Midbrain Computational and Robotic Auditory Model for focused hearing (MiCRAM)

用于聚焦听力的中脑计算和机器人听觉模型 (MiCRAM)

• 批准号: EP/D060648/1
• 负责人: Adrian Rees
• 金额: $ 19.2万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FD060648%2F1
• 关键词: Midbrain; Computational; Robotic; Auditory; Model

Our aim is to replicate in a computer model the processing that occurs in the auditory midbrain, the inferior colliculus, to analyse sounds. Our hypothesis is that a biologically inspired model of precortical processing, based on the way the brain processes sounds, will be more efficient than existing computational models. One way in which our hearing is superior to computer based sound recognition systems is in its ability to separate and identify sounds in noisy environments; for example when we hold a conversation at a noisy party. There would be great benefits to the quality of life if we could achieve this level of performance with computer interfaces or robotic systems. In particular we intend to demonstrate the utility of our model by adapting it to control a robot that is able to respond to sound stimuli in a noisy environment. In turn, we expect our model to generate predictions about brain function. We aim to do this through a collaboration between a team experts in computing, robotics, auditory processing and brain science.We hear sounds in the world around us when they activate receptors in our ears. These receptors encode sound to electrical impulses that activate a chain of processing centres in the brain and eventually the cortex. The cochlea, the part of the ear where sound is sensed, is shaped like a snail shell. Running down its length is a row of inner hair cells. Each cell is most sensitive to a particular frequency, and these hair cells in turn activate auditory nerve fibres. Because each nerve fibre only responds to a narrow range of sound frequencies and projects in an orderly way to the brain, frequency is represented in a spatially ordered or topographic manner called a tonotopic representation.The information carried by the nerve fibres enters the brain and divides into a number of processing channels that emphasise different aspects of the sound stream. These individual tonotopic representations converge and are processed in the inferior colliculus (IC), which in turn sends outputs to the thalamus and then to the auditory cortex. Evidence suggests that because these streams converge at the IC there is sufficient information available at this level to identify what the sound is and where it comes from. The IC also receives feedback from the cortex. This may provide an expected pattern of sound signals that the inferior colliculus compares with the incoming sound pattern to identify differences. It also may be that this acts in some way to spotlight auditory attention to emphasize some sounds and de-emphasise others.To build our model, the neuroscientists in our team will construct a database of current knowledge about the wiring and connections of the auditory brainstem, and add to this where necessary with new experiments about auditory processing in the brainstem of animals. The modellers will use this information to guide the creation of a computer model that can control the actions of a robot. We will use robots because animals do not just passively listen but actively seek out sounds to build an auditory picture of their world. This active behaviour allows them to put together experimental scenarios that have different results depending on how sound is processed. Working with robots makes it more likely that the results will have practical uses in helping to build better hearing aids, speech understanding systems, sound tracking systems, and sound-controlled robots. We expect that our model will help us to make predictions about how the midbrain functions and we will test these predictions in animal and robot experiments. The experts in modelling sound processing are very interested in testing whether some theories on how the ear and brain handle sound are consistent with what is seen in biology and thus our modelling will help to guide the direction of future research and reduce animal use. The database we develop will be made available to other researchers in the field.

我们的目标是在计算机模型中复制听觉中脑，即下丘，分析声音的过程。我们的假设是，基于大脑处理声音的方式的皮质前处理的生物启发模型将比现有的计算模型更有效。我们的听力优于基于计算机的声音识别系统的一个方面是，它能够在嘈杂的环境中分离和识别声音，例如，当我们在嘈杂的派对上进行对话时。如果我们能够通过计算机接口或机器人系统达到这种水平的性能，将会对生活质量产生巨大的好处。特别是，我们打算通过调整我们的模型来控制能够在嘈杂环境中对声音刺激做出反应的机器人，从而展示该模型的实用性。反过来，我们希望我们的模型能产生关于大脑功能的预测。我们的目标是通过计算机、机器人学、听觉处理和脑科学专家团队的合作来实现这一点。当声音激活我们耳朵中的感受器时，我们就能听到周围世界的声音。这些感受器将声音编码成电脉冲，激活大脑中的一系列处理中心，最终激活大脑皮层。耳蜗是耳朵中感觉到声音的部分，它的形状像蜗牛壳。沿着它的长度是一排内毛细胞。每个细胞对特定的频率最敏感，这些毛细胞反过来激活听神经纤维。因为每个神经纤维只对狭窄的声音频率作出反应，并以有序的方式投射到大脑，所以频率以空间有序或地形图的方式表示，称为强直性表征。神经纤维携带的信息进入大脑，并分成多个处理通道，强调声流的不同方面。这些单独的纯音表示会聚在一起，在下丘(IC)进行处理，然后将输出发送到丘脑，然后发送到听觉皮质。证据表明，因为这些流在IC汇聚，所以在这个水平上有足够的信息可用来识别声音是什么以及它来自哪里。IC还接收来自大脑皮层的反馈。这可以提供预期的声音信号模式，下丘与传入的声音模式进行比较以识别差异。为了建立我们的模型，我们团队中的神经科学家将建立一个关于听觉脑干连接和连接的当前知识数据库，并在必要时增加关于动物脑干听觉处理的新实验。建模师将使用这些信息来指导计算机模型的创建，该模型可以控制机器人的行动。我们将使用机器人，因为动物不仅被动地倾听，而且主动寻找声音来构建它们世界的听觉图景。这种活跃的行为使他们能够根据声音的处理方式，将不同结果的实验场景组合在一起。与机器人合作使其结果更有可能在帮助建造更好的助听器、语音理解系统、声音跟踪系统和声控机器人方面具有实际用途。我们希望我们的模型将帮助我们预测中脑是如何运作的，我们将在动物和机器人实验中测试这些预测。声音处理建模的专家们非常有兴趣测试一些关于耳朵和大脑如何处理声音的理论是否与生物学中看到的一致，因此我们的建模将有助于指导未来的研究方向，并减少动物的使用。我们开发的数据库将提供给该领域的其他研究人员使用。