Reconstructing computational system dynamics from neural data with recurrent neural networks

Durstewitz, Daniel; Koppe, Georgia; Thurm, Max Ingo

doi:10.1038/s41583-023-00740-7

Cited by 33 publications

(17 citation statements)

References 195 publications

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“…For example, the monkey brain–computer interface experiment conducted by Sadtler et al (2014; see “The Intrinsic Manifold”) could be replicated in humans using magnetoencephalography (e.g., Corsi et al, 2021; Stiso et al, 2020; Youssofzadeh et al, 2023). RNNs reproducing the intrinsic manifold could then either be constructed by hand (e.g., Wärnberg & Kumar, 2019) or reverse-engineered directly from the neural data (Durstewitz et al, 2023; see Box 3). The connectivity matrices of these networks could then be used to derive Gramians that formalize the shape of the intrinsic manifold.…”

Section: Discussionmentioning

confidence: 99%

“…However, this empirical approach requires that the experimenter set the initial state of the system and then apply impulsive inputs to it (Lall et al, 2002), which are difficult criteria to satisfy in (noninvasive) human experiments. A more promising approach is to train computational models directly on neural data in order to extract their underlying generative structure (Durstewitz, 2017; Durstewitz et al, 2023; Langdon & Engel, 2022; Pandarinath et al, 2018). For example, the intrinsic manifold can be produced by RNNs trained on fMRI data (Koppe et al, 2019) and/or task-related behavioral data (Z.…”

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confidence: 99%

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The controllosphere: The neural origin of cognitive effort.

Holroyd

2024

Psychological Review

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

The controllosphere: The neural origin of cognitive effort.

Holroyd

2024

Psychological Review

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“…We used trained recurrent neural networks [20, 22, 24]. RNNs can be trained to reproduce both neural activity and behavior [17, 23, 31, 34, 36].…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, we seek to characterize dynamical systems that could underlie working memory relying on phase coding with neural oscillations. We do so by assuming that cognitive functions can be described by a low-dimensional dynamical system, implemented through populations of neurons (computation through dynamics) [20][21][22][23][24][25][26], in line with empirical observations [27,28].…”

Section: Introductionmentioning

confidence: 99%

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Trained recurrent neural networks develop phase-locked limit cycles in a working memory task

Pals

Macke

Barak

2023

Preprint

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Neural oscillations are ubiquitously observed in many brain areas. One proposed functional role of these oscillations is that they serve as an internal clock, or 'frame of reference'. Information can be encoded by the timing of neural activity relative to the phase of such oscillations. In line with this hypothesis, there have been multiple empirical observations of such phase codes in the brain. Here we ask: What kind of neural dynamics support phase coding of information with neural oscillations? We tackled this question by analyzing recurrent neural networks (RNNs) that were trained on a working memory task. The networks were given access to an external reference oscillation and tasked to produce an oscillation, such that the phase difference between the reference and output oscillation maintains the identity of transient stimuli. We found that networks converged to stable oscillatory dynamics. Reverse engineering these networks revealed that each phase-coded memory corresponds to a separate limit cycle attractor. We characterized how the stability of the attractor dynamics depends on both reference oscillation amplitude and frequency, properties that can be experimentally observed. To understand the connectivity structures that underlie these dynamics, we showed that trained networks can be described as two phase-coupled oscillators. Using this insight, we condensed our trained networks to a reduced model consisting of two functional modules: One that generates an oscillation and one that implements a coupling function between the internal oscillation and external reference. In summary, by reverse engineering the dynamics and connectivity of trained RNNs, we propose a mechanism by which neural networks can harness reference oscillations for working memory. Specifically, we propose that a phase-coding network generates autonomous oscillations which it couples to an external reference oscillation in a multi-stable fashion.

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The role of artificial intelligence in the management of liver diseases

Lu,

Chuang,

2024

The Kaohsiung J of Med Scie

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Universal neonatal hepatitis B virus (HBV) vaccination and the advent of direct‐acting antivirals (DAA) against hepatitis C virus (HCV) have reshaped the epidemiology of chronic liver diseases. However, some aspects of the management of chronic liver diseases remain unresolved. Nucleotide analogs can achieve sustained HBV DNA suppression but rarely lead to a functional cure. Despite the high efficacy of DAAs, successful antiviral therapy does not eliminate the risk of hepatocellular carcinoma (HCC), highlighted the need for cost‐effective identification of high‐risk populations for HCC surveillance and tailored HCC treatment strategies for these populations. The accessibility of high‐throughput genomic data has accelerated the development of precision medicine, and the emergence of artificial intelligence (AI) has led to a new era of precision medicine. AI can learn from complex, non‐linear data and identify hidden patterns within real‐world datasets. The combination of AI and multi‐omics approaches can facilitate disease diagnosis, biomarker discovery, and the prediction of treatment efficacy and prognosis. AI algorithms have been implemented in various aspects, including non‐invasive tests, predictive models, image diagnosis, and the interpretation of histopathology findings. AI can support clinicians in decision‐making, alleviate clinical burdens, and curtail healthcare expenses. In this review, we introduce the fundamental concepts of machine learning and review the role of AI in the management of chronic liver diseases.

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Reconstructing computational system dynamics from neural data with recurrent neural networks

Cited by 33 publications

References 195 publications

The controllosphere: The neural origin of cognitive effort.

The controllosphere: The neural origin of cognitive effort.

Trained recurrent neural networks develop phase-locked limit cycles in a working memory task

The role of artificial intelligence in the management of liver diseases

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