How does the brain solve the pattern recognition problem?

摘要 对于人脑来说，识别自然界中复杂模式的能力通常是毫不费力的。例如, 健康的人可以很容易地识别面孔、复杂的气味混合物和味道，以及单词和句子， 即使图案被噪声破坏或出现在不同的环境中。模式识别不仅仅是 它对于与环境交流和互动是必不可少的，也是形成记忆的关键。然而， 其中涉及的潜在机制仍然是个谜。我们还没有一个完整的解决方案来解决如何 大脑(任何模型系统，无论大小)解决了这个问题，并通过编程计算机来完成 人类能够进行模式识别的壮举仍然是一个活跃的研究领域。这呈现了一种 治疗具有各种模式识别缺陷的大量个体的主要障碍(例如， 患有中枢听觉处理障碍、视觉失认、自闭症谱系障碍的患者，各种 神经退行性疾病或最近的中风)。在这里，我们建议在大脑中找到这个问题的解决方案 能够进行模式识别，但神经元数量比大多数哺乳动物的大脑少几个数量级。我的 实验室最近使用定量行为分析、计算模型和神经 电路操作，苍蝇可以产生和检测动态声学模式，这些模式在多个 时间尺度。此外，我们有独一无二的开创性方法来从功能上表征 果蝇的声音通讯系统，从感觉输入一直到运动输出。在基础上建设 对于这些成果，我们现在提出了一种从全套输入输出中记录的策略 神经网络的神经元(S)在这个模型系统中进行声学模式识别，并用于映射 潜在的联系。要做到这一点，我们重点测试两个重要的假设(假设跨越模型 系统)了解大脑如何完成歌曲模式识别。第一批实验验证了这一假设 听觉通路中的兴奋和抑制的精确平衡，最终产生稀疏和 对于时间特征选择性和歌曲模式识别来说，选择性响应是必需的。第二 实验验证了歌曲模式识别依赖于模板匹配或神经网络的假设 它将传入的听觉信号与特定模式的内部表示进行比较。终极的 这项研究的目标是启发设计简单的(基于少数神经元的)神经假体装置 恢复或补充在疾病或损伤中丧失的大脑功能。因为苍蝇歌声中的模式和人类 语音在相似的时间尺度上有所不同，果蝇识别歌曲模式的神经计算应该 对于解决更复杂系统中的模式识别问题具有参考价值。更广泛地说，我们的结果将 有助于更深入地了解神经系统如何处理听觉和物种特有的 信息，并有可能改变我们对神经系统如何产生感觉的理解- 受驱动的行为。

Abstract The ability to recognize complex patterns in nature is typically effortless for the human brain. For example, healthy humans can easily recognize faces, complex odor mixtures and tastes, and words and sentences, even when the patterns are corrupted by noise or occur in different contexts. Pattern recognition is not only essential for communication and interacting with the environment, it is also key to memory formation. However, the underlying mechanisms involved remain mysterious. We do not yet have a complete solution for how any brain (of any model system, large or small) solves this problem, and programming a computer to accomplish the feats of pattern recognition that humans are capable of is still an active area of research. This presents a major roadblock towards treating the large number of individuals with various pattern recognition deficits (e.g., patients suffering from central auditory processing disorder, visual agnosia, autism spectrum disorder, various neurodegenerative diseases, or a recent stroke). Here we propose to find a solution to this problem in a brain capable of pattern recognition, but with orders of magnitude fewer neurons than most mammalian brains. My lab has recently demonstrated, using quantitative behavioral assays, computational modeling, and neural circuit manipulations, that flies can both produce and detect dynamic acoustic patterns that vary over multiple timescales. Moreover, we have uniquely pioneered methods to functionally characterize neurons of the acoustic communication system of Drosophila, from sensory inputs all the way to motor outputs. Building on these achievements, we now propose a strategy for recording from the complete set of input and output neurons of the network(s) underlying acoustic pattern recognition in this model system, and for mapping the underlying connections. To do this, we focus on testing two prominent hypotheses (posited across model systems) for how the brain accomplishes song pattern recognition. The first experiments test the hypothesis that a precise balance of excitation and inhibition within the auditory pathway, ultimately generating sparse and selective responses, is required for temporal feature selectivity and song pattern recognition. The second experiments test the hypothesis that song pattern recognition relies on template matching, or a neural network that compares the incoming auditory signal to an internal representation of a particular pattern. The ultimate goal of this line of research is to inspire the design of simple (based on few neurons) neural prosthetic devices to restore or supplement brain function lost during disease or injury. Because patterns in fly song and human speech vary over similar timescales, neural computations for recognizing song patterns in Drosophila should be informative for solving pattern recognition in more complex systems. More broadly, our results will contribute to a deeper understanding of how nervous systems process auditory and species-specific information, and have the potential to transform our understanding of how nervous systems produce sensory- driven behaviors.