Signature of an anticipatory response in area VI as modeled by a probabilistic model and a spiking neural network
Signature of an anticipatory response in area VI as modeled by a probabilistic model and a spiking neural network
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DOI：
10.1109/ijcnn.2014.6889847
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Bernhard A. Kaplan;M. A. Khoei;A. Lansner;Laurent Udo Perrinet
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文献类型：
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作者：
Bernhard A. Kaplan;M. A. Khoei;A. Lansner;Laurent Udo Perrinet
当面临固有的神经延迟时，视觉系统如何对快速变化的环境创建连贯的表示？在本文中，我们研究了基于运动的预测在估计运动轨迹以补偿延迟信息采样中的作用。特别是，我们研究了信息的各向异性扩散如何解释神经群体中记录的对接近刺激的预期反应的发展。我们使用抽象概率框架和尖峰神经网络 (SNN) 模型来验证这一点。受 Nijhawan [1] 提出的机制的启发，我们首先使用贝叶斯粒子滤波器框架，并引入基于对角线运动的预测模型，该模型推断对轨迹方向上的延迟刺激的估计响应。在 SNN 实现中，我们使用兴奋性细胞之间的这种各向异性、循环连接的模式作为运动外推机制。与最近在猕猴初级视觉皮层细胞外记录中收集的实验数据一致[2]，我们模拟了不同的轨迹长度，并探索了预期反应如何依赖于沿着轨迹积累的信息。我们证明我们的概率框架和 SNN 模型都可以定性地复制实验数据。最重要的是，我们强调了开发依赖于轨迹的预期响应的要求，特别是导致运动外推机制的连接模式的各向异性性质。
As it is confronted to inherent neural delays, how does the visual system create a coherent representation of a rapidly changing environment? In this paper, we investigate the role of motion-based prediction in estimating motion trajectories compensating for delayed information sampling. In particular, we investigate how anisotropic diffusion of information may explain the development of anticipatory response as recorded in a neural populations to an approaching stimulus. We validate this using an abstract probabilistic framework and a spiking neural network (SNN) model. Inspired by a mechanism proposed by Nijhawan [1], we first use a Bayesian particle filter framework and introduce a diagonal motion-based prediction model which extrapolates the estimated response to a delayed stimulus in the direction of the trajectory. In the SNN implementation, we have used this pattern of anisotropic, recurrent connections between excitatory cells as mechanism for motion-extrapolation. Consistent with recent experimental data collected in extracellular recordings of macaque primary visual cortex [2], we have simulated different trajectory lengths and have explored how anticipatory responses may be dependent on the information accumulated along the trajectory. We show that both our probabilistic framework and the SNN model can replicate the experimental data qualitatively. Most importantly, we highlight requirements for the development of a trajectory-dependent anticipatory response, and in particular the anisotropic nature of the connectivity pattern which leads to the motion extrapolation mechanism.