Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
Department of Computer Science, University of Sheffield, Sheffield, UK.
Sci Rep. 2021 Sep 9;11(1):17904. doi: 10.1038/s41598-021-97217-3.
Stimulus-Specific Adaptation (SSA) to repetitive stimulation is a phenomenon that has been observed across many different species and in several brain sensory areas. It has been proposed as a computational mechanism, responsible for separating behaviorally relevant information from the continuous stream of sensory information. Although SSA can be induced and measured reliably in a wide variety of conditions, the network details and intracellular mechanisms giving rise to SSA still remain unclear. Recent computational studies proposed that SSA could be associated with a fast and synchronous neuronal firing phenomenon called Population Spikes (PS). Here, we test this hypothesis using a mean-field rate model and corroborate it using a neuromorphic hardware. As the neuromorphic circuits used in this study operate in real-time with biologically realistic time constants, they can reproduce the same dynamics observed in biological systems, together with the exploration of different connectivity schemes, with complete control of the system parameter settings. Besides, the hardware permits the iteration of multiple experiments over many trials, for extended amounts of time and without losing the networks and individual neural processes being studied. Following this "neuromorphic engineering" approach, we therefore study the PS hypothesis in a biophysically inspired recurrent networks of spiking neurons and evaluate the role of different linear and non-linear dynamic computational primitives such as spike-frequency adaptation or short-term depression (STD). We compare both the theoretical mean-field model of SSA and PS to previously obtained experimental results in the area of novelty detection and observe its behavior on its neuromorphic physical equivalent model. We show how the approach proposed can be extended to other computational neuroscience modelling efforts for understanding high-level phenomena in mechanistic models.
刺激特异性适应（SSA）是一种在许多不同物种和几个大脑感觉区域中都观察到的现象。它被提出作为一种计算机制，负责将与行为相关的信息与连续的感觉信息流分离。尽管 SSA 可以在广泛的条件下可靠地诱导和测量，但产生 SSA 的网络细节和细胞内机制仍不清楚。最近的计算研究提出，SSA 可能与一种称为群体峰（PS）的快速和同步神经元放电现象有关。在这里，我们使用平均场率模型来检验这个假设，并使用神经形态硬件来证实它。由于本研究中使用的神经形态电路以具有生物学现实时间常数的实时方式运行，因此它们可以再现生物系统中观察到的相同动态，同时探索不同的连接方案，并完全控制系统参数设置。此外，硬件允许在多次试验中迭代多次实验，延长时间，而不会丢失正在研究的网络和单个神经过程。通过这种“神经形态工程”方法，我们在生物启发的脉冲神经元递归网络中研究 PS 假设，并评估不同线性和非线性动态计算基元（如尖峰频率适应或短期抑制（STD））的作用。我们比较了 SSA 和 PS 的理论平均场模型与该领域新颖性检测中先前获得的实验结果，并观察其在神经形态物理等效模型上的行为。我们展示了如何扩展所提出的方法以用于理解机制模型中的高级现象的其他计算神经科学建模工作。
