Is a Brain a Computer or a Connectivity Device that can be Modelled by Computer

Computationalism vs. Connectionism.

The Shape of the Controversy between Connectionists and Classicists

The last forty years have been dominated by the classical view that (at least higher) human cognition is analogous to symbolic computation in digital computers. On the classical account, information is represented by strings of symbols, just as we represent data in computer memory or on pieces of paper. The connectionist claims, on the other hand, that information is stored non-symbolically in the weights, or connection strengths, between the units of a neural net. The classicist believes that cognition resembles digital processing, where strings are produced in sequence according to the instructions of a (symbolic) program. The connectionist views mental processing as the dynamic and graded evolution of activity in a neural net, each unit’s activation depending on the connection strengths and activity of its neighbors.

On the face of it, these views seem very different. However many connectionists do not view their work as a challenge to classicism and some overtly support the classical picture. So-called implementational connectionists seek an accommodation between the two paradigms. They hold that the brain’s net implements a symbolic processor. True, the mind is a neural net; but it is also a symbolic processor at a higher and more abstract level of description. So the role for connectionist research according to the implementationalist is to discover how the machinery needed for symbolic processing can be forged from neural network materials, so that classical processing can be reduced to the neural network account.

However, many connectionists resist the implementational point of view. Such radical connectionists claim that symbolic processing was a bad guess about how the mind works. They complain that classical theory does a poor job of explaining graceful degradation of function, holistic representation of data, spontaneous generalization, appreciation of context, and many other features of human intelligence which are captured in their models. The failure of classical programming to match the flexibility and efficiency of human cognition is by their lights a symptom of the need for a new paradigm in cognitive science. So radical connectionists would eliminate symbolic processing from cognitive science forever.

The controversy between radical and implementational connectionists is complicated by the invention of what are called hybrid connectionist architectures. Here elements of classical symbolic processing are included in neural nets (Wermter & Sun 2000). For example, Miikkulainen (1993) champions a complex collection of neural net modules that share data coded in activation patterns. Since one of the modules acts as a memory, the system taken as a whole resembles a classical processor with separate mechanisms for storing and operating on digital “words”. Smolensky (1990) is famous for inventing so called tensor product methods for simulating the process of variable binding, where symbolic information is stored at and retrieved from known “locations”. More recently, Eliasmith (2013) has proposed complex and massive architectures that use what are called semantic pointers, which exhibit features of classical variable binding. Once hybrid architectures such as these are on the table, it becomes more difficult to classify a given connectionist model as radical or merely implementational. This opens the interesting prospect that whether symbolic processing is actually present in the human brain may turn out to be a matter of degree.
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From Searle 1990:

"The point is not that the claim "The brain is a digital computer" is false.Rather it does not get up to the level of falsehood.It does not have a clear sense."

'The upshot of this part of the discussion is that in the sense of "information" used in cognitive science, itis simply false to say that the brain is an information processing device.'