Yu Theodore, Park Jongkil, Joshi Siddharth, Maier Christoph, Cauwenberghs Gert
Silicon Valley Labs of Texas Instruments, Santa Clara, CA 95051, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:775-8. doi: 10.1109/EMBC.2012.6346046.
Synchrony and temporal coding in the central nervous system, as the source of local field potentials and complex neural dynamics, arises from precise timing relationships between spike action population events across neuronal assemblies. Recently it has been shown that coincidence detection based on spike event timing also presents a robust neural code invariant to additive incoherent noise from desynchronized and unrelated inputs. We present spike-based coincidence detection using integrate-and-fire neural membrane dynamics along with pooled conductance-based synaptic dynamics in a hierarchical address-event architecture. Within this architecture, we encode each synaptic event with parameters that govern synaptic connectivity, synaptic strength, and axonal delay with additional global configurable parameters that govern neural and synaptic temporal dynamics. Spike-based coincidence detection is observed and analyzed in measurements on a log-domain analog VLSI implementation of the integrate-and-fire neuron and conductance-based synapse dynamics.
作为局部场电位和复杂神经动力学的来源，中枢神经系统中的同步性和时间编码源自神经元集合中尖峰动作群体事件之间精确的时间关系。最近的研究表明，基于尖峰事件时间的重合检测也呈现出一种强大的神经编码，对来自去同步化和不相关输入的加性非相干噪声具有不变性。我们在分层地址事件架构中，利用积分发放神经膜动力学以及基于池化电导的突触动力学，提出了基于尖峰的重合检测方法。在这个架构中，我们用控制突触连接性、突触强度和轴突延迟的参数，以及控制神经和突触时间动力学的额外全局可配置参数，对每个突触事件进行编码。基于尖峰的重合检测在积分发放神经元和基于电导的突触动力学的对数域模拟VLSI实现的测量中得到观察和分析。
