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Found 10 papers, 3 papers with code
Self-Replication, Spontaneous Mutations, and Exponential Genetic Drift in Neural Cellular Automata
This paper reports on patterns exhibiting self-replication with spontaneous, inheritable mutations and exponential genetic drift in Neural Cellular Automata.
Hybrid Life: Integrating Biological, Artificial, and Cognitive Systems
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.
Evolutionary Generation of Visual Motion Illusions
Why do we sometimes perceive static images as if they were moving?
Impact of GPU uncertainty on the training of predictive deep neural networks
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.
Neural Autopoiesis: Organizing Self-Boundary by Stimulus Avoidance in Biological and Artificial Neural Networks
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
Predictive coding can be regarded as a function which reduces the error between an input signal and a top-down prediction.
DNN Architecture for High Performance Prediction on Natural Videos Loses Submodule's Ability to Learn Discrete-World Dataset
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).
Reactive, Proactive, and Inductive Agents: An evolutionary path for biological and artificial spiking networks
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
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.
