Synchronization in memristively pulse-coupled oscillator networks – experiments

放电神经元集合的时间同步性被认为是解释高级大脑功能（如感知或意识）的关键因素。在神经科学中，这一潜在的基本特征被称为绑定问题。一个物体的不同特征（如颜色和形状）的“结合”的空间和时间机制导致大脑中的整个物体表征，但尚未完全理解和探索。脉冲神经元可以用弛豫型振荡器来表示：基于可编程单结晶体管（PUTs）和运算放大器，研究了弛豫型振荡器的非线性动力学，即两个振荡器和一组振荡器的相图、分岔、相位响应曲线。神经元合理的机制，如泊松分布的个别神经元的放电率，以及信号延迟和可变的耦合强度将分别通过延迟线和忆阻器件来实现。虽然离散振荡器网络将进行调查，以了解信号延迟和可变耦合强度的基本动力学方面，更高的集成网络将建立在250纳米CMOS技术最后，同样的拓扑方面，从海马，以及同步机制将在时变网络的框架进行研究。为此，还提供了网络仿真器，它允许检查不同电路拓扑和网络诱导动态中的单个忆阻器件。

Temporal synchrony of firing neuronal ensembles is considered as a key ingredient to explain higher brain functions, such as perception or consciousness. The underlying basic feature is known as the binding problem in neuroscience. The spatial and temporal mechanism for the "binding" of different features of an object, such as color and shape, leads to an entire object representation in the brain, but is not fully understood and explored. Spiking neurons can be electronically represented by relaxation type oscillators: based-on programmable unijunction transistors (PUTs) and operational amplifiers the non-linear dynamics, i.e. phase portraits, bifurcation, phase response curves of two and ensembles of relaxation type oscillators are investigated. Neuronal justified mechanisms, like Poisson distributed firing rates of individual neurons, as well as signal retardation and variable coupling strength will be implemented, respectively, by delay lines and memristive devices. While discrete oscillator networks will be investigated to understand the fundamental dynamical aspects of signal delays and variable coupling strengths, higher integrated networks will be built on a 250 nm CMOS technology Finally, likewise topological aspects, as known from the Hippocampus, as well as synchrony mechanisms will be studied in the framework of time-varying networks. For this purpose, a network emulator is also available, which allows to examine individual memristive devices within different circuit topologies and network induced dynamics.