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3.1: Introduction

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    In this chapter, we build upon the Neuron Chapter to understand how networks of detectors can produce emergent behavior that is more than the sum of their simple neural constituents. We focus on the networks of the neocortex ("new cortex", often just referred to as "cortex"), which is the evolutionarily most recent, outer portion of the brain where most of advanced cognitive functions take place. There are three major categories of emergent network phenomena:

    • Categorization of diverse patterns of activity into relevant groups: For example, faces can look very different from one another in terms of their raw "pixel" inputs, but we can categorize these diverse inputs in many different ways, to treat some patterns as more similar than others: male vs. female, young vs. old, happy vs. sad, "my mother" vs. "someone other", etc. Forming these categories is essential for enabling us to make the appropriate behavioral and cognitive responses (approach vs. avoid, borrow money from, etc.). Imagine trying to relate all the raw inputs of a visual image of a face to appropriate behavioral responses, without the benefit of such categories. The relationship ("mapping") between pixels and responses is just too complex. These intermediate, abstract categories organize and simplify cognition, just like file folders organize and simplify documents on your computer. One can argue that much of intelligence amounts to developing and using these abstract categories in the right ways. Biologically, we'll see how successive layers of neural detectors, organized into a hierarchy, enable this kind of increasingly abstract categorization of the world. We will also see that many individual neural detectors at each stage of processing can work together to capture the subtlety and complexity necessary to encode complex conceptual categories, in the form of a distributed representation. These distributed representations are also critical for enabling multiple different ways of categorizing an input to be active at the same time -- e.g., a given face can be simultaneously recognized as female, old, and happy. A great deal of the emergent intelligence of the human brain arises from multiple successive levels of cascading distributed representations, constituting the collective actions of billions of excitatory pyramidal neurons working together in the cortex.
    • Bidirectional excitatory dynamics are produced by the pervasive bidirectional (e.g., bottom-up and top-down or feedforward and feedback) connectivity in the neocortex. The ability of information to flow in all directions throughout the brain is critical for understanding phenomena like our ability to focus on the task at hand and not get distracted by irrelevant incoming stimuli (did my email inbox just beep??), and our ability to resolve ambiguity in inputs by bringing higher-level knowledge to bear on lower-level processing stages. For example, if you are trying to search for a companion in a big crowd of people (e.g., at a sporting event or shopping mall), you can maintain an image of what you are looking for (e.g., a red jacket), which helps to boost the relevant processing in lower-level stages. The overall effects of bidirectional connectivity can be summarized in terms of an attractor dynamic or multiple constraint satisfaction, where the network can start off in a variety of different states of activity, and end up getting "sucked into" a common attractor state, representing a cleaned-up, stable interpretation of a noisy or ambiguous input pattern. Probably the best subjective experience of this attractor dynamic is when viewing an Autostereogram (NOTE: links to Wikipedia for now) -- you just stare at this random-looking pattern with your eyes crossed, until slowly your brain starts to fall into the 3D attractor, and the image slowly emerges. The underlying image contains many individual matches of the random patterns between the two eyes at different lateral offsets -- these are the constraints in the multiple constraint satisfaction problem that eventually work together to cause the 3D image to appear -- this 3D image is the one that best satisfies all those constraints.
    • Inhibitory competition, mediated by specialized inhibitory interneurons is important for providing dynamic regulation of overall network activity, which is especially important when there are positive feedback loops between neurons as in the case of bidirectional connectivity. The existence of epilepsy in the human neocortex indicates that achieving the right balance between inhibition and excitation is difficult -- the brain obtains so many benefits from this bidirectional excitation that it apparently lives right on the edge of controlling it with inhibition. Inhibition gives rise to sparse distributed representations (having a relatively small percentage of neurons active at a time, e.g., 15% or so), which have numerous advantages over distributed representations that have many neurons active at a time. In addition, we'll see in the Learning Chapter that inhibition plays a key role in the learning process, analogous to the Darwinian "survival of the fittest" dynamic, as a result of the competitive dynamic produced by inhibition.

    We begin with a brief overview of the biology of neural networks in the neocortex.

    This page titled 3.1: Introduction is shared under a CC BY-SA license and was authored, remixed, and/or curated by O'Reilly, Munakata, Hazy & Frank.

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