Exploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena

By Jun Tani. 2016

122 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Continual Lifelong Learning with Neural Networks: A Review (2018, background): “haviour can emerge from the dense interaction of hierarchically arranged neural circuits in a self-organized manner (Tani, 2016).”
Lifelong Learning of Spatiotemporal Representations With Dual-Memory Recurrent Self-Organization (2018, background): “Importantly, there may be no distinction between training and test phases, which requires the system to concurrently learn and timely trigger behavioral responses (Cangelosi and Schlesinger, 2015; Tani, 2016).”
Balancing Stability and Plasticity through Advanced Null Space in Continual Learning (2022, background): “Continual learning aims at developing algorithms for neural networks with the same capabilities from a stream of data [34,48].”
The hardware lottery (2020, background): “Our brains are able to ﬁne-tune and retain skills across our lifetime (Benna & Fusi, 2016; Bremner et al., 2013; Stein et al., 2004; Tani & Press, 2016; Gallistel & King, 2009; Tulving, 2002; Barnett & Ceci, 2002).”
Online Continual Learning from Imbalanced Data (2020, background): “Moreover, the learning and application of new knowledge takes place concurrently, rather than in two distinct stages, by interacting with our surrounding environ ment (Tani, 2016).”