发表机构
Sharif University of Technology; University of Tehran; Islamic Azad University; Pasargad Institute for Advanced Innovative Solutions (PIAIS)（谢里夫理工大学; 德黑兰大学; 伊斯兰阿扎德大学; 帕萨加德高级创新解决方案研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文重实现Shirazi的贝叶斯-马尔可夫神经形态模型，通过定量模拟验证其可产生尖锐方向选择性等核心行为，还添加脉冲式SCI层实现并完成相关评估。
AI 中文摘要
初级视觉皮层（V1）中方向选择性的产生仍是计算神经科学的核心问题。Shirazi的贝叶斯-马尔可夫模型提出了一种概率解释，说明非定向的外侧膝状体（LGN）输入如何通过局部推理产生方向选择性抑制。在该模型中，纹状皮层抑制性（SCI）细胞的活动模式通过两层分层马尔可夫随机场的最大后验（MAP）准则从LGN活动模式中估计，所得推理通过局部并行松弛算法实现。本文对该框架进行了计算上明确的重实现和定量模拟研究：我们重构了数学模型，描述了其完全由LGN驱动的更新规则，并实现了保留原始局部团操作的向量化模拟框架，使系统的参数扫描成为可能；我们使用方向调谐曲线、方向选择性指数（OSI）、受控LGN噪声扰动、对比度测试及模型变体比较对模型进行评估；我们还添加了使用 leaky integrate-and-fire 和 Hodgkin-Huxley 神经元的脉冲式SCI层实现，以检验速率编码的SCI场是否可通过时间上明确的神经活动表达。模拟结果支持贝叶斯-马尔可夫框架的核心定性行为：尖锐的方向选择性、对中等程度LGN噪声的鲁棒性，以及推理得到的抑制场的生物学可解释的概念验证脉冲实现。
英文摘要
The emergence of orientation selectivity in the primary visual cortex (V1) remains a central question in computational neuroscience. Shirazi's Bayes-Markov model proposed a probabilistic explanation for how orientation-selective inhibition can arise from non-oriented lateral geniculate nucleus (LGN) inputs through local inference. In that formulation, the activity pattern of striate cortical inhibitory (SCI) cells is estimated from the LGN activity pattern by a maximum a posteriori (MAP) criterion over a two-layer hierarchical Markov random field, and the resulting inference is implemented through a local parallel relaxation algorithm. We provide a computationally explicit re-implementation and quantitative simulation study of this framework. We reconstruct the mathematical model, describe its fully LGN-driven update rule, and implement a vectorized simulation framework that preserves the original local clique operations while making systematic parameter sweeps feasible. We evaluate the model using orientation tuning curves, an orientation selectivity index (OSI), controlled LGN noise perturbations, contrast tests, and model-variant comparisons. We further add a spiking SCI-layer realization using leaky integrate-and-fire and Hodgkin-Huxley neurons to examine whether the rate-coded SCI field can be expressed through temporally explicit neural activity. The simulations support the central qualitative behavior of the Bayes-Markov framework: sharp orientation selectivity, robustness to moderate LGN noise, and a biologically interpretable proof-of-concept spiking realization of the inferred inhibitory field.
Comments24 pages. Computational neuroscience; orientation selectivity; neuromorphic modeling
