Response of Selective Attention in Middle Temporal Area
The primary visual cortex processes a large amount of visual information, however, due to its large receptive fields, when multiple stimuli fall within one receptive field, there are computational problems. To solve this problem, the visual system uses selective attention, which allocates resources to a specific spatial location, to attend to one of the stimuli in the receptive field. During this process, the center and width of the attending receptive field change. The model presented in the paper, which is extended and altered from Bobier et al., simulates the selective attention between the primary visual cortex, V1, and middle temporal (MT) area. The responses of the MT columns, which encode the target stimulus, are compared to the results of an experiment conducted by Womelsdorf et al. on the receptive field shift and shrinkage in macaque MT area from selective attention. Based on the results, the responses in the MT area are similar to the Gaussian shaped receptive fields found in the experiment. As well, the responses of the MT columns are also measured for accuracy of representing the target visual stimulus and is found to represent the stimulus with a root mean squared error around 0.17 to 0.18. The paper also explores varying model parameters, such as the membrane time constant and maximum firing rates, and how those affect the measurement. This model is a start to modeling the responses of selective attention, however there are still improvements that can be made to better compare with the experiment, produce more accurate responses and incorporate more biologically plausible features.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching
Stereo matching methods based on iterative optimization, like RAFT-Stereo and IGEV-Stereo, have evolved into a cornerstone in the field of stereo matching. However, these methods struggle to simultaneously capture high-f…
Stereo MatchingIIANet: An Intra- and Inter-Modality Attention Network for Audio-Visual Speech Separation
Recent research has made significant progress in designing fusion modules for audio-visual speech separation. However, they predominantly focus on multi-modal fusion at a single temporal scale of auditory and visual feat…
Speech SeparationRelaxing Anchor-Frame Dominance for Mitigating Hallucinations in Video Large Language Models
Recent Video Large Language Models (Video-LLMs) have demonstrated strong capability in video understanding, yet they still suffer from hallucinations. Existing mitigation methods typically rely on training, input modific…
Response GenerationRoToR: Towards More Reliable Responses for Order-Invariant Inputs
Mitigating positional bias of language models (LMs) for listwise inputs is a well-known and important problem (e.g., lost-in-the-middle). While zero-shot order-invariant LMs have been proposed to solve this issue, their …
Graph Question AnsweringMMLUQuestion AnsweringDeep Neural Encoder-Decoder Model to Relate fMRI Brain Activity with Naturalistic Stimuli
We propose an end-to-end deep neural encoder-decoder model to encode and decode brain activity in response to naturalistic stimuli using functional magnetic resonance imaging (fMRI) data. Leveraging temporally correlated…