Ditlevsen, S. | bioRxiv Science

bioRxiv2018

Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence

Diving behaviour of narwhals is still largely unknown. We build three-state Hidden Markov models (HMM) to describe the diving behaviour of a narwhal and fit the models to a three-dimensional response vector of maximum dive depth, duration of dives and post-dive surface time of 8,609 dives measured in East Greenland over 83 days, an extraordinarily long and rich data set. In particular, diurnal patterns in diving behaviour for a marine mammal is being inferred, by using periodic B-splines with boundary knots in 0 and 24 hours. Several HMMs with covariates are used to characterize dive patterns. Narwhal diving patterns have not been analysed like this before, but in studies of other whale species, response variables have been assumed independent. We extend the existing models to allow for dependence between state distributions, and show that the dependence has an impact on the conclusions drawn about the diving behaviour. It is thus paramount to relax this strong and biologically unrealistic assumption to obtain trustworthy inferences.\n\nAuthor summaryNarwhals live in pristine environments. However, the increase in average temperatures in the Arctic and the concomitant loss of summer sea ice, as well as increased human activities, such as ship traffic and mineral exploration leading to increased noise pollution, are changing the environment, and therefore probably also the behavior and well-being of the narwhal. Here, we use probabilistic models to unravel the diving and feeding behavior of a male narwhal, tagged in East Greenland in 2013, and followed for nearly two months. The goal is to gain knowledge of the whales normal behavior, to be able to later detect possible changes in behavior due to climatic changes and human influences. We find that the narwhal uses around two thirds of its time searching for food, it typically feeds during deep dives (more than 350 m), and it can have extended periods, up to 3 days, without feeding activity.

ecology↗

bioRxiv2018

Distinguishing between parallel and serial processing in visual attention from neurobiological data

Serial and parallel processing in visual search have been long debated in psychology but the processing mechanism remains an open issue. Serial processing allows only one object at a time to be processed, whereas parallel processing assumes that various objects are processed simultaneously. Here we present novel neural models for the two types of processing mechanisms based on analysis of simultaneously recorded spike trains using electrophysiological data from prefrontal cortex of rhesus monkeys while processing task-relevant visual displays. We combine mathematical models describing neuronal attention and point process models for spike trains. The same model can explain both serial and parallel processing by adopting different parameter regimes. We present statistical methods to distinguish between serial and parallel processing based on both maximum likelihood estimates and decoding the momentary focus of attention when two stimuli are presented simultaneously. Results show that both processing mechanisms are in play for the simultaneously recorded neurons, but neurons tend to follow parallel processing in the beginning after the onset of the stimulus pair, whereas they tend to serial processing later on. This could be explained by parallel processing being related to sensory bottom-up signals or feedforward processing, which typically occur in the beginning after stimulus onset, whereas top-down signals related to cognitive modulatory influences guiding attentional effects in recurrent feedback connections occur after a small delay, and is related to serial processing, where all processing capacities are being directed towards the attended object.\n\nAuthor summaryA fundamental question concerning processing of visual objects in our brain is how a population of cortical cells respond when presented with more than a single object in their receptive fields. Is one object processed at a time (serial processing), or are all objects processed simultaneously (parallel processing)? Inferring the dynamics of attentional states in simultaneously recorded spike trains from sensory neurons while being exposed to a pair of visual stimuli is key to advance our understanding of visual cognition. We propose novel statistical models and measures to quantify and follow the time evolution of the visual cognition processes right after stimulus onset. We find that in the beginning processing appears to be predominantly parallel, which develops into serial processing 150 - 200 ms after stimulus onset in prefrontal cortex of rhesus monkeys.

neuroscience↗