Abstract
This chapter attempts to move from the fundamental computational properties of the brain, previously described, into a sketch of how the brain builds a representation of the world. The next part of the book will deal more specifically with the linguistic portrayal we humans have of the world, a topic which has also served as a tentative approach to explaining the neural mechanisms that allow animals to build knowledge. In a sense, the core question of epistemology. Several philosophers, like Jerry Fodor (1983, 1990), have denied that an explanation of representations can be given in terms of neural biophysical properties. Today, such a radical refusal of the neurocomputational approach has become more and more marginal, in any case its thorough defense is beyond the scope of this book, and left to better advocates (Churchland 2002).
On the other hand, any endeavor toward explaining mental representations by neural mechanisms, has first to acknowledge that the notion of “representation” itself is problematic, and at the heart of current philsophical controversies. In addition, there are also positions entirely within a neuroscientific perspective, that deny the concept of representations completely. The title of this chapter leaves no doubt that we, instead, appeal to the notion of representations. It is out of the scope of this book the attempt to settle the philsophical debate on representations, and to lay down any new theory, our approach is to explore the emergence of semantic phenomena through the use of neurocomputational models based on a set of plausible mechanisms, that will be described in this chapter. Before that, we will offer a short overview of the philsophical issues posed by mental representation in general, with more detail on neural representation, and on neural computation over representations.
We will offer a selection of a small number of mechanisms, deemed to be at the core of the bridge between electrochemical activity and world representation. The ability of detecting coincidences, at different scale levels of neural circuits, is regarded as the most general, and probably most effective mechanism. Other more specific strategies will be discussed such as receptive fields, topological organizations, and selective processing pathways.
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Notes
- 1.
One of the models in this book (Sect. 6.2.3) demonstrates the feasibility of learning from a limited number of experiences, using coincidence detection only.
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Plebe, A., De La Cruz, V.M. (2016). Representational Mechanisms. In: Neurosemantics. Studies in Brain and Mind, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-28552-8_3
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