Representational geometry of perceptual decisions in the monkey parietal cortex

Okazawa, Gouki; Hatch, Christina E.; Mancoo, Allan; Machens, Christian K.; Kiani, Roozbeh

doi:10.1016/j.cell.2021.05.022

Cited by 74 publications

(90 citation statements)

References 82 publications

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“…However, unlike in the neural network, manifolds (number lines) obtained from BOLD data were curved. We note that the curvature of these representational manifolds around their midpoint yields approximately orthogonal axes for rank and uncertainty, and that this phenomenon has been previously observed in scalp EEG recordings [25] and in multi-unit activity from area LIP of the macaque [33].…”

Section: Resultssupporting

confidence: 73%

“…𝑖 1 and 𝑖 6 ) tended to be larger than those involving intermediate ranks [t33 = 4.8, p < 0.001; t33 = 4.4, p < 0.001 in PPC and dmPFC respectively]. We note that the curvature of these representational manifolds around their midpoint yields approximately orthogonal axes for rank and uncertainty, and that this phenomenon has been previously observed in scalp EEG recordings [25] and in multi-unit activity from area LIP of the macaque [33].…”

Section: Resultssupporting

confidence: 72%

“…One curiosity of our findings is that neural manifolds for the transitive series were not straight as in the neural networks, but instead were inflected around their midpoints (ranks 3/4 in test_short or 6/7 in test_long), forming a horseshoe shape in low-dimensional space. We have previously observed this pattern in geometric analysis of whole-brain scalp EEG signals evoked by transitively ordered images [25] and a recent report has emphasised a similar phenomenon in macaque PPC and medial PFC during discrimination of both faces and dot motion patterns [33]. The reasons for this form to the manifold is unclear.…”

Section: Discussionmentioning

confidence: 76%

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Neural knowledge assembly in humans and deep networks

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et al. 2021

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Human understanding of the world can change rapidly when new information comes to light, such as when a plot twist occurs in a work of fiction. This flexible knowledge assembly requires few-shot reorganisation of neural codes for relations among objects and events. However, existing computational theories are largely silent about how this could occur. Here, participants learned a transitive ordering among novel objects within two distinct contexts, before exposure to new knowledge revealing how the contexts were linked. BOLD signals in dorsal frontoparietal cortical areas revealed that objects were rapidly and dramatically rearranged on the neural manifold after minimal exposure to the linking information. We then adapt stochastic online gradient descent to permit similar rapid knowledge assembly in a neural network model.

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Section: Resultssupporting

confidence: 73%

Section: Resultssupporting

confidence: 72%

Section: Discussionmentioning

confidence: 76%

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Neural knowledge assembly in humans and deep networks

Nelli

Braun

Dumbalska

et al. 2021

Preprint

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“…Thus, the first PC is the eigenvector with the highest eigenvalue. We mainly analyzed eigenvectors for the first three PCs (PC1 to PC3) in the following analyses, as the top three PCs had been analyzed previously (Okazawa et al, 2021). Note that we applied PCA once to each neural population, and thus, the total variances contained in the data differed among the neural populations.…”

Section: Methodsmentioning

confidence: 99%

“…Recent innovations in the state-space analysis applied to multi-neuronal activities provide insight into the dynamic structure of information processing in a neural population (Brendel et al, 2011; Churchland et al, 2012; Mante et al, 2013). The identified dynamic structures of neural population activity are known as neural population dynamics and are assumed to reflect some underlying computations occurring in a neural network in the sensory, cognitive, and motor domains (Aoi et al, 2020; Churchland et al, 2012; Murray et al, 2017; Okazawa et al, 2021; Osako et al, 2021; Raposo et al, 2014; Rossi-Pool et al, 2021). In the state-space analysis, multi-neuronal interactions with fine temporal evolution have provided a different perspective from the conventional analytical framework for single-neuron activity, known as the representational model.…”

Section: Introductionmentioning

confidence: 99%

Comparison of neural population dynamics in the regression subspace between continuous and categorical task parameters

Chen

Kunimatsu

Oya

et al. 2022

Preprint

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Neural population dynamics, presumably fundamental computational units in the brain, provide a key framework for understanding information processing in the sensory, cognitive, and motor functions. However, neural population dynamics is not explicitly related to the conventional analytic framework for single-neuron activity, i.e., representational models that analyze neuronal modulations associated with cognitive and motor parameters. In this study, we applied a recently developed state-space analysis to incorporate the representational models into the dynamic model in combination with these parameters. We compared neural population dynamics between continuous and categorical task parameters during two visual recognition tasks, using the datasets originally designed for a single-neuron approach. We successfully extracted neural population dynamics in the regression subspace, which represent modulation dynamics for both continuous and categorical task parameters with reasonable temporal characteristics. Furthermore, we combined the classical optimal-stimulus analysis paradigm for the single-neuron approach (i.e., stimulus identified as maximum neural responses) into the dynamic model, and found that the most prominent modulation dynamics at the lower dimension were derived from these optimal responses. Thus, our approach provides a unified framework for incorporating knowledge acquired with the single-neuron approach into the dynamic model as a standard procedure for describing neural modulation dynamics in the brain.

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Neural circuits and processes of working memory

Zhu,

Constantinidis

2025

Encyclopedia of the Human Brain

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Representational geometry of perceptual decisions in the monkey parietal cortex

Cited by 74 publications

References 82 publications

Neural knowledge assembly in humans and deep networks

Neural knowledge assembly in humans and deep networks

Comparison of neural population dynamics in the regression subspace between continuous and categorical task parameters

Neural circuits and processes of working memory

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