A fast fixed-point algorithm for independent component analysis
Neural Computation
Learning Recognition and Segmentation Using the Cresceptron
International Journal of Computer Vision
Independent component analysis: algorithms and applications
Neural Networks
Neural Networks as Cybernetic Systems
Neural Networks as Cybernetic Systems
Self-Organizing Maps
Neural Computation
On developmental mental architectures
Neurocomputing
Dually Optimal Neuronal Layers: Lobe Component Analysis
IEEE Transactions on Autonomous Mental Development
From neural networks to the brain: autonomous mental development
IEEE Computational Intelligence Magazine
Task Transfer by a Developmental Robot
IEEE Transactions on Evolutionary Computation
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It is largely unknown how the brain deals with time. The new field of research on autonomous development must enable machines to develop intelligent behaviors that respond not only to spatial features, but also temporal features. Hidden Markov Model (HMM) has a probability based mechanism to deal with time warping, but no effective online method exists that can deal with general temporal structure and temporal abstraction. By online, we mean that the agent must respond to spatial and temporal context immediately while the sensory stream flows in. By general temporal context, we mean various desirable temporal subsets, such as deletion (e.g., stop words) and variable temporal lengths (e.g., beyond bigrams and trigrams). By temporal abstraction, we mean using abstract meaning of context, instead of concrete forms. This paper proposes a brain inspired online scheme for making sequential decisions based on general temporal context. By sequential decisions, the action from the network depends on not only inputs and outputs but also emergent internal context states. In our neuromorphic scheme, the internal states are not predefined symbols, but distributed context depending on the internal attention. Our complexity analysis shows how this scheme greatly reduces the exponential time complexity O(2t) of all the possible number of contexts of length t down to linear time complexity O(cnt), where n is the number of neurons in the network and c is the average number of synapses of each neuron. In this paper, we concentrate on processing sequential text inputs by an online agent network under motor-supervised learning.