1 code implementation • 22 May 2023 • Lana Sinapayen
This paper reports on patterns exhibiting self-replication with spontaneous, inheritable mutations and exponential genetic drift in Neural Cellular Automata.
no code implementations • 1 Dec 2022 • Manuel Baltieri, Hiroyuki Iizuka, Olaf Witkowski, Lana Sinapayen, Keisuke Suzuki
Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural and computational sciences.
1 code implementation • 25 Dec 2021 • Lana Sinapayen, Eiji Watanabe
Why do we sometimes perceive static images as if they were moving?
no code implementations • 3 Sep 2021 • Maciej Pietrowski, Andrzej Gajda, Takuto Yamamoto, Taisuke Kobayashi, Lana Sinapayen, Eiji Watanabe
GPU-specific computational processing is more indeterminate than that by CPUs, and hardware-derived uncertainties, which are often considered obstacles that need to be eliminated, might, in some cases, be successfully incorporated into the training of deep neural networks.
no code implementations • 24 Feb 2021 • Lana Sinapayen
Complex systems fail.
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.
1 code implementation • 16 Apr 2019 • Lana Sinapayen, Atsushi Noda
Yet PredNet cannot be trained to reach even mediocre accuracy on an artificial video dataset created with the rules of the Game of Life (GoL).
no code implementations • 18 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.
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.