Modeling attention-driven plasticity in auditory cortical receptive fields.
Modeling attention-driven plasticity in auditory cortical receptive fields.
复制标题
DOI：
10.3389/fncom.2015.00106
复制
发表时间：
2015
影响因子：
3.2
通讯作者：
Elhilali M
中科院分区：
文献类型：
--
作者：
Carlin MA;Elhilali M
为了在复杂的声学环境中导航，听者调整神经过程以专注于声学前景中的行为相关声音，同时最大限度地减少背景中干扰物的影响，这种能力被称为自上而下的选择性注意。特别引人注目的例子注意力驱动的可塑性已被报道在初级听觉皮层通过动态重塑的频谱-时间感受野（STRFs）。通过增强对前景特征的神经响应，同时抑制对背景的神经响应，STRF可以充当自适应对比度匹配滤波器，其直接有助于改善行为相关和不相关声音之间的认知隔离。在这项研究中，我们提出了一个新的歧视性框架建模注意力驱动的可塑性的STRFs在初级听觉皮层。该模型描述了一个一般的策略，通过优化，最大限度地提高前景和干扰之间的可辨别性，同时保持一定程度的稳定性，在皮层表示的皮层可塑性。该模型的第一个实例描述了一种形式的基于特征的注意力，并产生STRF适应模式与神经生理学研究中先前报道的对比度匹配滤波器一致。该模型的扩展捕获了一种形式的基于对象的注意力，其中自上而下的信号作用于调制域中表征的感觉输入的抽象表示。基于对象的模型，使明确的预测符合有限的神经生理学数据目前可用，但可以很容易地进行实验评估。最后，我们绘制的模型和解剖电路之间的相似之处，据报道，在积极的注意。所提出的模型强烈表明，将注意力驱动的可塑性解释为在感觉皮质水平上操作的区分性适应，这与之前在不同感觉方式中描述的类似策略一致。
To navigate complex acoustic environments, listeners adapt neural processes to focus on behaviorally relevant sounds in the acoustic foreground while minimizing the impact of distractors in the background, an ability referred to as top-down selective attention. Particularly striking examples of attention-driven plasticity have been reported in primary auditory cortex via dynamic reshaping of spectro-temporal receptive fields (STRFs). By enhancing the neural response to features of the foreground while suppressing those to the background, STRFs can act as adaptive contrast matched filters that directly contribute to an improved cognitive segregation between behaviorally relevant and irrelevant sounds. In this study, we propose a novel discriminative framework for modeling attention-driven plasticity of STRFs in primary auditory cortex. The model describes a general strategy for cortical plasticity via an optimization that maximizes discriminability between the foreground and distractors while maintaining a degree of stability in the cortical representation. The first instantiation of the model describes a form of feature-based attention and yields STRF adaptation patterns consistent with a contrast matched filter previously reported in neurophysiological studies. An extension of the model captures a form of object-based attention, where top-down signals act on an abstracted representation of the sensory input characterized in the modulation domain. The object-based model makes explicit predictions in line with limited neurophysiological data currently available but can be readily evaluated experimentally. Finally, we draw parallels between the model and anatomical circuits reported to be engaged during active attention. The proposed model strongly suggests an interpretation of attention-driven plasticity as a discriminative adaptation operating at the level of sensory cortex, in line with similar strategies previously described across different sensory modalities.