Dynamical information processing in a neuronal microcircuit

我们的大脑由大量称为神经元的细胞相互连接形成的电路组成。在皮层中，主要的神经元类型是锥体细胞，它是我们神经元网络中的主要信息处理器。锥体细胞被数量较少但更多样化的中间神经元群体包围，这些中间神经元与锥体细胞和它们自身局部形成连接。通过建立详细的大脑电路的计算机模型，我们将探索这些中间神经元的微电路如何控制通过锥体细胞的信息流。特别地，我们将研究中间神经元如何影响锥体细胞是基于先前存储的知识处理信息还是从新的经验中学习。知识储存在神经元之间连接的强度中，学习取决于这些突触（连接）强度的可塑性或可变性。人工神经网络（ANN）领域已经证明，可以使用这种范例来构建信息处理设备。然而，目前的人工神经网络模型使用的电路和细胞类型比我们的大脑简单得多。我们希望进一步了解大脑皮层中复杂微电路的运作，以及它如何动态地控制信息处理和学习。基于我们的发现，新的人工神经网络设计应该会出现，它们在科普现实世界的信息处理方面更加灵活和健壮。我们将尝试模拟大脑的一小部分如何充当智能记忆设备。我们不断受到感官信息的轰炸，其中一些我们记得，其中一些激发了旧记忆的回忆。大脑如何存储和回忆信息的某些方面被捕获在被称为联想记忆的数学ANN模型中，该模型于40多年前首次开发。这些模型的工作原理是通过简单计算单元之间连接强度的变化来存储信息模式，这些计算单元以非常简单的方式模拟大脑中神经元的操作。当先前存储的模式的噪声或部分版本呈现给网络时，旧的记忆被召回。这些设备不是很灵活。他们必须被告知什么时候储存一个模式，什么时候应该回忆一个记忆。它们能储存的信息种类相当有限。我们的目标是建立一个更灵活的模型，可以控制自己的存储和召回模式的信息到达不可预测的速度。这个数学模型将基于我们现在所知道的海马体神经元回路的许多细节，海马体是哺乳动物大脑中充当短期记忆的一部分。该模型将在计算机软件中实现，并通过模拟记忆中的存储和回忆来进行测试。通过建立这个模型，我们希望获得对许多不同类型的神经元及其形成的复杂电路实际工作的基本见解。在整个皮层中发现了非常相似的神经元类型和回路，因此我们所学到的应该增加我们对整个大脑信息处理的理解，而不仅仅是海马体中的记忆形成。该模型应该提供相关的见解，了解和治疗神经退行性疾病，涉及记忆障碍，如阿尔茨海默氏症和各种形式的痴呆症。这项工作也应该是有价值的领域的移动的机器人的目标是建立自主的，移动的机器，必须与动态环境的互动，以同样的方式，动物做。它可以导致制定基于神经网络的动态记忆模型，适合纳入这样的机器人。

Our brains consist of electrical circuits formed by the interconnection of vast numbers of cells called neurons. In the cortex the dominant neuronal type is the pyramidal cell, which is the main information processor in our neuronal networks. The pyramidal cells are surrounded by a smaller number but much more diverse population of interneurons that form connections locally with pyramidal cells and themselves. By building detailed computer models of brain circuits we will explore how these microcircuits of interneurons control the flow of information through pyramidal cells. In particular we will investigate how the interneurons can influence whether the pyramidal cells are processing information on the basis of previously stored knowledge or are learning from new experiences. Knowledge is stored in the strengths of connections between neurons and learning depends on how plastic or changeable these synaptic (connection) strengths are. The field of artificial neural networks (ANNs) has demonstrated that information processing devices can be built using this paradigm. However, current ANN models use much simpler circuitry and cell types than our brains. We hope to further our understanding of the operation of the complex microcircuitry found in cortex and how it acts to dynamically control information processing and learning. Based on what we discover new designs for ANNs should emerge that are much more flexible and robust in being able to cope with real-world information processing.We will attempt to model how a small section of the brain can act as an intelligent memory device. We are continually bombarded with sensory information, some of which we remember and some of which sparks the recall of old memories. Some aspects of how the brain may store and recall information are captured in mathematical ANN models known as associative memories, which were first developed over 40 years ago. These models work by storing patterns of information via changes in the strengths of connections between simple computing units that mimic the operation of neurons in the brain in a very simple way. Old memories are recalled when a noisy or partial version of a previously stored pattern is presented to the network. These devices are not very flexible. They must be told when to store a pattern and when they are supposed to recall a memory. The type of information they can store is quite limited. We aim to build a much more flexible model that can control for itself the storage and recall of patterns of information arriving at unpredictable rates. This mathematical model will be based upon the many details we now know of the neuronal circuitry of the hippocampus, a part of the mammalian brain that acts as a short-term memory. The model will be implemented in computer software and tested by running simulations of storage and recall in the memory.By building this model we hope to gain fundamental insights into how the many different types of neurons, and the complex circuits they form, actually work. Very similar neuronal types and circuits are found throughout the cortex, so what we learn should increase our understanding of information processing throughout the brain, not just for memory formation in the hippocampus. The model should provide insights of relevance to the understanding of, and therapies for neurodegenerative diseases that involve memory impairment, such as Alzheimer's disease and various forms of dementia.The work should also be of value to the field of mobile robotics where the aim is to build autonomous, mobile machines that must interact with a dynamic environment, in the same way that animals do. It could lead to the formulation of neural network-based dynamic memory models suitable for incorporation into such robots.