Title :
Effects of neural entrainment within a biologically realistic network
Author :
Canfield, John ; Carter, Michael
Author_Institution :
Dept. of Electr. & Comput. Eng., New Hampshire Univ., Durham, NH, USA
Abstract :
A network of realistic neuron models interconnected by Hebbian synapses is shown to be capable of autoassociative recall. However, a collective network property, the robust entrainment of activity between neurons, leads to several interesting network level phenomena not seen in attractor networks with conventional neuron models. These include phase-locked bursting patterns of neural activity, the temporal segmentation of mixed pattern input cues, and the functionality to serve as a subunit for the storage of temporal sequences. Given the previously established accuracy of the utilized single neuron model as a reduced order approximation of the Hodgkin-Huxley equations, these findings are well suited for comparison with their biological correlates
Keywords :
Hebbian learning; brain models; electroencephalography; neurophysiology; Hebbian synapses; Hodgkin-Huxley equations; autoassociative recall; biologically realistic network; collective network property; mixed pattern input cues; neural activity; neural entrainment; phase-locked bursting patterns; realistic neuron models; reduced order approximation; single neuron model; temporal segmentation; temporal sequence storage; Biological system modeling; Brain modeling; Calcium; Cerebral cortex; Electrodes; Electroencephalography; Equations; Neurons; Robustness; Signal processing;
Conference_Titel :
Bioengineering Conference, 1997., Proceedings of the IEEE 1997 23rd Northeast
Conference_Location :
Durham, NH
Print_ISBN :
0-7803-3848-0
DOI :
10.1109/NEBC.1997.594937