Biology subjects

Standvoss, K.

Publications and source records attributed to Standvoss, K..

4 recordsLinked to original sources

bioRxiv2022

Taking shortcuts: Great for travel, but not for reproducible methods sections

Methods sections are often missing critical details needed to reproduce an experiment. Methodological shortcut citations, in which authors cite previous papers instead of describing the method in detail, may contribute to this problem. This meta-research study used three approaches to systematically examine the use of shortcut citations in neuroscience, biology and psychiatry. First, we examined papers to determine why authors use citations in the methods section and to assess how often shortcut citations were used. Common reasons for using citations in the methods section included explaining how something was done by citing a previous resource that used the method (methodological shortcut citation), giving credit or specifying what was used (who or what citation), and providing context or a justification (why citation). Next, we reviewed 15 papers to determine what can happen when readers follow shortcut citations to find methodological details. While shortcut citations can be used effectively, problems encountered included difficulty identifying or accessing the cited materials, missing or insufficient descriptions of the cited method, and chains of shortcut citations. Third, we examined journal policies. Fewer than one quarter of journals had policies describing how authors should report methods that have been described previously or asking authors to explain modifications of previously described methods. We propose that methodological shortcut citations should meet three criteria; cited resources should describe a method very similar to the authors method, provide enough detail to allow others to implement the method, and be open access. We outline actions that authors and journals can take to use shortcut citations responsibly, while fostering a culture of open and reproducible methods reporting.

neuroscience↗

bioRxiv2021

Humans and mice fluctuate between external and internal modes of sensory processing

2Perception is known to cycle through periods of enhanced and reduced sensitivity to external information. Here, we asked whether such infra-slow fluctuations arise as a noise-related epiphenomenon of limited processing capacity or, alternatively, represent a structured mech{-}anism of perceptual inference. Using two large-scale datasets, we found that humans and mice waver between alternating intervals of externally- and internally-oriented modes of sensory analysis. During external mode, perception aligned more closely with the external sensory information, whereas internal mode was characterized by enhanced biases toward perceptual history. Computational modeling indicated that dynamic changes in mode are enabled by two interlinked factors: (i), the integration of subsequent inputs over time and, (ii), infra-slow anti-phase oscillations in the perceptual impact of external sensory information versus internal predictions that are provided by perceptual history. Simulated data suggested that between-mode fluctuations may benefit perception by generating unambiguous error signals that enable robust learning and metacognition in volatile environments. 3 One sentence summaryHumans and mice fluctuate between external and internal modes of sensory processing.

neuroscience↗

bioRxiv2020

Visual Attention Through Uncertainty Minimization in Recurrent Generative Models

Allocating visual attention through saccadic eye movements is a key ability of intelligent agents. Attention is both influenced through bottom-up stimulus properties as well as top-down task demands. The interaction of these two attention mechanisms is not yet fully understood. A parsimonious reconciliation posits that both processes serve the minimization of predictive uncertainty. We propose a recurrent generative neural network model that predicts a visual scene based on foveated glimpses. The model shifts its attention in order to minimize the uncertainty in its predictions. We show that the proposed model produces naturalistic eye movements focusing on informative stimulus regions. Introducing additional tasks modulates the saccade patterns towards task-relevant stimulus regions. The models saccade characteristics correspond well with previous experimental data in humans, providing evidence that uncertainty minimization could be a fundamental mechanisms for the allocation of visual attention.

neuroscience↗

bioRxiv2017

Encoding And Decoding Dynamic Sensory Signals With Recurrent Neural Networks: An Application Of Conceptors To Birdsongs

In a constantly changing environment the brain has to make sense of dynamic patterns of sensory input. These patterns can refer to stimuli with a complex and hierarchical structure which has to be inferred from the neural activity of sensory areas in the brain. Such areas were found to be locally recurrently structured as well as hierarchically organized within a given sensory domain. While there is a great body of work identifying neural representations of various sensory stimuli at different hierarchical levels, less is known about the nature of these representations. In this work, we propose a model that describes a way to encode and decode sensory stimuli based on the activity patterns of multiple, recurrently connected neural populations with different receptive fields. We demonstrate the ability of our model to learn and recognize complex, dynamic stimuli using birdsongs as exemplary data. These birdsongs can be described by a 2-level hierarchical structure, i.e. as sequences of syllables. Our model matches this hierarchy by learning single syllables on a first level and sequences of these syllables on a top level. Model performance on recognition tasks is investigated for an increasing number of syllables or songs to recognize and compared to state-of-the-art machine learning approaches. Finally, we discuss the implications of our model for the understanding of sensory pattern processing in the brain. We conclude that the employed encoding and decoding mechanisms might capture general computational principles of how the brain extracts relevant information from the activity of recurrently connected neural populations.

neuroscience↗