Biology subjects

Kording, K. P.

Publications and source records attributed to Kording, K. P..

10 recordsLinked to original sources

bioRxiv2018

Bioscience-scale automated detection of figure element reuse

Scientists reuse figure elements sometimes appropriately, e.g. when comparing methods, and sometimes inappropriately, e.g. when presenting an old experiment as a new control. To understand such reuse, automatically detecting it would be important. Here we present an analysis of figure element reuse on a large dataset comprising 760 thousand open access articles and 2 million figures. Our algorithm detects figure region reuse, while being robust to rotation, cropping, resizing, and contrast changes, and estimates which of the reuses have biological meaning. Then a three-person panel analyzes how problematic these biological reuses are using contextual information such as captions and full texts. Based on the panel reviews, we estimate that 9% of the biological reuses would be unanimously perceived as at least suspicious. We further estimate that 0.6% of all articles would be unanimously perceived as fraudulent, with inappropriate reuses occurring 43% across articles, 28% within article, and 29% within a figure. Our tool rapidly detects image reuse at scale, promising to be useful to a broad range of people that campaign for scientific integrity. We suggest that a great deal of scientific fraud will be, sooner or later, detectable by automatic methods.

scientific communication and education↗

bioRxiv2018

Linear-Nonlinear-Time-Warp-Poisson models of neural activity

Prominent models of spike trains assume only one source of variability - stochastic (Poisson) spiking - when stimuli and behavior are fixed. However, spike trains may also reflect variability due to internal processes such as planning. For example, we can plan a movement at one point in time and execute it at some arbitrary later time. Neurons involved in planning may thus share an underlying time-course that is not precisely locked to the actual movement. Here we combine the standard Linear-Nonlinear-Poisson (LNP) model with Dynamic Time Warping (DTW) to account for shared temporal variability. When applied to recordings from macaque premotor cortex, we find that time warping considerably improves predictions of neural activity. We suggest that such temporal variability is a widespread phenomenon in the brain which should be modeled.

neuroscience↗

bioRxiv2018

From preliminary to definitive plans: two classes of neurons in frontal eye field

Prior to selecting an action, we often consider other possibilities. How does the brain represent these preliminary plans prior to action selection? Here, we investigated this question in the oculomotor system during self-guided search of natural scenes. We found two classes of neurons in the frontal eye field (FEF): 1) \"late selection neurons\" that represented the selected action plan not long before the upcoming saccade, and 2) \"early selection neurons\" that became predictive of the upcoming saccade much earlier, often before the previous saccade had even ended. Crucially, these early selection neurons did not only predict the upcoming saccade direction; they also reflected the probabilities of possible upcoming saccades, even when they did not end up being selected. Our results demonstrate that during naturalistic eye movements, separate populations of neurons code for preliminary and definitive plans.

neuroscience↗

bioRxiv2018

Sampling Neuron Morphologies

The intricate morphology of neurons has fascinated since the dawn of neuroscience, and yet, it is hard to synthesize them. Current algorithms typically define a growth process with parameters that allow matching aspects of the morphologies. However, such algorithmic growth processes are far simpler than the biological ones. What is needed is an algorithm that, given a database of morphologies, produces more of those. Here, we introduce a generator for neuron morphologies that is based on a statistical sampling process. Our Reversible Jump Markov chain Monte Carlo (RJMCMC) method starts with a trivial neuron and iteratively perturbs the morphology bringing the features close to those of the database. By quantifying the statistics of the generated neurons, we find that it outperforms growth-based models for many features. Good generative models for neuron morphologies promise to be important both for neural simulations and for morphology reconstructions from imaging data.

neuroscience↗

bioRxiv2017

Corticocortical signaling drives activity in a downstream area rapidly and scalably

Quantitative analysis of corticocortical signaling is needed to understand and model information processing in cerebral networks. However, higher-order pathways, hodologically remote from sensory input, are not amenable to spatiotemporally precise activation by sensory stimuli. Here, we combined parametric channelrhodopsin-2 (ChR2) photostimulation with multi-unit electrophysiology to study corticocortical driving in a parietofrontal pathway from retrosplenial cortex (RSC) to posterior secondary motor cortex (M2) in mice in vivo. Ketamine anesthesia was used both to eliminate complex activity associated with the awake state and to enable stable recordings of responses over a wide range of stimulus parameters. Photostimulation of ChR2-expressing neurons in RSC, the upstream area, produced local activity that decayed quickly. This activity in turn drove downstream activity in M2 that arrived rapidly (5-10 ms latencies), and scaled in amplitude across a wide range of stimulus parameters as an approximately constant fraction (~0.2) of the upstream activity. A model-based analysis could explain the corticocortically driven activity with exponentially decaying kernels (~20 ms time constant) and small delay. Reverse (antidromic) driving was similarly robust. The results show that corticocortical signaling in this pathway drives downstream activity rapidly and scalably, in a mostly linear manner. These properties, identified in anesthetized mice and represented in a simple model, suggest a robust basis for supporting complex non-linear dynamic activity in corticocortical circuits in the awake state.\n\nSIGNIFICANCE STATEMENTThe signaling properties of corticocortical connections are not well understood, particularly for higher-order inter-areal pathways. Here, we developed a paradigm based on parametric optogenetic photostimulation, linear-array electrophysiology, and mathematical modeling to characterize signaling along corticortical connections linking retrosplenial cortex to posterior secondary motor cortex (M2) in anesthetized mice. The results indicate that corticocortically driven activity in the downstream area followed the optogenetically evoked upstream activity in a rapid and scalable manner, and could be described with a simple linear integrator model. These findings suggest that this pathway, when activated selectively in the unconscious state, supports intrinsically linear inter-areal communication.

neuroscience↗

bioRxiv2017

Population Coding Of Conditional Probability Distributions In Dorsal Premotor Cortex

Our bodies and the environment constrain our movements. For example, when our arm is fully outstretched, we cannot extend it further. More generally, the distribution of possible movements is conditioned on the state of our bodies in the environment, which is constantly changing. However, little is known about how the brain represents such distributions, and uses them in movement planning. Here, we recorded from dorsal premotor cortex (PMd) and primary motor cortex (M1) while monkeys reached to randomly placed targets. The hands position within the workspace created probability distributions of possible upcoming targets, which affected movement trajectories and latencies. PMd, but not M1, neurons had increased activity when the monkeys hand position made it likely the upcoming movement would be in the neurons preferred directions. Across the population, PMd activity represented probability distributions of individual upcoming reaches, which depended on rapidly changing information about the bodys state in the environment.

neuroscience↗

bioRxiv2017

How Does Mixed Reality Affect Quiet Stance?

Mixed reality (MR) has promise for learning, design, and entertainment, and for use during everyday life. However, when interacting with objects in mixed reality, will moving objects make us fall or perturb our postural stability? To address this question, we recruited participants, instructed them to stand quietly, and measured how much virtual objects presented in mixed reality (Microsoft HoloLens) affected their stance. We analyzed the effects of solid object and text, in both a static and a dynamic setting. Mixed reality events induced some movements, but the effect, while significant, was exceptionally small (< 1mm & < 0.5{degrees} perturbations in terms of mean distance and angle rotations). We conclude that induced movement in \"real reality\" should not be too much of a concern when designing mixed reality applications.

physiology↗

bioRxiv2017

Modern machine learning far outperforms GLMs at predicting spikes

Neuroscience has long focused on finding encoding models that effectively ask \"what predicts neural spiking?\" and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a GLM. Here we compared the predictive performance of GLMs to three leading machine learning methods: feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from standard representations of reaching kinematics, and in rat hippocampal cells from open field location and orientation. In general, the modern methods (particularly XGBoost and the ensemble) produced more accurate spike predictions and were less sensitive to the preprocessing of features. This discrepancy in performance suggests that standard feature sets may often relate to neural activity in a nonlinear manner not captured by GLMs. Encoding models built with machine learning techniques, which can be largely automated, more accurately predict spikes and can offer meaningful benchmarks for simpler models.

neuroscience↗

bioRxiv2017

Saccadic suppression as a perceptual consequence of efficient sensorimotor estimation

Humans perform saccadic eye movements two to three times per second. When doing so, the nervous system strongly suppresses sensory feedback for extended periods of time in comparison with the movement time. Why does the brain discard so much visual information? Here we suggest that perceptual suppression may arise from efficient sensorimotor computations, assuming that perception and control are fundamentally linked. More precisely, we show that a Bayesian estimator should reduce the weight of sensory information around the time of saccades, as a result of signal dependent noise and of sensorimotor delays. Such reduction parallels the behavioral suppression occurring prior to and during saccades, and the reduction in neural responses to visual stimuli observed across the visual hierarchy. We suggest that saccadic suppression originates from efficient sensorimotor processing, indicating that the brain shares neural resources for perception and control.

neuroscience↗

bioRxiv2016

Ten simple rules for structuring papers

Good scientific writing is essential to career development and to the progress of science. A well-structured manuscript allows readers and reviewers to get excited about the subject matter, to understand and verify the papers contributions, and to integrate these contributions into a broader context. However, many scientists struggle with producing high-quality manuscripts and typically get little training in paper writing. Focusing on how readers consume information, we present a set of 10 simple rules to help you get across the main idea of your paper. These rules are designed to make your paper more influential and the process of writing more efficient and pleasurable.

scientific communication and education↗