Neural coding of visual inputs in the human medial temporal lobe

由视网膜捕获的图像由神经元传输到枕叶中的初级视觉区域。经过几个处理阶段，这些信息被传输到下颞叶皮层，并从那里投射到内侧颞叶（MTL）。正是从这里，PI与加州大学洛杉矶分校医学中心的伊扎克·弗里德实验室合作，记录了单个神经元对视觉刺激的反应。这些记录来自患有癫痫的人类患者，其被植入颅内电极，以确定癫痫发作病灶，用于可能的手术切除。最近已经显示，大比例的MTL神经元对名人、熟悉的个人、地标和动物的图片作出反应（Quian基罗加等人，Nature 435：1102-1107; 2005）。这种反应是高度选择性的，因为每个神经元只对很少的图片做出反应。相反，分布式代码中的信息是隐含的特定分布在大群体的细胞中的发射活动，这些数据是一致的MTL中存在一个明确的表示。换句话说，个体或物体的身份是由少量的神经元来表示的，而这些神经元中的每一个都“明确地”告诉我们所看到的是什么。一群神经元代表了多少信息，可以通过从所讨论的神经元的反应模式中重建刺激来客观地量化。这种重建或解码提供了一种有价值的方法来理解大脑如何提取特征并破译神经元群体活动中编码的信息。鉴于在MTL神经元中发现的显式表示，为了进一步了解感知如何在MTL中表示，我们计划研究是否有可能在每次试验中预测同时记录的MTL神经元的活动显示的图片。这样的研究直接解决了更普遍的问题，即大脑中的神经元如何代表人类的视觉信息。除了进行常规分析外，还提出开发新的方法来分析这些数据。在植入电极附近的神经元的活动反映在尖峰上。每个电极检测周围所有神经元的尖峰，每个神经元发出给定形状的尖峰。为了理解这些单位如何在大脑中编码信息，必须有一种可靠的方法来检测和分类尖峰（即根据它们的形状将尖峰从不同的神经元中分离出来）。PI已经开发了一种用于检测和分类尖峰的自动方法（Quian基罗加等人，神经计算，16：1661-1687; 2004），并且计划进一步改进该算法。特别地，通过更精细地调整参数，可以以自动方式获得更多的神经元。此外，进一步优化算法可以使其运行速度更快，甚至在线。这些改进将对其他动物神经生理学实验室有所帮助。特别是，这对于自动分类是强制性的其他应用非常重要，例如脑机接口和神经假体（即直接由神经信号引导的假体设备）的开发。从神经元的活动预测视觉输入的可能性还取决于用于解码的算法以及这些算法读出的用于进行预测的信息类型。在这方面，重要的是要研究不同的解码算法的性能，也要研究神经元的反应是最重要的解码功能。

Images captured by the retina are transmitted by neurons to primary visual areas in the occipital lobe. After several processing stages, this information is transmitted to the inferior temporal cortex and from there it projects to the medial temporal lobe (MTL). It is from here that the PI has been recording individual neurons responding to visual stimuli in collaboration with the laboratory of Itzhak Fried at the UCLA medical center. These recordings are from human patients suffering from epilepsy, which are implanted with intracranial electrodes in order to determine the seizure focus for possible surgical resection. Very recently it has been shown that a large proportion of MTL neurons respond to pictures of celebrities, familiar individuals, landmarks, and animals (Quian Quiroga et al., Nature 435: 1102-1107; 2005). Such responses were highly selective in the sense that each of these neurons responded only to very few pictures. In contrast to distributed codes in which information is represented implicitly by the specific distribution of firing activity in large populations of cells, these data are in agreement with the existence of an explicit representation in MTL. In other words, the identity of individuals or objects is represented by a small number of neurons and each of these neurons is 'explicitly' telling the identity of what is being seen.How much information is represented by a population of neurons can be quantified in an objective manner by reconstructing the stimulus from the patterns of responses of the neurons in question. Such reconstruction, or decoding, offers a valuable approach to understand how the brain extracts features and deciphers information encoded in the activity of population of neurons. Given the explicit representation found in MTL neurons, in order to get further insights on how percepts are represented in MTL, we plan to study whether it is possible to predict in each trial which picture was shown from the activity of simultaneously recorded MTL neurons. Such a study addresses directly the more general issue of how neurons in the brain represent visual information in humans. Besides performing conventional analysis, it is proposed to develop new methods to analyze this data. The activity of neurons in the vicinity of the implanted electrodes is reflected in spikes. Each electrode detects the spikes of all neurons in its surroundings and each neuron fires spikes of a given shape. In order to understand how these units encode information in the brain, it is mandatory to have a reliable way of detecting and sorting the spikes (i.e. separating the spikes from the different neurons based on their shapes). An automatic method has been developed by the PI for detection and sorting of spikes (Quian Quiroga et al, Neural Computation, 16: 1661-1687; 2004) and it is planned to further improve this algorithm. In particular, with finer tuning of parameters it may be possible to obtain more neurons in an automatic way. Moreover, further optimization of the algorithm can make it run faster and even on-line. It is expected that these improvements will be useful to other animal neurophysiology laboratories. In particular, this is important for other applications were automatic sorting is mandatory, such as the development of Brain Machine Interfaces and Neural Prosthesis (i.e. prosthetic devices guided directly by neural signals). The possibility of predicting visual inputs from the activity of neurons also depends on the algorithm used for decoding and what type of information is read-out by these algorithms for doing the predictions. In this respect, it is important to study the performance of different decoding algorithms and also to study what features of the neuronal responses are the most important for decoding.