Brain-Inspired Neuronal Model of Attention and Memory
Lead Research Organisation:
Plymouth University
Department Name: Centre for Theoretical and Comp Neurosci
Abstract
Attention is necessary and vital for living organisms due to the limited processing capability of the visual system which precludes the rapid analysis of the whole visual scene. Selective visual attention is a cognitive process that allows a living organism to extract from the incoming visual information the part that is most important at a given moment and that should be processed in more detail. For example, detailed processing of the extracted information can include novelty detection and allocation of a novel object to memory.In this project a large-scale brain-inspired model of hierarchically organised spiking neurons will be developed, that solves the problem of consecutive selection of objects by combining object oriented attention, memory, and novelty detection. Since we believe that the brain does not invent a special processing mechanism for each cognitive function but adapts similar mechanisms for a particular type of processing, it is a challenge to develop a model based on a small set of general principles of information processing (e.g. synchronisation, adaptation of natural frequencies, resonance amplitude increase). We believe that these theoretical principles are the key to the performance of the biological brain and within the proposed research will be implemented for the first time in combined model of attention and memory. Such developments offer great potential, both in shedding fresh light on the basic mechanisms underpinning information processing in the brain and in the design of a new generation of computational devices, cognitive robots, etc.
Organisations
People |
ORCID iD |
Roman Borisyuk (Principal Investigator) |
Publications
Borisyuk R
(2007)
Metastable states, phase transitions, and persistent neural activity.
in Bio Systems
Borisyuk R
(2013)
Spiking neural network model for memorizing sequences with forward and backward recall.
in Bio Systems
Borisyuk R
(2009)
Visual perception of ambiguous figures: synchronization based neural models.
in Biological cybernetics
Borisyuk R
(2007)
International symposium: theory and neuroinformatics in research related to deep brain stimulation.
in Expert review of medical devices
Borisyuk R
(2008)
Stochasticity and functionality of neural systems: Mathematical modelling of axon growth in the spinal cord of tadpole
in Biosystems
Borisyuk R
(2016)
Forecasting the 2005 General Election: A Neural Network Approach
in The British Journal of Politics and International Relations
Borisyuk R
(2006)
Oscillations and waves in the models of interactive neural populations.
in Bio Systems
Borisyuk R
(2009)
A neural model of selective attention and object segmentation in the visual scene: an approach based on partial synchronization and star-like architecture of connections.
in Neural networks : the official journal of the International Neural Network Society
Borisyuk R
(2009)
Model of the tadpole spinal cord: The interplay of deterministic and stochastic processes in development of specialised neural circuit
in IFAC Proceedings Volumes
Borisyuk R.
(2009)
Selective attention model of moving objects
in Neural Network World
Description | We have discovered several important details on neuronal mechanisms of cognitive functions. For example, we have developed a computational model of visual perception of the Necker cube (ambiguous figure) . Remarkably, this model switches between two perceptions in a way which is consistent with results of psychological experiments. |
Exploitation Route | The findings are published and can be used by others. Software was submitted to the public database and can be used by any researcher. |
Sectors | Healthcare |
Description | Findings have been published in International research journals. They have attracted attention of other researchers and have been cited by some papers. I believe that these findings make a progress in our understanding the brain mechanisms of cognitive functions. |