Search Results for author:
Found 10 papers, 8 papers with code
Assessing the robustness of critical behavior in stochastic cellular automata
We systematically perturb the probabilities of an optimal stochastic CA known to produce critical behavior, and we report that such a CA is able to remain in a critical regime up to a certain degree of noise.
Towards the Neuroevolution of Low-level Artificial General Intelligence
We evaluate a method to evolve a biologically-inspired artificial neural network that learns from environment reactions named Neuroevolution of Artificial General Intelligence (NAGI), a framework for low-level AGI.
Collective control of modular soft robots via embodied Spiking Neural Cellular Automata
Voxel-based Soft Robots (VSRs) are a form of modular soft robots, composed of several deformable cubes, i. e., voxels.
A Unified Substrate for Body-Brain Co-evolution
The genome of such a multicellular organism guides the development of its body from a single cell, including its control system.
Towards self-organized control: Using neural cellular automata to robustly control a cart-pole agent
The observations of the environment are transmitted in input cells, while the values of output cells are used as a readout of the system.
A deep learning based tool for automatic brain extraction from functional magnetic resonance images in rodents
In this study, we address this problem by implementing a neural network based method that uses a U-Net architecture to segment the brain area into a mask and removing the skull and other tissues from the image.
A general representation of dynamical systems for reservoir computing
The advantages of such implementation are its usage on specialized and optimized deep learning libraries, the capabilities to generalize it to other types of networks and the possibility to evolve cellular automata and other dynamical systems in terms of connectivity, update and learning rules.
DeepTEGINN: Deep Learning Based Tools to Extract Graphs from Images of Neural Networks
The goals are to simplify the training and usage of deep learning methods for computer vision and facilitate its integration into graph extraction pipelines.
A Conceptual Bio-Inspired Framework for the Evolution of Artificial General Intelligence
In this work, a conceptual bio-inspired parallel and distributed learning framework for the emergence of general intelligence is proposed, where agents evolve through environmental rewards and learn throughout their lifetime without supervision, i. e., self-learning through embodiment.
Bidirectional Learning for Robust Neural Networks
A multilayer perceptron can behave as a generative classifier by applying bidirectional learning (BL).
