THESIS
2017
xi, 51 pages : illustrations (some color) ; 30 cm
Abstract
Psychophysics experiments showed that when multisensory stimulations arrive at
nearly the same time, the time interval between them is perceived to be shortened. This is
known as temporal integration. Meanwhile, neurophysics experiments showed that brain
cells and nerve cells are able to anticipate future direction and location, therefore it is deduce that they encode predictive information. The capacity of continuous attractor neural
networks (CANNs) in both temporal integration and prediction are considered. Furthermore, biological experiments showed that the coupling strengths of synapses between
neurons can experience short-term changes depending on their firing histories, which is
known as short-term synaptic plasticity (STP). We investigate how CANNs with STP
causes signals...[
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Psychophysics experiments showed that when multisensory stimulations arrive at
nearly the same time, the time interval between them is perceived to be shortened. This is
known as temporal integration. Meanwhile, neurophysics experiments showed that brain
cells and nerve cells are able to anticipate future direction and location, therefore it is deduce that they encode predictive information. The capacity of continuous attractor neural
networks (CANNs) in both temporal integration and prediction are considered. Furthermore, biological experiments showed that the coupling strengths of synapses between
neurons can experience short-term changes depending on their firing histories, which is
known as short-term synaptic plasticity (STP). We investigate how CANNs with STP
causes signals to shift in time, in particular, how temporal integration behaves when
network couplings and plasticities vary. We nd that STP enables richer dynamics of integration, including order and time scale dependent integration. We also investigate how
CANNs with STP track stimulus under stochastic processes. We find that STP allow the
network to encode past or future information.
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