Neurogaming Technology Meets Neuroscience Education: A Cost-Effective, Scalable, and Highly Portable Undergraduate Teaching Laboratory for Neuroscience.

Active research-driven approaches that successfully incorporate new technology are known to catalyze student learning. Yet achieving these objectives in neuroscience education is especially challenging due to the prohibitive costs and technical demands of research-grade equipment. Here we describe a method that circumvents these factors by leveraging consumer EEG-based neurogaming technology to create an affordable, scalable, and highly portable teaching laboratory for undergraduate courses in neuroscience. This laboratory is designed to give students hands-on research experience, consolidate their understanding of key neuroscience concepts, and provide a unique real-time window into the working brain. Survey results demonstrate that students found the lab sessions engaging. Students also reported the labs enhanced their knowledge about EEG, their course material, and neuroscience research in general.

Decoding the Brain: Neural Representation and the Limits of Multivariate Pattern Analysis in Cognitive Neuroscience.

Since its introduction, multivariate pattern analysis (MVPA), or 'neural decoding', has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the decoder's dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the dictum, arguing that it is false: decodability is a poor guide for revealing the content of neural representations. However, we also suggest how the dictum can be improved on, in order to better justify inferences about neural representation using MVPA. 1Introduction2A Brief Primer on Neural Decoding: Methods, Application, and Interpretation 2.1What is multivariate pattern analysis?2.2The informational benefits of multivariate pattern analysis3Why the Decoder's Dictum Is False 3.1We don't know what information is decoded3.2The theoretical basis for the dictum3.3Undermining the theoretical basis4Objections and Replies 4.1Does anyone really believe the dictum?4.2Good decoding is not enough4.3Predicting behaviour is not enough5Moving beyond the Dictum6Conclusion.

Influence of Biological Factors on Connectivity Patterns for Concholepas concholepas (loco) in Chile.

In marine benthic ecosystems, larval connectivity is a major process influencing the maintenance and distribution of invertebrate populations. Larval connectivity is a complex process to study as it is determined by several interacting factors. Here we use an individual-based, biophysical model, to disentangle the effects of such factors, namely larval vertical migration, larval growth, larval mortality, adults fecundity, and habitat availability, for the marine gastropod Concholepas concholepas (loco) in Chile. Lower transport success and higher dispersal distances are observed including larval vertical migration in the model. We find an overall decrease in larval transport success to settlement areas from northern to southern Chile. This spatial gradient results from the combination of current direction and intensity, seawater temperature, and available habitat. From our simulated connectivity patterns we then identify subpopulations of loco along the Chilean coast, which could serve as a basis for spatial management of this resource in the future.

Large-Scale Examination of Spatio-Temporal Patterns of Drifting Fish Aggregating Devices (dFADs) from Tropical Tuna Fisheries of the Indian and Atlantic Oceans.

Since the 1990s, massive use of drifting Fish Aggregating Devices (dFADs) to aggregate tropical tunas has strongly modified global purse-seine fisheries. For the first time, a large data set of GPS positions from buoys deployed by French purse-seiners to monitor dFADs is analysed to provide information on spatio-temporal patterns of dFAD use in the Atlantic and Indian Oceans during 2007-2011. First, we select among four classification methods the model that best separates "at sea" from "on board" buoy positions. A random forest model had the best performance, both in terms of the rate of false "at sea" predictions and the amount of over-segmentation of "at sea" trajectories (i.e., artificial division of trajectories into multiple, shorter pieces due to misclassification). Performance is improved via post-processing removing unrealistically short "at sea" trajectories. Results derived from the selected model enable us to identify the main areas and seasons of dFAD deployment and the spatial extent of their drift. We find that dFADs drift at sea on average for 39.5 days, with time at sea being shorter and distance travelled longer in the Indian than in the Atlantic Ocean. 9.9% of all trajectories end with a beaching event, suggesting that 1,500-2,000 may be lost onshore each year, potentially impacting sensitive habitat areas, such as the coral reefs of the Maldives, the Chagos Archipelago, and the Seychelles.