生物模拟视觉信息时空编码方法用于体内生物神经网络
Xingchen Wang1,2, Bo Lv1, Fengzhen Tang1
1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shengyang 110016, China.
Biomimetics (Basel, Switzerland)
|June 25, 2025
概括
研究人员开发了一种新的视觉编码方法,使生物神经网络 (BNNs) 能够处理图像. 这种仿生方法显著提高了图像识别准确度,并增强了神经网络连接.
科学领域:
- 神经科学是一个神经科学.
- 机器人技术 机器人技术 机器人技术
- 生物仿真工程 生物仿真工程
背景情况:
- 与机器人系统集成的生物神经网络 (BNNs) 对信息处理和自适应学习有很大的前景.
- 目前的BNN机器人系统由于无效的感官编码而难以进行视觉感知.
- 需要先进的方法来将视觉输入与BNN处理能力相结合.
研究的目的:
- 为BNN提出和验证生物模拟视觉信息的时空编码方法.
- 通过将视觉数据转化为神经刺激,使BNN能够执行图像识别任务.
- 评估编码方法对BNN信息处理和功能连接的影响.
主要方法:
- 开发了一种改进的延迟相位编码方法,以将高维图像转换为脉冲序列.
- 利用卷积,时间延迟,对齐和压缩来准备刺激.
- 通过使用高密度微电极阵列 (HD-MEAs) 在体外BNNs上进行了三阶段的无监督培训.
- 使用逻辑回归模型解码神经活动,以评估图像识别性能.
主要成果:
- 拟议的编码方法为不同的时空刺激在BNN中产生了可分离的触发模式.
- 在三个训练阶段后,图像识别精度达到80.33%±7.94%,比第一阶段提高13.64%.
- 无监督培训导致BNN连接数量,连接强度和模块间参与系数显著增加.
结论:
- 开发的仿生编码方法有效地使BNN能够处理视觉信息以进行图像识别.
- 该方法增强了BNN内部的功能连接性和跨模块信息交换.
- 这种方法代表了将生物神经计算与机器人感知相结合的重大进步.
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