The topological realization

Abstract

In this paper, I argue that the newly developed network approach in neuroscience and biology provides a basis for formulating a unique type of realization, which I call topological realization. Some of its features and its relation to one of the dominant paradigms of realization and explanation in sciences, i.e. the mechanistic one, are already being discussed in the literature. But the detailed features of topological realization, its explanatory power and its relation to another prominent view of realization, namely the semantic one, have not yet been discussed. I argue that topological realization is distinct from mechanistic and semantic ones because the realization base in this framework is not based on local realisers, regardless of the scale (because the local vs global distinction can be applied at any scale) but on global realizers. In mechanistic approach, the realization base is always at the local level, in both ontic (Craver 2007(Craver , 2013) and representational accounts (Bechtel and Richardson 2010). The explanatory power of realization relation in mechanistic approach comes directly from the realization relation-either by showing how a model is mapped onto a mechanism, or by describing some ontic relations that are explanatory in themselves. Similarly, the semantic approach requires that concepts at different scales logically satisfy microphysical descriptions, which are at the local level. In topological framework the realization base can be found at different scales, but whatever the scale the realization base is global, within that scale, and not local. Furthermore, topological realization enables us to answer the "why" questions, which according to Polger (2010) make it explanatory. The explanatoriness of topological realization stems from understanding mathematical consequences of different topologies, not from the mere fact that a system realizes them.

Models and mechanisms in network neuroscience

“…In particular, Silberstein & Chemero (2013) argue that dynamical models in network neuroscience yield covering law explanations rather than mechanistic explanations. Other commentators argue that network models deliver "topological" or other kinds of mathematical explanations (Huneman 2010;Kostić 2016;Rathkopf 2015;Woodward 2013). Although some commentators question the legitimacy of these nonmechanistic explanations (Craver 2016;Kaplan and Craver 2011), the present contribution takes no stance on whether such putative explanations are genuinely explanatory, nor on whether models in only a mechanistic construal of network neuroscience, but also an improved understanding of the way in which sophisticated mathematical and computational methods can be used to deliver mechanistic explanations quite generally.…”

Network explanations and explanatory directionality

“…Craver [6, p. 707] stresses this challenge (taking it to push against recognizing network explanations as a type of non-causal explanation). 2 The problem of directionality and the puzzle of correlational networks signal that, at least in many cases, the explanatory power of network models derives from their ability to represent how phenomena are situated, etiologically and constitutively, in the causal and constitutive structures of our complex world. This article presents an alternative way of addressing the problem of directionality.…”

Evolving Concepts of “Hierarchy” in Systems Neuroscience

“…Several authors have stressed the distinctness of these forms of explanation and debated the relationship between them. In particular, those who think that topological explanations are entirely distinct from mechanistic ones tend to stress their abstraction (Huneman 2010), or the fact that they describe global properties of systems, rather than local causal interactions (Kostić 2016, Rathkopf, 2018.…”