Quantum Brain: A Recurrent Quantum Neural Network Model to Describe Eye Tracking of Moving Targets

Details Quantum Brain: A Recurrent Quantum Neural Network Model to Describe Eye Tracking of Moving Targets Quantum Brain: A Recurrent Quantum Neural Network Model to Describe Eye Tracking of Moving Targets

2004-07-02

arxiv.org/abs/q-bio/0407001v1

Biology

Quantitative Biology

Neurons and Cognition

7 pages, 7 figures submitted to Physical Review Letters

Scientific paper

10.1007/s10702-005-7125-6

A theoretical quantum brain model is proposed using a nonlinear Schroedinger wave equation. The model proposes that there exists a quantum process that mediates the collective response of a neural lattice (classical brain). The model is used to explain eye movements when tracking moving targets. Using a Recurrent Quantum Neural Network(RQNN) while simulating the quantum brain model, two very interesting phenomena are observed. First, as eye sensor data is processed in a classical brain, a wave packet is triggered in the quantum brain. This wave packet moves like a particle. Second, when the eye tracks a fixed target, this wave packet moves not in a continuous but rather in a discrete mode. This result reminds one of the saccadic movements of the eye consisting of 'jumps' and 'rests'. However, such a saccadic movement is intertwined with smooth pursuit movements when the eye has to track a dynamic trajectory. In a sense, this is the first theoretical model explaining the experimental observation reported concerning eye movements in a static scene situation. The resulting prediction is found to be very precise and efficient in comparison to classical objective modeling schemes such as the Kalman filter.

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