Although the brain-computer metaphor has served cognitive psychology well, research in cognitive neuroscience has revealed many important differences between brains and computers. Appreciating these differences may be crucial to understanding the mechanisms of neural information processing, and ultimately for the creation of artificial intelligence. Below, I review the most important of these differences (and the consequences to cognitive psychology of failing to recognize them): similar ground is covered in this excellent (though lengthy) lecture.
One consequence of this over-simplification is that we are only now learning that „memory“ regions (such as the hippocampus) are also important for imagination, the representation of novel goals, spatial navigation, and other diverse functions
It’s easy to think that neurons are essentially binary, given that they fire an action potential if they reach a certain threshold, and otherwise do not fire. This superficial similarity to digital „1’s and 0’s“ belies a wide variety of continuous and non-linear processes that directly influence neuronal processing.
For example, one of the primary mechanisms of information transmission appears to be the rate at which neurons fire – an essentially continuous variable. Similarly, networks of neurons can fire in relative synchrony or in relative disarray; this coherence affects the strength of the signals received by downstream neurons. Finally, inside each and every neuron is a leaky integrator circuit, composed of a variety of ion channels and continuously fluctuating membrane potentials.
This is known as byte-addressable memory
Failure to recognize these important subtleties ous mischaracterization of perceptrons, a neural network without an intermediate layer between input and output. In linear networks, any function computed by a 3-layer network can also be computed by a suitably rearranged 2-layer network. In other words, combinations of multiple linear functions can be modeled precisely by just a single linear function. Since their simple 2-layer networks could not solve many important problems, Minksy Papert reasoned that that larger networks also could not. In contrast, the computations performed by more realistic (i.e., nonlinear) networks are highly dependent on the number of layers – thus, „perceptrons“ grossly underestimate the computational power of neural networks.
In computers, information in memory is accessed by polling its precise memory address. In contrast, the brain uses content-addressable memory, such that information can be accessed in memory through „spreading activation“ from closely related concepts. For example, thinking of the word „fox“ may automatically spread activation to memories related to other clever animals, fox-hunting horseback riders, or attractive members of the opposite sex.
The end result is that your brain has a kind of „built-in Google,“ in which just a few cues (key words) are enough to cause a full memory to be retrieved. Of course, similar things can be done in computers, mostly by building massive indices of stored data, which then also need to be stored and searched through for the relevant information (incidentally, this is pretty much what Google does, with a few twists).
Although this may seem like a rather minor difference between computers and brains, it has profound effects on neural computation. For example, a lasting debate in cognitive psychology concerned whether information is lost from memory because of simply decay or because of interference from other information. In retrospect, this debate is partially based on the false asssumption that these two possibilities are dissociable, as they can be in computers. Many are now realizing that this debate represents a false dichotomy.
An unfortunate legacy of the brain-computer metaphor is the tendency for cognitive psychologists to seek out modularity in the brain. For example, the idea that computers require memory has lead some to seek for the „memory area,“ when in fact these distinctions are far more messy.