Explanation in Computational Neuroscience: Causal and Non-causal

Chirimuuta, Mazviita

doi:10.1093/bjps/axw034

Cited by 46 publications

(25 citation statements)

References 51 publications

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“…An immediate caveat is in order. Algorithms are mathematical, "medium independent" (Elber-Dorozko and Shagrir 2019) posits, and themselves are not individuated in terms of contents, which generally involve referential relations to the environment (Chirimuuta 2017;Egan 2010Egan , 2014a. As such, AH is often conjoined with an attempt to naturalize content in one of the ways that philosophers have traditionally analyzed-e.g., informational or teleological views.…”

Section: Setupmentioning

confidence: 99%

Contents, vehicles, and complex data analysis in neuroscience

Burnston

2020

Synthese

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The notion of representation in neuroscience has largely been predicated on localizing the components of computational processes that explain cognitive function. On this view, which I call "algorithmic homuncularism," individual, spatially and temporally distinct parts of the brain serve as vehicles for distinct contents, and the causal relationships between them implement the transformations specified by an algorithm. This view has a widespread influence in philosophy and cognitive neuroscience, and has recently been ably articulated and defended by Shea (2018). Still, I am skeptical about algorithmic homuncularism, and I argue against it by focusing on recent methods for complex data analysis in systems neuroscience. I claim that analyses such as principle components analysis and linear discriminant analysis prevent individuating vehicles as algorithmic homuncularism recommends. Rather, each individual part contributes to a global state space, trajectories of which vary with important task parameters. I argue that, while homuncularism is false, this view still supports a kind of "vehicle realism," and I apply this view to debates about the explanatory role of representation.

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Section: Setupmentioning

confidence: 99%

Contents, vehicles, and complex data analysis in neuroscience

Burnston

2020

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“…A backlash against “mechanistic imperialism” has begun highlighting a role for functional, fictional, noncausal, and mathematical explanations in the life sciences (Weiskopf, ) and indeed in computational neuroscience more specifically (Chirimuuta, ). These authors are likely to think that the attempt to force DCNN‐based explanations into a mechanistic mold is a mistake, emphasizing instead the computational or mathematical principles which could be implemented in a variety of very different mechanisms.…”

Section: Open Questions Future Research Directions and Live Debatesmentioning

confidence: 99%

Deep learning: A philosophical introduction

Buckner

2019

Philosophy Compass

108

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Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally accepted explanation as to why they work so well. This article provides an introduction to these networks as well as an opinionated guidebook on the philosophical significance of their structure and achievements. It argues that deep learning neural networks differ importantly in their structure and mathematical properties from the shallower neural networks that were the subject of so much philosophical reflection in the 1980s and 1990s. The article then explores several different explanations for their success and ends by proposing three areas of inquiry that would benefit from future engagement by philosophers of mind and science.

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“…Free-energy theorists may simply reject the idea that adequate scientific representation of life science phenomena must target the component parts and operations and internal organization of mechanisms. Exemplifying this rejection is Chirimuuta's (2018) contention that several explanatorily adequate models in computational neuroscience are non-mechanistic. The models she considers would be instances of "efficient coding explanation," which, abstracting away from biophysical specifics, answer why certain neuronal systems should behave in the ways described by the models.…”

Section: How To Explain Life Phenomena?mentioning

confidence: 99%

First principles in the life sciences: the free-energy principle, organicism, and mechanism

Colombo

Wright

2018

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The free-energy principle states that all systems that minimize their free energy resist a tendency to physical disintegration. Originally proposed to account for perception, learning, and action, the free-energy principle has been applied to the evolution, development, morphology, anatomy and function of the brain, and has been called a postulate, an unfalsifiable principle, a natural law, and an imperative. While it might afford a theoretical foundation for understanding the relationship between environment, life, and mind, its epistemic status is unclear. Also unclear is how the free-energy principle relates to prominent theoretical approaches to life science phenomena, such as organicism and mechanism. This paper clarifies both issues, and identifies limits and prospects for the free-energy principle as a first principle in the life sciences. Keywords Adaptation • Free energy • Life • Mechanism • Organicism 1 Introduction According to the free-energy principle (FEP), all systems that minimize their free energy resist a tendency to physical disintegration. Originally proposed to explain how sensory cortex infers the causes of its inputs and learns causal regularities, FEP has been used to elucidate the function of action, perception, and attention, and to account for organisms' evolution and development (

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Explanation in Computational Neuroscience: Causal and Non-causal

Cited by 46 publications

References 51 publications

Contents, vehicles, and complex data analysis in neuroscience

Contents, vehicles, and complex data analysis in neuroscience

Deep learning: A philosophical introduction

First principles in the life sciences: the free-energy principle, organicism, and mechanism

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