Philosophers often say that classical computation involves “rule-governed symbol manipulation” while neural network computation is non-symbolic. The intuitive picture is that “information” in neural networks is globally distributed across the weights and activations, rather than concentrated in localized symbols. However, the notion of “symbol” itself requires explication, so it is often unclear what theorists mean by describing computation as symbolic versus non-symbolic. As mentioned in §1, the Turing formalism places very few conditions on “symbols”. Regarding primitive symbols, Turing assumes just that there are finitely many of them and that they can be inscribed in read/write memory locations. Neural networks can also manipulate symbols satisfying these two conditions: as just noted, one can implement a Turing-style model in a neural network.