Search Results for author: Atsushi Masumori

Found 6 papers, 1 papers with code

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.

Automata Quest: NCAs as a Video Game Life Mechanic

1 code implementation23 Sep 2023 Hiroki Sato, Tanner Lund, Takahide Yoshida, Atsushi Masumori

We study life over the course of video game history as represented by their mechanics.

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

Reactive, Proactive, and Inductive Agents: An evolutionary path for biological and artificial spiking networks

no code implementations18 Feb 2019 Lana Sinapayen, Atsushi Masumori, Ikegami Takashi

We propose an evolutionary path for neural networks, leading an organism from reactive behavior to simple proactive behavior and from simple proactive behavior to induction-based behavior.

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.

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