A NWB-based dataset and processing pipeline of human single-neuron activity during a declarative memory task

A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory

Novitates Gabonenses 93: a fresh look at Podostemaceae in Gabon following recent inventories, with a new combination for Ledermanniella nicolasii

Background and aims – Podostemaceae is a family of strictly aquatic plants found in rapids and waterfalls. Despite a recent treatment in the Flore du Gabon, the family remained poorly known, with no major studies including Gabonese collections, and almost no targeted inventories since 1966. We present the first large-scale inventory of this family in Gabon, targeting Podostemaceae throughout the country, providing new additions to the flora of Gabon and many new records of poorly known species. Material and methods – Fieldwork was conducted in Gabon between 2017 and 2021. The collected specimens were primarily preserved in ethanol with associated silica gel-preserved material and photographs. Material available at BR, BRLU, LBV, MO, P, WAG, and Z/ZT was examined. For each species, information on distribution and ecology is presented, as well as a distribution map in Gabon. Key results – The 500 newly collected specimens represent 91.4% of all known collections of Podostemaceae from Gabon. Three taxa are newly recorded for the country, including one genus (Inversodicraea tenax, Ledermanniella schlechteri, and Saxicolella nana). New distribution records are also presented for 13 little-known species. Four taxa are excluded from the Gabonese flora (the genus Dicraeanthus, Inversodicraea ledermannii, Ledermanniella sanagaensis, and Macropodiella garrettii). To date, 20 species belonging to five different genera are known to occur in Gabon. A new combination is proposed for Ledermanniella nicolasii, and Inversodicraea tanzaniensis is now considered as a synonym of Inversodicraea tenax

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating