27 citations · 36 across the 17 of their papers we have counts for
7 papers · 1 filter
Transient State Reorganization and Cell Differentiation in the Developmental Dynamics of Growing Neural Cellular Automata
Hiroki Sato, Atsushi Masumori, Takashi Ikegami
Growing Neural Cellular Automata (GNCA) develop complex morphologies from a single seed cell through shared local rules, yet the internal dynamics of this process remain poorly und…
Structured Fluctuations and the Information Dynamics of Self-Maintenance in Growing Neural Cellular Automata
Atsushi Masumori, Hiroki Sato, Takashi Ikegami
Growing Neural Cellular Automata (GNCA) are capable of robust self-maintenance and self-repair, yet the internal dynamical mechanisms that support these capabilities remain poorly…
Self-Replicating and Self-Employed Smart Contract on Ethereum Blockchain
Atsushi Masumori, Norihiro Maruyama, Takashi Ikegami
Blockchain is the underlying technology for cryptocurrencies such as Bitcoin. Blockchain is a robust distributed ledger that uses consensus algorithms to approve transactions in a…
Neural Autopoiesis: Organizing Self-Boundary by Stimulus Avoidance in Biological and Artificial Neural Networks
Atsushi Masumori, Lana Sinapayen, Norihiro Maruyama +5
Living organisms must actively maintain themselves in order to continue existing. Autopoiesis is a key concept in the study of living organisms, where the boundaries of the organis…
Predictive Coding as Stimulus Avoidance in Spiking Neural Networks
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. If reducing the error is equivalent to reducing the influ…
Reactive, Proactive, and Inductive Agents: An evolutionary path for biological and artificial spiking networks
Lana Sinapayen, Atsushi Masumori, Ikegami Takashi
Complex environments provide structured yet variable sensory inputs. To best exploit information from these environments, organisms must evolve the ability to anticipate consequenc…
Learning by Stimulation Avoidance: A Principle to Control Spiking Neural Networks Dynamics
Lana Sinapayen, Atsushi Masumori, Takashi Ikegami
Learning based on networks of real neurons, and by extension biologically inspired models of neural networks, has yet to find general learning rules leading to widespread applicati…