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Biology subjects

Hem, I. G.

Publications and source records attributed to Hem, I. G..

3 recordsLinked to original sources

Prefrontal cortex encodes behavior states decoupled from movement

Prefrontal cortex is often viewed as an extension of the motor system, but little is understood of how it relates to natural motor behavior. We therefore tracked the kinematics of freely moving rats performing minimally structured tasks and measured which aspects of behavior were read out in prefrontal neural populations. Naturalistic behaviors such as rearing or chasing a bait were each encoded by unique neural ensembles, but the behavioral representations were not anchored to posture or movement. Rather, the coding of kinematic features depended on their relevance to the animals current behavior or which task the animal performed. Behavior-specific ensembles often preceded and outlasted physical actions and, accordingly, prefrontal population activity evolved at slower timescales than in motor cortex. These findings argue that prefrontal coding of behavior is not locked to motor output, and may instead reflect motivations to perform certain actions rather than the actions themselves. HighlightsO_LIPrefrontal neural ensembles uniquely encode different naturalistic actions C_LIO_LIBehavioral tuning is not explained by movement kinematics C_LIO_LIPopulation activity in prefrontal cortex evolves slower than in M1 C_LIO_LISingle-cell coding of behavior varies across tasks yet ensemble coding is stable C_LI

neuroscience↗

Bayesian inference of spike-time dependent learning rules from single neuron recordings in humans

Spike-timing dependent plasticity (STDP) learning rules are popular in both neuroscience and artificial neural networks due to their ability to capture the change in neural connections arising from the correlated activity of neurons. Recent technological advances have made large neural recordings common, substantially increasing the probability that two connected neurons are simultaneously observed, which we can use to infer functional connectivity and associated learning rules. We use a Bayesian framework and assume neural spike recordings follow a binary data model to infer the connections and their evolution over time from data using STDP rules. We test the resulting method on simulated and real data, where the real case study consists of human electrophysiological recordings. The simulated case study allows validation of the model, and the real case study shows that we are able to infer learning rules from awake human data.

neuroscience↗

Robust Genomic Modelling Using Expert Knowledge about Additive, Dominance and Epistasis Variation

We propose a novel Bayesian approach that robustifies genomic modelling by leveraging expert knowledge through prior distributions. The central component is the hierarchical decomposition of phenotypic variation into additive and non-additive genetic variation, which leads to an intuitive model parameterization that can be visualised as a tree. The edges of the tree represent ratios of variances, for example broad-sense heritability, which are quantities for which expert knowledge is natural to exist. Penalized complexity priors are defined for all edges of the tree in a bottom-up procedure that respects the model structure and incorporates expert knowledge through all levels. We investigate models with different sources of variation and compare the performance of different priors implementing varying amounts of expert knowledge in the context of plant breeding. A simulation study shows that the proposed priors implementing expert knowledge improve the robustness of genomic modelling and the selection of the genetically best individuals in a breeding program. We observe this improvement in both variety selection on genetic values and parent selection on additive values; the variety selection benefited the most. In a real case study expert knowledge increases phenotype prediction accuracy for cases in which the standard maximum likelihood approach did not find optimal estimates for the variance components. Finally, we discuss the importance of expert knowledge priors for genomic modelling and breeding, and point to future research areas of easy-to-use and parsimonious priors in genomic modelling.

genomics↗