The method to map the neural signals to the neuron from which it originates is spike sorting. A low-power spike sorting system is presented for a neural implant device. The spike sorter constitutes a two-step trainer module that is shared by the signal acquisition channel associated with multiple electrodes. A low-power Spiking Neural Network (SNN) module is responsible for assigning the spike class. The two-step shared supervised on-chip training module is presented for improved training accuracy for the SNN. Post implant, the relatively power-hungry training module can be activated conditionally based on a statistics-driven retraining algorithm that allows on the fly training and adaptation. A low-power analog implementation for the SNN classifier is proposed based on resistive crossbar memory exploiting its approximate computing nature. Owing to the direct mapping of SNN functionality using physical characteristics of devices, the analog mode implementation can achieve ∼21 × lower power than its fully digital counterpart. We also incorporate the effect of device variation in the training process to suppress the impact of inevitable inaccuracies in such resistive crossbar devices on the classification accuracy. A variation-aware, digitally calibrated analog front-end is also presented, which consumes less than ∼50 nW power and interfaces with the digital training module as well as the analog SNN spike sorting module. Hence, the proposed scheme is a low-power, variation-tolerant, adaptive, digitally trained, all-analog spike sorter device, applicable to implantable and wearable multichannel brain-machine interfaces.

中文翻译：

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使用模拟尖峰神经网络分类器的高效尖峰排序方案

将神经信号映射到其起源的神经元的方法是尖峰排序。提出了一种用于神经植入装置的低功率尖峰分类系统。尖峰分类器构成一个由与多个电极关联的信号采集通道共享的两步训练器模块。一个低功耗尖峰神经网络 (SNN) 模块负责分配尖峰类。提出了两步共享监督片上训练模块，以提高 SNN 的训练精度。植入后，可以根据统计驱动的再训练算法有条件地激活相对耗电的训练模块，该算法允许动态训练和适应。基于电阻纵横存储器利用其近似计算特性，提出了一种用于 SNN 分类器的低功耗模拟实现。由于使用设备的物理特性直接映射 SNN 功能，模拟模式实现可以实现比其全数字对应物低 21 倍的功耗。我们还在训练过程中加入了设备变化的影响，以抑制此类电阻式交叉开关设备中不可避免的不准确性对分类准确性的影响。还展示了一个变化感知、数字校准的模拟前端，它消耗不到 50 nW 的功率，并与数字训练模块以及模拟 SNN 尖峰排序模块接口。因此，所提出的方案是一种低功耗、容变、自适应、数字训练、全模拟尖峰分选设备，适用于可植入和可穿戴的多通道脑机接口。