Neural tuning and representational geometry

Kriegeskorte, Nikolaus; Wei, Xue-Xin

doi:10.1038/s41583-021-00502-3

Cited by 127 publications

(119 citation statements)

References 239 publications

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“…1e ; see Methods; see also refs. 50 , 51 ). This method fits an encoding model at each voxel independently, using weighted combinations of deepnet units ( ), to predict the univariate response profile.…”

Section: Resultsmentioning

confidence: 99%

A self-supervised domain-general learning framework for human ventral stream representation

Konkle

Alvarez

2022

Nat Commun

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Anterior regions of the ventral visual stream encode substantial information about object categories. Are top-down category-level forces critical for arriving at this representation, or can this representation be formed purely through domain-general learning of natural image structure? Here we present a fully self-supervised model which learns to represent individual images, rather than categories, such that views of the same image are embedded nearby in a low-dimensional feature space, distinctly from other recently encountered views. We find that category information implicitly emerges in the local similarity structure of this feature space. Further, these models learn hierarchical features which capture the structure of brain responses across the human ventral visual stream, on par with category-supervised models. These results provide computational support for a domain-general framework guiding the formation of visual representation, where the proximate goal is not explicitly about category information, but is instead to learn unique, compressed descriptions of the visual world.

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Section: Resultsmentioning

confidence: 99%

A self-supervised domain-general learning framework for human ventral stream representation

Konkle

Alvarez

2022

Nat Commun

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“…Neural response naturally forms neural manifold by systematically varying the stimulus. The geometry of the encoding manifold has multiple implications in understanding the format of the representation and in linking neural responses to the behavior (reviewed in [62]). To understand how the tuning variability affects the geometrical properties of the encoding manifold, we begin by simulating a multiplicative gain change, which is simple scenario exhibiting how fluctuating signals are displayed by the geometric analysis.…”

Section: Change Of Representational Geometry Induced By Spontaneous F...mentioning

confidence: 99%

Unsupervised approach to decomposing neural tuning variability

Zhu

Wei

2022

Preprint

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Neural representation is often described by the tuning curves of individual neurons with respect to certain stimulus variables. Despite this tradition, it has become increasingly clear that neural tuning can vary substantially in accordance with a collection of internal and external factors. A challenge we are facing is the lack of appropriate methods to accurately capture trial-to-trial tuning variability directly from the noisy neural responses. Here we introduce an unsupervised statistical approach, Poisson functional principal component analysis (Pf-PCA), which identifies different sources of systematic tuning fluctuations, moreover encompassing several current models (e.g.,multiplicative gain models) as special cases. Applying this method to neural data recorded from macaque primary visual cortex– a paradigmatic case for which the tuning curve approach has been scientific essential– we discovered a novel simple relationship governing the variability of orientation tuning, which unifies different types of gain changes proposed previously. By decomposing the neural tuning variability into interpretable components, our method enables discovery of new structure of the neural code, capturing the influence of the stimulus drive and internal states simultaneously.

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“…While these methods have been popular as a means of comparing ANNs to neural activity, they can also be applied to look for theoretically-motivated encoding schemes or compare across different neural populations within a single system, thereby providing insights into how information is transformed. See: Kriegeskorte [2008], Kornblith et al [2019], Morcos et al [2018b], Kriegeskorte and Kievit [2013], Kriegeskorte and Wei [2021] Representation Geometry A diverse set of analyses that characterize the geometry of neural population responses are becoming more common as a means of understanding the computations and transformations the brain is implementing. See: Bernardi et al [2020], Chung andAbbott [2021], Nieh et al [2021], Chaudhuri et al [2019] Network Analyses.…”

Section: The Toolboxmentioning

confidence: 99%

“…Methods for quantifying the type and amount of information encoded in a given population's activity can include characterization of tuning properties, trained decoder performance, regression models, and formal metrics of information theory. See: [Kriegeskorte and Wei, 2021, Kriegeskorte and Douglas, 2019, Butts and Goldman, 2006, Quiroga and Panzeri, 2009, Timme and Lapish, 2018, Paninski et al, 2007 Bespoke Methods. Many experimental studies have analyses designed specifically for the data collected in the study, and these methods normally don't undergo formal method development.…”

Section: The Toolboxmentioning

confidence: 99%

Testing the Tools of Systems Neuroscience on Artificial Neural Networks

W.¹

2022

Preprint

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Neuroscientists apply a range of common analysis tools to recorded neural activity in order to glean insights into how neural circuits implement computations. Despite the fact that these tools shape the progress of the field as a whole, we have little empirical evidence that they are effective at quickly identifying the phenomena of interest. Here I argue that these tools should be explicitly tested and that artificial neural networks (ANNs) are an appropriate testing grounds for them. The recent resurgence of the use of ANNs as models of everything from perception to memory to motor control stems from a rough similarity between artificial and biological neural networks and the ability to train these networks to perform complex high-dimensional tasks. These properties, combined with the ability to perfectly observe and manipulate these systems, makes them well-suited for vetting the tools of systems and cognitive neuroscience. I provide here both a roadmap for performing this testing and a list of tools that are suitable to be tested on ANNs. Using ANNs to reflect on the extent to which these tools provide a productive understanding of neural systems-and on exactly what understanding should mean here-has the potential to expedite progress in the study of the brain.

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Neural tuning and representational geometry

Cited by 127 publications

References 239 publications

A self-supervised domain-general learning framework for human ventral stream representation

A self-supervised domain-general learning framework for human ventral stream representation

Unsupervised approach to decomposing neural tuning variability

Testing the Tools of Systems Neuroscience on Artificial Neural Networks

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