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Neurons have thousands, sometimes tens of thousands, of synapses,    spaced along the branches of the dendrites.

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Less than 10% of the cell's synapses are in the proximal area near the cell body. The other 90% are too far away to cause a spike.

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A prediction  occurs when a neuron recognizes a pattern [in the ~10,000 synapses in its dendrites],    creates a dendrite spike,    and is primed to spike earlier than other neurons.

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Each neuron can recognize hundreds of patterns that predict when the neuron should become active.

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Prediction is built into the fabric of the neocortex, the neuron.

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We wrote software simulations that tested its capacity and were surprised to find that as few as 20,000 neurons    can learn thousands of complete  sequences

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Sequence memory continued to work    even if 30% of the neurons died or if the input was noisy.

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The function of the cortical column is reference frames.

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Different parts of the body (fingertips, palm, lips) might touch the coffee cup at the same time. The brain isn't making one prediction:; it's making dozens or even hundreds of predictions at the same time.

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The neocortex must know the location, relative to the cup, of every part of my body that is touching it.

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Vision is doing the same thing as touch.    Each patch of your retina    sees only a small part of an entire object.

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The visual cortex assigns each piece    to a location relative to the object being observed.

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Since the complex circuitry in every cortical column is similar, location and reference frames must be universal properties of the neocortex.

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Each column in the  neocortex – whether it represents visual input, tactile input, auditory input, language, or high-level thought – must have neurons that represent reference frames and locations.

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The brain builds models of the world    by associating sensory input with locations and reference frames.

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By defining an object using a reference frame, the brain can manipulate the entire object at once.

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The function of most of the neurons    in each cortical column    is to create reference frames and track locations

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Vernon Mountcastle argued that there was a universal algorithm  that exists in every cortical column, yet he didn't know what the algorithm was.

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Francis Crick wrote that we needed a new framework to understand the brain,    yet he didn't know what the framework should be.

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Mountcastle's algorithm and Crick's framework were both based on reference frames.

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The entire neocortex works by creating reference frames,    with many thousands    active simultaneously.

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Each cortical column must know the location    of its input    relative to the object being sensed.

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Altghough a cortical column is tiny, about 1 mm on a side, each of these layers might have 10,000 neurons.

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Upper layer receives the sensory input to the column. When it arrives, it causes several hundred neurons to become active.

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Bottom layer represents the current location in a reference frame.

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Every cortical column learns models of objects.

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Each cortical column has a set of cells the grid cells,    another set equivalent to place cells,    and another set equivalent to head direction cells,    all of which were first discovered in parts of the old brain.

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There are about 150,000 columns in the human cortex.

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Mountcastle proposed that every column in the neocortex  performs the same basic function.

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Language, and other high-level cognitive abilities, at some fundamental level are the same as seeing,    touching, and hearing.

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Mountcastle deduced that there must be some basic function that underlies everything the neocortex does – not just perception, but all the things we think of as intelligence.

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The reference frame allows a cortical column to learn the locations of features that define the shape of an object.

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We can think of reference frames as a way to organize    any kind of knowledge.

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Cells that create reference frames are similar, but not identical, to the grid cells and place cells    found in older parts of the brain.

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A column is a mechanism built of neurons that blindly tries to discover and model the structure of whatever it is causing its inputs to change.

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Brains once again evolved to use that same mechanism    to learn and represent    the structure underlying  conceptual objects,    such as mathematics and democracy.

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Thinking occurs when the neurons    invoke location after location in a reference frame,    bringing to mind    what was stored in each location.

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Where is knowledge stored in the brain?    Knowledge in the brain is distributed.   Knowledge of something is distributed in thousands of columns,    but they are a small subset of all the columns.

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A column in V2    gets input from a large area of the retina,    but the image is fuzzier.

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A neuron    never depends on a single synapse.    Instead, it might use 30 synapses    to recognize a pattern.

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A network of neurons is never dependent on a single cell.    Even the loss of 30% of the neurons    usually has only a marginal effect on the performance of the network.

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The neocortex is not dependent on a single cortical column.    The brain continues to function    even if a stroke or trauma    wipes out thousands of columns.

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Cells in some layers    send axons long distances within the neocortex.    We propose that the cells with long-distance connections are voting.

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The basic idea of how columns can vote is not complicated. Using its long-range connections,    a column broadcasts what it thinks it is observing.    Often a column will be uncertain,    in which case its neurons will send multiple possibilities at the same time.    Simultaneously, the column receives projections from other columns    representing their guesses. The most common guesses    suppress the least common ones    until the entire network    settles on one answer.

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The voting neurons in each column    form a stable pattern that represents the object    and where it is    relative to you.

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What we perceive    is based on the stable voting neurons.

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The information from these neurons is spread broadly to other areas of the brain,    where it can be turned into language    or stored in short-term memory.

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The number of voting neurons    active at any time is small.

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If you were looking at the neurons responsible for voting,    you might see 98% of the cells being silent    and 2% continuously firing.

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Attention plays an essential role in how the brain learns models.