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Steven Pinker -- How the Mind Works - Hampshire High Italian ...

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100 I HOW THE MIND WORKSincoming levels; if <strong>the</strong> total exceeds a threshold, <strong>the</strong> neuron will; becomemore active, sending a signal in turn to any neuron connected to it.Though neurons are always firing and incoming signals merely cause it tofire at a detectably faster or slower rate, it is sometimes convenient todescribe <strong>the</strong>m as being ei<strong>the</strong>r off (resting rate) or on (elevated rate).McCulloch and Pitts showed how <strong>the</strong>se toy neurons could be wiredup to make logic gates. Logic gates implement <strong>the</strong> basic logical relations"and," "or," and "not" that underlie simple inferences. "A and B" (conceptually)is true if A is true and if B is true. An AND-gate (mechanically)produces an output if both of its inputs are on. To make an ANDigate outof toy neurons, set <strong>the</strong> threshold of <strong>the</strong> output unit to be greater thaneach of <strong>the</strong> incoming weights but less than <strong>the</strong>ir sum, as in <strong>the</strong> mini-networkon <strong>the</strong> left below. "A or B" (conceptually) is true if A is true or if B istrue. An OR-gate (mechanically) produces an output if ei<strong>the</strong>r of its inputsis on. To make one, set <strong>the</strong> threshold to be less than each incomingweight, as in <strong>the</strong> middle mini-network below. Finally, "not A" (conceptually)is true if A is false, and vice versa. A NOT-gate (mechanically) producesan output when it receives no input, and vice versa. To make one,set <strong>the</strong> threshold at zero, so <strong>the</strong> neuron will fire when it gets no input,and make <strong>the</strong> incoming weight negative, so that an incoming signal willturn <strong>the</strong> neuron off, as in <strong>the</strong> mini-network on <strong>the</strong> right.6AND OR NOTSuppose that each toy neuron represents a simple proposition. Themini-networks can be wired toge<strong>the</strong>r, with <strong>the</strong> output of one feeding <strong>the</strong>input to ano<strong>the</strong>r, to evaluate <strong>the</strong> truth of a complex proposition. Forexample, a neural network could evaluate <strong>the</strong> proposition {[(X chews itscud) and (X has cloven hooves)] or [(X has fins) and (X has scales)]}, asummary of what it takes for an animal to be kosher. In fact, if a networkof toy neurons is connected to some kind of extendable memory (such asa roll of paper moving under a rubber stamp and an eraser), it would be aTuring machine, a full-powered computer.©-.1l

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