2019 • Olaf Witkowski, Takashi Ikegami
We propose an approach of open-ended evolution via the simulation of swarm dynamics.
no code implementations • 25 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.
no code implementations • 18 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.
no code implementations • 25 Nov 2014 • Julien Hubert, Takashi Ikegami
Memories in the brain are separated in two categories: short-term and long-term memories.
no code implementations • 1 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.
no code implementations • 11 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.