Computational Cognitive Neuroscience

        How does the brain think? This is one of the most challenging unsolved questions in science. Armed with new methods, data, and ideas, researchers in a variety of fields bring us closer to fully answering this question each day. We can even watch the brain as it thinks, using modern neuroimaging machines that record the biological shadows of thought and transform them into vivid color images. These amazing images, together with the results from many other important techniques, have advanced our understanding of the neural bases of cognition considerably. We can consolidate these various different approaches under the umbrella discipline of cognitive neuroscience, which has as its goal answering this most important of scientific questions.

  Cognitive neuroscience will remain a frontier for many years to come, because both thoughts and brains are incredibly complex and difficult to understand. Sequences of images of the brain thinking reveal a vast network of glowing regions that interact in complex ways with changing patterns of thought. Each picture is worth a thousand words -- indeed, language often fails us in the attempt to capture the richness and subtlety of it all. Computational models based on biological properties of the brain can provide an important tool for understanding all of this complexity. Such models can capture the flow of information from your eyes recording these letters and words, up to the parts of your brain activated by the different word meanings, resulting in an integrated comprehension of this text. Although our understanding of such phenomena is still incomplete, these models enable us to explore their underlying mechanisms, which we can implement on a computer and manipulate, test, and ultimately understand.

  This book provides an introduction to this emerging subdiscipline known as computational cognitive neuroscience: simulating human cognition using biologically based networks of neuronlike units ( neural networks). We provide a textbook-style treatment of the central ideas in this field, integrated with computer simulations that allow readers to undertake their own explorations of the material presented in the text. An important and unique aspect of this book is that the explorations include a number of large-scale simulations used in recent original research projects, giving students and other researchers the opportunity to examine these models up close and in detail.

In this chapter, we present an overview of the basic motivations and history behind computational cognitive neuroscience, followed by an overview of the subsequent chapters covering basic neural computational mechanisms (part I) and cognitive phenomena (part II). Using the neural network models in this book, you will be able to explore a wide range of interesting cognitive phenomena, including:

Visual encoding:

A neural network will view natural scenes (mountains, trees, etc.), and, using some basic principles of learning, will develop ways of encoding these visual scenes much like those your brain uses to make sense of the visual world.

Spatial attention:

By taking advantage of the interactions between two different streams of visual processing, you can see how a model focuses its attention in different locations in space, for example to scan a visual scene. Then, you can use this model to simulate the attention performance of normal and brain-damaged people.

Episodic memory:

By incorporating the structure of the brain area called the hippocampus, a neural network will become able to form new memories of everyday experiences and events, and will simulate human performance on memory tasks.

Working memory:

You will see that specialized biological mechanisms can greatly improve a network's working memory (the kind of memory you need to multiply 42 by 17 in your head, for example). Further, you will see how the skilled control of working memory can be learned through experience.

Word reading:

You can see how a network can learn to read and pronounce nearly 3,000 English words. Like human subjects, this network can pronounce novel nonwords that it has never seen before (e.g., ``mave'' or ``nust''), demonstrating that it is not simply memorizing pronunciations -- instead, it learns the complex web of regularities that govern English pronunciation. And, by damaging a model that captures the many different ways that words are represented in the brain, you can simulate various forms of dyslexia.

Semantic representation:

You can explore a network that has ``read'' every paragraph in this textbook and in the process acquired a surprisingly good understanding of the words used therein, essentially by noting which words tend to be used together or in similar contexts.

Task directed behavior:

You can explore a model of the ``executive'' part of the brain, the prefrontal cortex, and see how it can keep us focused on performing the task at hand while protecting us from getting distracted by other things going on.

Deliberate, explicit cognition:

A surprising number of things occur relatively automatically in your brain (e.g., you are not aware of exactly how you translate these black and white strokes on the page into some sense of what these words are saying), but you can also think and act in a deliberate, explicit fashion. You'll explore a model that exhibits both of these types of cognition within the context of a simple categorization task, and in so doing, provides the beginnings of an account of the biological basis of conscious awareness.