Network Modularity as a Foundation for Neural Reuse

Stanley, Matthew L.; Gessell, Bryce; Brigard, Felipe De

doi:10.1086/701037

Cited by 12 publications

(9 citation statements)

References 63 publications

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“…Put differently, it might be that if we go up a level we may get more of the same, now with brain networks representing complex combinations of information in a way that evolves dynamically towards a choice (Cisek and Thura 2018;cf. Anderson 2014;Burnston, forthcoming;Stanley et al 2019). If so, then AH will not describe between-area processing either.…”

Section: Ah Reconsideredmentioning

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: Ah Reconsideredmentioning

confidence: 99%

Contents, vehicles, and complex data analysis in neuroscience

Burnston

2020

Synthese

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“…While these previous studies have provided valuable information of what regions play compensatory roles in aging, recent developments in network neuroscience may bring further insights into how compensation is achieved. There has been growing consensus that although many cognitive functions can be attributed to specific 'core' brain regions, they are in fact carried out collaboratively by many different regions (Bressler and Menon 2010;Sporns 2014;Medaglia et al 2015;Stanley et al 2019). Episodic memory retrieval, for example, is associated with the activation of many disparate brain regions, which play different but complementary roles, such as storing and indexing memory traces, and evaluating the quality of retrieval (Cabeza and Nyberg 2000;Wagner et al 2005;Cabeza 2008;Spaniol et al 2009;Huijbers et al 2011;Rugg and Vilberg 2013), and is also supported by increased connectivity across the network (Geib, Stanley, Dennis, et al 2017;Geib, Stanley, Wing, et al 2017;Westphal et al 2017).…”

Section: Retrieval Performance In Oa Supported By Pfc Integrationmentioning

confidence: 99%

“…That is to say, we focused on whether and to what extent the global connectivity patterns of PFC nodes in the high memory state display dissimilarity with the global connectivity patterns in the low memory state. Converging lines of evidence suggest that transient, dynamic changes in patterns of functional connectivity are crucial for supporting cognitive functions across task domains and demands (Cole et al 2013;Cole et al 2014;Spielberg et al 2015;Simony et al 2016;Gallen et al 2016;Geib, Stanley, Dennis, et al 2017;Geib, Stanley, Wing, et al 2017;Davis et al 2018;Stanley et al 2019). The ability for PFC regions to flexibly reconfigure their functional connections is thought to subserve control-related functions (Cole et al 2013), including those that aid in memory retrieval (Monge et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Age-related compensatory reconfiguration of PFC connections during episodic memory retrieval

Deng

Stanley

Monge

et al. 2019

Preprint

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During demanding cognitive tasks, older adults (OAs) frequently show greater prefrontal cortex (PFC) activity than younger adults (YAs). This age-related PFC activity increase is often associated with enhanced cognitive performance, suggesting functional compensation. However, the brain is a complex network of interconnected regions, and it is unclear how network connectivity of PFC regions differs for OAs vs. YAs. To investigate this, we examined the age-related difference in functional brain network mediating episodic memory retrieval. YAs and OAs participants encoded and then recalled visual scenes, and age-related differences in network topology during memory retrieval were investigated as a function of memory performance. We measured both quantitative changes in functional integration and qualitative reconfiguration in connectivity patterns. The study yielded three main findings. First, PFC regions were more functionally integrated with the rest of the brain network in OAs. Critically, this age-related increase in PFC integration was associated with better retrieval performance. Second, PFC regions showed stronger performance-related reconfiguration of connectivity patterns in OAs. Finally, the magnitude of PFC reconfiguration increases in OAs tracked reconfiguration reductions in the medial temporal lobe (MTL)a core episodic memory region, suggesting that PFC connectivity in OAs may be compensating for MTL deficits.

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“…Alpha and theta similarly make diverse contributions depending on the functional network in which they are acting (Voytek et al [2010]; Lisman and Jensen [2013]). And functional networks for given tasks can be differentiated according to which brain areas they comprise (see Stanley et al [2019], for helpful discussion). The examples discussed in this and the next section exhibit these principles of functional variability.…”

Section: Dynamic Interactionmentioning

confidence: 99%

Getting over Atomism: Functional Decomposition in Complex Neural Systems

Burnston¹

2021

The British Journal for the Philosophy of Science

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Functional decomposition is an important goal in the life sciences, and is central to mechanistic explanation and explanatory reduction. A growing literature in philosophy of science, however, has challenged decomposition-based notions of explanation. ‘Holists’ posit that complex systems exhibit context-sensitivity, dynamic interaction, and network dependence, and that these properties undermine decomposition. They then infer from the failure of decomposition to the failure of mechanistic explanation and reduction. I argue that complexity, so construed, is only incompatible with one notion of decomposition, which I call ‘atomism’, and not with decomposition writ large. Atomism posits that function ascriptions must be made to parts with minimal reference to the surrounding system. Complexity does indeed falsify atomism, but I contend that there is a weaker, ‘contextualist’ notion of decomposition that is fully compatible with the properties that holists cite. Contextualism suggests that the function of parts can shift with external context, and that interactions with other parts might help determine their context-appropriate functions. This still admits of functional decomposition within a given context. I will give examples based on the notion of oscillatory multiplexing in systems neuroscience. If contextualism is feasible, then holist inferences are faulty—one cannot infer from the presence of complexity to the failure of decomposition, mechanism, and reductionism. 1Introduction2Atomism3Holist Inferences in Detail4Contextualism as an Alternative5Multiplexing and Contextualist Decomposition 5.1Internal dynamics5.2Dynamic interaction5.3Network dependence6Philosophical Upshot 6.1The scope and limits of mechanistic explanation6.2The context objection to reduction7Conclusion

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Network Modularity as a Foundation for Neural Reuse

Cited by 12 publications

References 63 publications

Contents, vehicles, and complex data analysis in neuroscience

Contents, vehicles, and complex data analysis in neuroscience

Age-related compensatory reconfiguration of PFC connections during episodic memory retrieval

Getting over Atomism: Functional Decomposition in Complex Neural Systems

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