IEEE Trans Biomed Eng. 2024 Nov;71(11):3098-3110. doi: 10.1109/TBME.2024.3408279. Epub 2024 Oct 25.
Develop a novel and highly efficient framework that decodes Inferior Colliculus (IC) neural activities for phoneme recognition.
We propose using Hyperdimensional Computing (HDC) to support an efficient phoneme recognition algorithm, in contrast to widely applied Deep Neural Networks (DNN). The high-dimensional representation and operations in HDC are rooted in human brain functionalities and naturally parallelizable, showing the potential for efficient neural activity analysis. Our proposed method includes a spatial and temporal-aware HDC encoder that effectively captures global and local patterns. As part of our framework, we deploy the lightweight HDC-based algorithm on a highly customizable and flexible hardware platform, i.e., Field Programmable Gate Arrays (FPGA), for optimal algorithm speedup. To evaluate our method, we record IC neural activities on gerbils while playing the sound of different phonemes.
We compare our proposed method with multiple baseline machine learning algorithms in recognition quality and learning efficiency, across different hardware platforms. The results show that our method generally achieves better classification quality than the best-performing baseline. Compared to the Deep Residual Neural Network (i.e., ResNet), our method shows a speedup up to 74×, 67×, 210× on CPU, GPU, and FPGA respectively. We achieve up to 15% (10%) higher accuracy in consonant (vowel) classification than ResNet.
By leveraging brain-inspired HDC for IC neural activity encoding and phoneme classification, we achieve orders of magnitude runtime speedup while improving accuracy in various challenging task settings.
Decoding IC neural activities is an important step to enhance understanding about human auditory system. However, these responses from the central auditory system are noisy and contain high variance, demanding large-scale datasets and iterative model fine-tuning. The proposed HDC-based framework is more scalable and viable for future real-world deployment thanks to its fast training and overall better quality.
开发一种新颖且高效的框架，用于对下丘（IC）神经活动进行解码以实现音素识别。
我们提出使用超维计算（HDC）来支持高效的音素识别算法，与广泛应用的深度神经网络（DNN）形成对比。HDC 中的高维表示和操作基于人类大脑功能，并且自然地可并行化，显示出有效分析神经活动的潜力。我们提出的方法包括一个具有空间和时间意识的 HDC 编码器，可有效地捕获全局和局部模式。作为我们框架的一部分，我们将基于轻量级 HDC 的算法部署在高度可定制和灵活的硬件平台（即现场可编程门阵列（FPGA））上，以实现最佳的算法加速。为了评估我们的方法，我们在播放不同音素的声音时，在沙鼠上记录 IC 神经活动。
我们在不同的硬件平台上，通过识别质量和学习效率，将我们提出的方法与多种基线机器学习算法进行了比较。结果表明，我们的方法通常比表现最好的基线实现了更好的分类质量。与深度残差神经网络（即 ResNet）相比，我们的方法在 CPU、GPU 和 FPGA 上的速度分别提高了 74 倍、67 倍和 210 倍。在辅音（元音）分类方面，我们的准确率比 ResNet 提高了 15%（10%）。
通过利用大脑启发的 HDC 对 IC 神经活动进行编码和音素分类，我们在提高准确性的同时实现了数量级的运行时加速，在各种具有挑战性的任务设置中都有出色的表现。
对 IC 神经活动进行解码是增强对人类听觉系统理解的重要步骤。然而，这些来自中枢听觉系统的反应是嘈杂的，并且包含很高的方差，需要大规模数据集和迭代模型的微调。由于其快速的训练和整体更高的质量，所提出的基于 HDC 的框架更具可扩展性和适用于未来的实际部署。
