The interaction between integration and segmentation neurons for motion perception

Zarei, Parvin; Kameneva, Tatiana; Ibbotson, M.; Burkitt, Anthony N.; Grayden, David B.

doi:10.1186/1471-2202-16-s1-p86

Cited by 1 publication

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“…The majority of the neurons in the middle temporal (MT) area have receptive fields with antagonistic surrounds [11], [12]. An essential utility of surround mechanisms is to detect motion discontinuities or motion boundaries [13], [14] based on the differential motion. There are different kinds of surround geometry [15], [16].…”

Section: Introductionmentioning

confidence: 99%

The Hodgkin–Huxley neuron model for motion detection in image sequences

Yedjour

Meftah

Yedjour

et al. 2021

Neural Comput & Applic

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In this paper, we consider a biologically-inspired spiking neural network model for motion detection. The proposed model simulates the neurons' behavior in the cortical area MT to detect different kinds of motion in image sequences. We choose the conductance-based neuron model of the Hodgkin-Huxley to define MT cell responses. Based on the center-surround antagonism of MT receptive fields, we model the area MT by its great proportion of cells with directional selective responses. The network's spiking output corresponds to an MT neuron population's firing rates and enables to extract motion boundaries. We conduct several experiments on real image sequences. The experimental results show the proposed network's ability to segregate multiple moving objects from an image sequence and reproduce the MT cells' responses. We perform a quantitative evaluation on the YouTube Motion Boundaries (YMB) dataset, and we compare the result to state-ofthe-art methods for boundary detection in videos: boundary flow estimation (BF) and temporal boundary difference (BD). The proposed network model provides the best results on YMB compared to BF and BD methods.

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