Neuronal circuits for context-driven bias in auditory categorization

听觉范畴化中语境驱动偏向的神经元模型 在日常生活中，由于感觉信号和神经元反应都是嘈杂的，因此重要的认知任务，如 听觉分类是基于不确定信息的。为了克服这种限制，听众将其他 信号类型，例如短时间尺度和长时间尺度上的声音统计数据以及来自其他感觉模态的信号 他们的分类决策过程。在行为层面上，这样的上下文信号通过转移而使分类产生偏差。 听者的心理测量曲线在神经元水平上，分类需要感觉表征的转换 转化为由这些上下文信号调制的类别成员资格的表示。而分类 已经在皮层中发现了表征，支持这些表征出现的细胞类型和神经元机制 代表性仍然未知。此外，神经元范畴表征的机制是 由上下文信号调制，引起行为偏差，还没有被探索。我们的目标是找出 特定细胞类型对分类的贡献，并了解背景信号如何影响神经元的机制。 偏向听觉分类。 多项研究表明，听觉皮层（AC）和后顶叶皮层（PPC）的神经元 参与了听觉分类。基于AC的良好描述的电路架构，最近的研究，以及我们的 根据初步数据，我们提出了一系列假设，描述了AC内兴奋-抑制回路的作用， 创建和偏置分类刺激表示和PPC-AC投射在驱动源中的作用， 偏置信号。为了验证这些假设，我们在一个两种选择的强迫选择任务中训练小鼠， 的 任务， 协会）。 将“目标”声音的频率分为两个重叠类别之一（“低”或“高”）。当老鼠参与这个过程时， 我们系统地操纵三个偏置信号（短期和长期刺激统计，以及跨模态 这种设计允许我们在贝叶斯框架内构建认知任务， 实验测试的特定神经元细胞类型的功能的计算模型。 行为 活动 类别. 在 听觉 我们将联合收割机 和计算框架与电生理记录和光遗传操纵的神经元 首先，我们将测试AC中不同的神经元细胞类型是否对刺激信息进行差异编码 其次，我们将测试AC中特定的抑制性神经元细胞类型是否以及如何介导上下文依赖 听觉分类第三，我们将测试皮质-皮质反馈是否以及如何介导情境依赖， 分类 与BRAIN计划的目标一致，我们的项目将为大脑皮层提供一个机械框架， 支持复杂行为的电路。这些结果将定量地解决一个重要的悬而未决的问题， 相同或不同的神经元群体整合跨多个时间尺度和跨感觉的信息， 模态，概括或专门化的偏见的表征在分类。

NEURONAL CIRCUITS FOR CONTEXT-DRIVEN BIAS IN AUDITORY CATEGORIZATION In everyday life, because both sensory signals and neuronal responses are noisy, important cognitive tasks, such as auditory categorization, are based on uncertain information. To overcome this limitation, listeners incorporate other types of signals, such as the statistics of sounds over short and long time scales and signals from other sensory modalities into their categorization decision processes. At the behavioral level, such contextual signals bias categorization by shifting the listener's psychometric curve. At the neuronal level, categorization requires a transformation of sensory representation into a representation of category membership that is modulated by these contextual signals. While categorical representations have been found in the cortex, the cell types and neuronal mechanisms supporting the emergence of these representations remains unknown. Furthermore, the mechanisms by which neuronal categorical representations are modulated by contextual signals, giving rise to a behavioral bias, have not been explored. Our goal is to identify the contribution of specific cell types to categorization and to understand the neuronal mechanisms for how contextual signals bias auditory categorization. Multiple studies have demonstrated that neurons in auditory cortex (AC) and the posterior parietal cortex (PPC) are involved in auditory categorization. Based on the well-described circuit architecture of the AC, recent studies, and our preliminary data, we propose a series of hypotheses that delineate the role of excitatory-inhibitory circuits within AC in creating and biasing categorical stimulus representations and for the role of PPC-AC projections in driving the source for the bias signal. To test these hypotheses, we train mice in a two-alternative-forced choice task in which mice categorize the task, associations). frequency of a “target” sound into one of two overlapping categories (“low” or “high”). While mice participate in this we systematically manipulate three bias signals (short-term and long-term stimulus statistics, and cross-modal Thisdesign allows us to frame the cognitive task within a Bayesian framework, which generates formal computational models for the function of specific neuronal cell types that are tested experimentally. behavioral activity. category. in auditory We will combine this and computational framework with electrophysiological recordings and optogenetic manipulations of neuronal First, we will test whether distinct neuronal cell types in AC differentially encode information about stimulus Second, we will test whether and how specific inhibitory neuronal cell types in AC mediate context dependence auditory categorization. Third, we will test whether and how cortico-cortical feedback mediates context dependence in categorization. Aligned with the goals of the BRAIN initiative, our project will deliver a mechanistic framework for a cortical circuit supporting a complex behavior. These results will quantitatively address an important open question to what extent the same or distinct neuronal populations integrate information across multiple temporal scales and across sensory modalities, generalizing or specializing the representation of the bias in categorization.