The nervous system is under tight energy constraints and must represent information efficiently. This is particularly relevant in the dorsal part of the medial superior temporal area (MSTd) in primates where neurons encode complex motion patterns to support a variety of behaviors. A sparse decomposition model based on a dimensionality reduction principle known as non-negative matrix factorization (NMF) was previously shown to account for a wide range of monkey MSTd visual response properties. This model resulted in sparse, parts-based representations that could be regarded as basis flow fields, a linear superposition of which accurately reconstructed the input stimuli. This model provided evidence that the seemingly complex response properties of MSTd may be a by-product of MSTd neurons performing dimensionality reduction on their input. However, an open question is how a neural circuit could carry out this function. In the current study, we propose a spiking neural network (SNN) model of MSTd based on evolved spike-timing-dependent plasticity and homeostatic synaptic scaling (STDP-H) learning rules. We demonstrate that the SNN model learns compressed and efficient representations of the input patterns similar to the patterns that emerge from NMF, resulting in MSTd-like receptive fields observed in monkeys. This SNN model suggests that STDP-H observed in the nervous system may be performing a similar function as NMF with sparsity constraints, which provides a test bed for mechanistic theories of how MSTd may efficiently encode complex patterns of visual motion to support robust self-motion perception.

SIGNIFICANCE STATEMENT The brain may use dimensionality reduction and sparse coding to efficiently represent stimuli under metabolic constraints. Neurons in monkey area MSTd respond to complex optic flow patterns resulting from self-motion. We developed a spiking neural network model that showed MSTd-like response properties can emerge from evolving spike-timing-dependent plasticity with STDP-H parameters of the connections between then middle temporal area and MSTd. Simulated MSTd neurons formed a sparse, reduced population code capable of encoding perceptual variables important for self-motion perception. This model demonstrates that complex neuronal responses observed in MSTd may emerge from efficient coding and suggests that neurobiological plasticity, like STDP-H, may contribute to reducing the dimensions of input stimuli and allowing spiking neurons to learn sparse representations.

神经系统受到严格的能量限制，必须有效地表达信息。这与灵长类动物内侧上颞区 (MSTd) 的背侧部分尤其相关，其中神经元编码复杂的运动模式以支持各种行为。基于称为非负矩阵分解 (NMF) 的降维原理的稀疏分解模型先前已被证明可以解释各种猴子 MSTd 视觉响应特性。该模型产生了稀疏的、基于部分的表示，可以被视为基础流场，其线性叠加准确地重建了输入刺激。该模型提供的证据表明，MSTd 看似复杂的响应特性可能是 MSTd 神经元对其输入进行降维的副产品。然而，一个悬而未决的问题是神经回路如何执行这一功能。在当前的研究中，我们提出了一种基于进化的尖峰时序依赖性可塑性和稳态突触缩放（STDP-H）学习规则的 MSTd 尖峰神经网络（SNN）模型。我们证明，SNN 模型可以学习类似于 NMF 中出现的模式的输入模式的压缩且有效的表示，从而在猴子中观察到类似 MSTd 的感受野。该 SNN 模型表明，在神经系统中观察到的 STDP-H 可能执行与具有稀疏约束的 NMF 类似的功能，这为 MSTd 如何有效编码复杂的视觉运动模式以支持鲁棒的自运动的机械理论提供了测试平台洞察力。

意义声明大脑可以使用降维和稀疏编码来有效地表示代谢限制下的刺激。猴子 MSTd 区域的神经元对自我运动产生的复杂光流模式做出反应。我们开发了一种尖峰神经网络模型，该模型表明，与中颞区和 MSTd 之间连接的 STDP-H 参数一起演化的尖峰时间依赖性可塑性可以产生类似 MSTd 的响应特性。模拟的 MSTd 神经元形成了稀疏、精简的群体代码，能够编码对自我运动感知很重要的感知变量。该模型表明，在 MSTd 中观察到的复杂神经元反应可能来自有效的编码，并表明神经生物学可塑性（如 STDP-H）可能有助于减少输入刺激的维度并允许尖峰神经元学习稀疏表示。