Neuromorphic applications emulate the processing performed by the brain by using spikes as inputs instead of time-varying analog stimuli. Therefore, these time-varying stimuli have to be encoded into spikes, which can induce important information loss. To alleviate this loss, some studies use population coding strategies to encode more information using a population of neurons rather than just one neuron. However, configuring the encoding parameters of such a population is an open research question. This work proposes an approach based on maximizing the mutual information between the signal and the spikes in the population of neurons. The proposed algorithm is inspired by the information-theoretic framework of Partial Information Decomposition. Two applications are presented: blood pressure pulse wave classification, and neural action potential waveform classification. In both tasks, the data is encoded into spikes and the encoding parameters of the neuron populations are tuned to maximize the encoded information using the proposed algorithm. The spikes are then classified and the performance is measured using classification accuracy as a metric. Two key results are reported. Firstly, adding neurons to the population leads to an increase in both mutual information and classification accuracy beyond what could be accounted for by each neuron separately, showing the usefulness of population coding strategies. Secondly, the classification accuracy obtained with the tuned parameters is near-optimal and it closely follows the mutual information as more neurons are added to the population. Furthermore, the proposed approach significantly outperforms random parameter selection, showing the usefulness of the proposed approach. These results are reproduced in both applications.

翻译：神经形态应用通过使用脉冲作为输入而非时变模拟刺激来模拟大脑的处理过程。因此，这些时变刺激必须被编码为脉冲，这一过程可能导致显著的信息损失。为减轻此类损失，部分研究采用群体编码策略，通过神经元群体而非单一神经元来编码更多信息。然而，此类群体的编码参数配置仍是一个开放的研究问题。本研究提出一种基于最大化信号与神经元群体间互信息的方法。该算法受部分信息分解的信息论框架启发。本文展示了两个应用案例：血压脉搏波分类与神经动作电位波形分类。在这两项任务中，数据均被编码为脉冲，并采用所提算法调整神经元群体的编码参数以最大化编码信息。随后对脉冲进行分类，并以分类准确率作为性能度量指标。研究获得两项关键结果：首先，增加群体神经元数量会使互信息与分类准确率同步提升，其增益超出各神经元独立贡献的总和，这证明了群体编码策略的有效性；其次，经参数调优获得的分类准确率接近最优值，且随着群体神经元数量的增加，其变化趋势与互信息高度一致。此外，所提方法显著优于随机参数选择策略，验证了该方法的实用性。这些结果在两个应用案例中均得到复现。