TY - BOOK

T1 - Novel mathematical neural models for visual attention

AU - Li, Kang

PY - 2016

Y1 - 2016

N2 - Visual attention has been extensively studied in psychology, but some fundamental questionsremain controversial. We focus on two questions in this study. First, we investigate how aneuron in visual cortex responds to multiple stimuli inside the receptive eld, described byeither a response-averaging or a probability-mixing model. Second, we discuss how stimuliare processed during visual search, explained by either a serial or a parallel mechanism.Here we present novel mathematical methods to answer the psychology questions from aneural perspective, combining the formulation of neural explanations for the visual attentiontheories and spiking neuron models for single spike trains. Statistical inference and modelselection are performed and various numerical methods are explored. The designed methodsalso give a framework for neural coding under visual attention theories. We conduct bothanalysis on real data and theoretical study with simulations.Our ndings are shown in separate projects. First, the probability-mixing model is favoredover the response-averaging model, shown by analysis on experimental data from monkeys.Second, both parallel and serial processing exist, with a tendency of being parallel in thebeginning and a tendency of being serial later on, shown by another set of experimental datafrom monkeys. Third, we show that the probability-mixing and response-averaging modelcan be separated and parameters can be successfully estimated for either model in a morerealistic biophysical system, supported by simulation study. Finally, we present the decodingof multiple temporal stimuli under these visual attention theories, also in a realistic biophysicalsituation with simulations.

AB - Visual attention has been extensively studied in psychology, but some fundamental questionsremain controversial. We focus on two questions in this study. First, we investigate how aneuron in visual cortex responds to multiple stimuli inside the receptive eld, described byeither a response-averaging or a probability-mixing model. Second, we discuss how stimuliare processed during visual search, explained by either a serial or a parallel mechanism.Here we present novel mathematical methods to answer the psychology questions from aneural perspective, combining the formulation of neural explanations for the visual attentiontheories and spiking neuron models for single spike trains. Statistical inference and modelselection are performed and various numerical methods are explored. The designed methodsalso give a framework for neural coding under visual attention theories. We conduct bothanalysis on real data and theoretical study with simulations.Our ndings are shown in separate projects. First, the probability-mixing model is favoredover the response-averaging model, shown by analysis on experimental data from monkeys.Second, both parallel and serial processing exist, with a tendency of being parallel in thebeginning and a tendency of being serial later on, shown by another set of experimental datafrom monkeys. Third, we show that the probability-mixing and response-averaging modelcan be separated and parameters can be successfully estimated for either model in a morerealistic biophysical system, supported by simulation study. Finally, we present the decodingof multiple temporal stimuli under these visual attention theories, also in a realistic biophysicalsituation with simulations.

UR - https://soeg.kb.dk/permalink/45KBDK_KGL/fbp0ps/alma99122937708205763

M3 - Ph.D. thesis

BT - Novel mathematical neural models for visual attention

PB - Department of Mathematical Sciences, Faculty of Science, University of Copenhagen

ER -