Successive learning in hetero-associative memory using chaotic neural networks
Related Experiment Video
Updated: Oct 3, 2026

A Lateralized Odor Learning Model in Neonatal Rats for Dissecting Neural Circuitry Underpinning Memory Formation
Published on: August 18, 2014
1Department of Electrical Engineering, Faculty of Science and Technology, Keio University, Yokohama, Japan. osana@soft.ics.keio.ac.jp
Abstract:
In this paper, we propose a successive learning method in hetero-associative memories, such as Bidirectional Associative Memories and Multidirectional Associative Memories, using chaotic neural networks. It can distinguish unknown data from the stored known data and can learn the unknown data successively. The proposed model makes use of the difference in the response to the input data in order to distinguish unknown data from the stored known data. When input data is regarded as unknown data, it is memorized. Furthermore, the proposed model can estimate and learn correct data from noisy unknown data or incomplete unknown data by considering the temporal summation of the continuous data input. In addition, similarity to the physiological facts in the olfactory bulb of a rabbit found by Freeman are observed in the behavior of the proposed model. A series of computer simulations shows the effectiveness of the proposed model.
Related Concept Videos
Long-term Potentiation
Long-term Potentiation
Hebbian LTP
LTP can occur when presynaptic neurons...
Neural Circuits
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Higher Mental Functions of Brain: Learning and Memory
Associative Learning
Classical conditioning, also known...
Real-World Application of Classical Conditioning
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...

