Dataset Information

Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition.

ABSTRACT: Humans can easily understand other people's actions through visual systems, while computers cannot. Therefore, a new bio-inspired computational model is proposed in this paper aiming for automatic action recognition. The model focuses on dynamic properties of neurons and neural networks in the primary visual cortex (V1), and simulates the procedure of information processing in V1, which consists of visual perception, visual attention and representation of human action. In our model, a family of the three-dimensional spatial-temporal correlative Gabor filters is used to model the dynamic properties of the classical receptive field of V1 simple cell tuned to different speeds and orientations in time for detection of spatiotemporal information from video sequences. Based on the inhibitory effect of stimuli outside the classical receptive field caused by lateral connections of spiking neuron networks in V1, we propose surround suppressive operator to further process spatiotemporal information. Visual attention model based on perceptual grouping is integrated into our model to filter and group different regions. Moreover, in order to represent the human action, we consider the characteristic of the neural code: mean motion map based on analysis of spike trains generated by spiking neurons. The experimental evaluation on some publicly available action datasets and comparison with the state-of-the-art approaches demonstrate the superior performance of the proposed model.

SUBMITTER: Shu N

PROVIDER: S-EPMC4489578 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition.

Shu Na N Gao Zhiyong Z Chen Xiangan X Liu Haihua H

PloS one 20150701 7

Humans can easily understand other people's actions through visual systems, while computers cannot. Therefore, a new bio-inspired computational model is proposed in this paper aiming for automatic action recognition. The model focuses on dynamic properties of neurons and neural networks in the primary visual cortex (V1), and simulates the procedure of information processing in V1, which consists of visual perception, visual attention and representation of human action. In our model, a family of ...[more]

PMID: 26132270

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Project description:UnlabelledWe do not fully understand how behavioral state modulates the processing and transmission of sensory signals. Here, we studied the cortical representation of the retinal image in mice that spontaneously switched between a state of rest and a constricted pupil, and one of active locomotion and a dilated pupil, indicative of heightened attention. We measured the selectivity of neurons in primary visual cortex for orientation and spatial frequency, as well as their response gain, in these two behavioral states. Consistent with prior studies, we found that preferred orientation and spatial frequency remained invariant across states, whereas response gain increased during locomotion relative to rest. Surprisingly, relative gain, defined as the ratio between the gain during locomotion and the gain during rest, was not uniform across the population. Cells tuned to high spatial frequencies showed larger relative gain compared with those tuned to lower spatial frequencies. The preferential enhancement of high-spatial-frequency information was also reflected in our ability to decode the stimulus from population activity. Finally, we show that changes in gain originate from shifts in the operating point of neurons along a spiking nonlinearity as a function of behavioral state. Differences in the relative gain experienced by neurons with high and low spatial frequencies are due to corresponding differences in how these cells shift their operating points between behavioral states.Significance statementHow behavioral state modulates the processing and transmission of sensory signals remains poorly understood. Here, we show that the mean firing rate and neuronal gain increase during locomotion as a result in a shift of the operating point of neurons. We define relative gain as the ratio between the gain of neurons during locomotion and rest. Interestingly, relative gain is higher in cells with preferences for higher spatial frequencies than those with low-spatial-frequency selectivity. This means that, during a state of locomotion and heightened attention, the population activity in primary visual cortex can support better spatial acuity, a phenomenon that parallels the improved spatial resolution observed in human subjects during the allocation of spatial attention.

Dataset Information

Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition.

Publications

Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition.

Similar Datasets

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