Representing stimulus motion with waves in adaptive neural fields

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Computational Neuroscience Pub Date : 2024-04-12 DOI:10.1007/s10827-024-00869-z
Sage Shaw, Zachary P Kilpatrick
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Abstract

Traveling waves of neural activity emerge in cortical networks both spontaneously and in response to stimuli. The spatiotemporal structure of waves can indicate the information they encode and the physiological processes that sustain them. Here, we investigate the stimulus-response relationships of traveling waves emerging in adaptive neural fields as a model of visual motion processing. Neural field equations model the activity of cortical tissue as a continuum excitable medium, and adaptive processes provide negative feedback, generating localized activity patterns. Synaptic connectivity in our model is described by an integral kernel that weakens dynamically due to activity-dependent synaptic depression, leading to marginally stable traveling fronts (with attenuated backs) or pulses of a fixed speed. Our analysis quantifies how weak stimuli shift the relative position of these waves over time, characterized by a wave response function we obtain perturbatively. Persistent and continuously visible stimuli model moving visual objects. Intermittent flashes that hop across visual space can produce the experience of smooth apparent visual motion. Entrainment of waves to both kinds of moving stimuli are well characterized by our theory and numerical simulations, providing a mechanistic description of the perception of visual motion.

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在自适应神经场中用波来表示刺激运动
在大脑皮层网络中,神经活动的游走波既会自发出现,也会对刺激做出反应。波的时空结构可以表明它们所编码的信息以及维持它们的生理过程。在此,我们以视觉运动处理为模型,研究了自适应神经场中出现的行波的刺激-响应关系。神经场方程将大脑皮层组织的活动模拟为连续的可兴奋介质,自适应过程提供负反馈,产生局部活动模式。在我们的模型中,突触连通性由一个积分核来描述,该积分核会因活动相关的突触抑制而动态减弱,从而导致边缘稳定的行进前沿(具有衰减的后沿)或固定速度的脉冲。我们的分析量化了弱刺激如何随时间改变这些波的相对位置,我们通过扰动得到的波响应函数对其进行了描述。持续不断的可见刺激是移动视觉物体的模型。在视觉空间中跳跃的间歇性闪光可以产生平滑的视觉运动体验。我们的理论和数值模拟很好地描述了这两种运动刺激对波的诱导,从而提供了视觉运动感知的机理描述。
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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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