Abstract
This paper offers a novel view of unity in neuroscience. I set out by discussing problems with the classical account of unity-by-reduction, due to Oppenheim and Putnam. That view relies on a strong notion of levels, which has substantial problems. A more recent alternative, the mechanistic “mosaic” view due to Craver, does not have such problems. But I argue that the mosaic ideal of unity is too minimal, and we should, if possible, aspire for more. Relying on a number of recent works in theoretical neuroscience—network motifs, canonical neural computations (CNCs) and design-principles—I then present my alternative: a “flat” view of unity, i.e. one that is not based on levels. Instead, it treats unity as attained via the identification of recurrent explanatory patterns, under which a range of neuroscientific phenomena are subsumed. I develop this view by recourse to a causal conception of explanation, and distinguish it from Kitcher’s view of explanatory unification and related ideas. Such a view of unity is suitably ambitious, I suggest, and has empirical plausibility. It is fit to serve as an appropriate working hypothesis for 21st century neuroscience.
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Notes
Oppenheim and Putnam do address the worry that “some things do not belong at any level” (Ibid, p. 11). But the case they discuss (“a man in a phone booth”) is, by their own lights, uninteresting from a scientific view-point and they thus conclude that “the problem posed by such [cases] is not serious...” (Ibid). The problems Kim alludes to are, however, interesting and serious.
For a vivid example of this way of thinking about levels—see the image in Churchland and Sejnowski (1988, p. 16).
A related argument is made by Potochnick and McGill (2012, p. 131) with respect to pheromones.
I add the qualifier ‘direct’ for in a sufficiently broad and indirect sense, almost anything interacts with anything else. But that would not make for a useful notion of levels.
For more on action potentials and the mechanistic outlook see (Levy 2014).
Several authors with mechanist leaning have developed views of the relation between different parts of science in terms of theoretical integration. See for instance Darden and Maull (1977), Bechtel (1984), and Craver and Darden (2013, Ch. 10). These views are kindred spirits to Craver’s, as he himself notes. But as my discussion is geared towards understanding unity within a domain, namely neuroscience, and as I do not presuppose that what holds for neuroscience holds in other cases, or vice versa, I will not discuss inter-field integration here.
The mosaic can, in principle, encompass synergies of a non-evidential sort—for instance by mutually supportive explanations (where one field explains one aspect of a process or system, another field a different aspect), synergistic experimental techniques (e.g. recording from a cell while a subject performs a behavioral task) and so on. These kinds of aspects are emphasized in (Darden and Maull 1977) and in (Craver and Darden 2013)
PR involves a change in the period of an oscillatory pattern of firing within a neuronal population, resulting in the amplification or dampening of further incoming stimuli, depending on where in the (reset) phase they “hit”.
The references to design, here and in the book’s title, are not incidental. Sterling and Laughlin believe that many of the principles stem from the design-like character of natural selection, and appeal to many ideas from electrical engineering, computer science and related areas in the course of the book. In this respect too there are similarities between this approach and the motifs and CNC cases discussed earlier, in which the notion of design plays a central motivating role. But a discussion of this interesting connection won’t be possible here.
This is not to say they are mechanistic explanations—a matter which is controversial, especially with regards to CNCs (Chirimuuta 2014). But this will not make a difference here.
As noted in footnote 10, part of the motivation for work on motifs, CNCs, and design principles such Sparsify is the thought that they are subject to constraints akin to those under which engineered (manmade) devices are made. From this point of view, the prospect of a unified account of neural systems and, say, the internet is not problematic. It is a prediction of such approaches and its confirmation supports it.
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Levy, A. The unity of neuroscience: a flat view. Synthese 193, 3843–3863 (2016). https://doi.org/10.1007/s11229-016-1256-0
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DOI: https://doi.org/10.1007/s11229-016-1256-0