Spatio-temporal variation of macrofaunal assemblages in the deep Blanes submarine canyon

The spatio-temporal variations in diversity and abundance of deep-sea macrofaunal assemblages (excluding meiofaunal taxa, as Nematoda, Copepoda and Ostracoda) from the Blanes Canyon (BC) and adjacent open slope are described. The Catalan Sea basin is characterized by the presence of numerous submarine canyons, which are globally acknowledged as biodiversity hot-spots, due to their disturbance regime and incremented conveying of organic matter. This area is subjected to local deep-sea fisheries activities, and to recurrent cold water cascading events from the shelf. The upper canyon (~900 m), middle slope (~1200 m) and lower slope (~1500 m) habitats were investigated during three different months (October 2008, May 2009 and September 2009). A total of 624 specimens belonging to 16 different taxa were found into 67 analyzed samples, which had been collected from the two study areas. Of these, Polychaeta, Mollusca and Crustacea were always the most abundant groups. As expected, the patterns of species diversity and evenness were different in time and space. Both in BC and open slope, taxa diversity and abundance are higher in the shallowest depth and lowest at -1500 m depth. This is probably due to different trophic regimes at these depths. The abundance of filter-feeders is higher inside BC than in the adjacent open slope, which is also related with an increment of predator polychaetes. Surface deposit-feeders are more abundant in the open slope than in BC, along with a decrement of filter-feeders and their predators. Probably these differences are due to higher quantities of suspended organic matter reaching the canyon. The multivariate analyses conducted on major taxa point out major differences effective taxa richness between depths and stations. In September 2009 the analyzed communities double their abundances, with a corresponding increase in richness of taxa. This could be related to a mobilizing event, like the release of accumulated food-supply in a nepheloid layer associated to the arrival of autumn. The highest abundance in BC is detected in the shallowest depth and in late summer (September), probably due to higher food availability caused by stronger flood events coming from Tordera River. The effects of such events seemed to involve adjacent open slope too. The nMDS conducted on major taxa abundance shows a slight temporal difference between the three campaigns samples, with a clear clustering between samples of Sept 09. All depth and all months were dominated by Polychaeta, which have been identified to family level and submitted to further analysis. Family richness have clearly minimum at the -1200 m depth of BC, highlighting the presence of a general impact affecting the populations in the middle slope. Three different matrices have been created, each with a different taxonomic level (All Taxa “AT”, Phylum Level “PL” and Polychaeta Families “PF”). Multivariate analysis (MDS, SIMPER) conducted on PL matrix showed a clear spatial differences between stations (BC and open slope) and depths. MDSs conducted on other two matrices (AT and PF) showed similar patterns, but different from PL analysis. A 2 nd stage analysis have been conducted to understand differences between different taxonomic levels, and PL level has been chosen as the most representative of variation. The faunal differences observed were explained by depth, station and season. All work has been accomplished in the Centre d’estudis avançats de Blanes (CEAB-CSIC), within the framework of Spanish PROMETEO project "Estudio Integrado de Cañones y Taludes PROfundos del MEdiTErráneo Occidental: un hábitat esencial", Ref. CTM2007-66316-C02- 01/MAR.

Spatio-temporal models of the functional architecture of the visual cortex

In this work I tried to explore many aspects of cognitive visual science, each one based on different academic fields, proposing mathematical models capable to reproduce both neuro-physiological and phenomenological results that were described in the recent literature. The structure of my thesis is mainly composed of three chapters, corresponding to the three main areas of research on which I focused my work. The results of each work put the basis for the following, and their ensemble form an homogeneous and large-scale survey on the spatio-temporal properties of the architecture of the visual cortex of mammals.

A Bayesian changepoint analysis on spatio-temporal point processes

Changepoint analysis is a well established area of statistical research, but in the context of spatio-temporal point processes it is as yet relatively unexplored. Some substantial differences with regard to standard changepoint analysis have to be taken into account: firstly, at every time point the datum is an irregular pattern of points; secondly, in real situations issues of spatial dependence between points and temporal dependence within time segments raise. Our motivating example consists of data concerning the monitoring and recovery of radioactive particles from Sandside beach, North of Scotland; there have been two major changes in the equipment used to detect the particles, representing known potential changepoints in the number of retrieved particles. In addition, offshore particle retrieval campaigns are believed may reduce the particle intensity onshore with an unknown temporal lag; in this latter case, the problem concerns multiple unknown changepoints. We therefore propose a Bayesian approach for detecting multiple changepoints in the intensity function of a spatio-temporal point process, allowing for spatial and temporal dependence within segments. We use Log-Gaussian Cox Processes, a very flexible class of models suitable for environmental applications that can be implemented using integrated nested Laplace approximation (INLA), a computationally efficient alternative to Monte Carlo Markov Chain methods for approximating the posterior distribution of the parameters. Once the posterior curve is obtained, we propose a few methods for detecting significant change points. We present a simulation study, which consists in generating spatio-temporal point pattern series under several scenarios; the performance of the methods is assessed in terms of type I and II errors, detected changepoint locations and accuracy of the segment intensity estimates. We finally apply the above methods to the motivating dataset and find good and sensible results about the presence and quality of changes in the process.

Assessment of gait spatio-temporal parameters in neurological disorders using wearable inertial sensors

Movement analysis carried out in laboratory settings is a powerful, but costly solution since it requires dedicated instrumentation, space and personnel. Recently, new technologies such as the magnetic and inertial measurement units (MIMU) are becoming widely accepted as tools for the assessment of human motion in clinical and research settings. They are relatively easy-to-use and potentially suitable for estimating gait kinematic features, including spatio-temporal parameters. The objective of this thesis regards the development and testing in clinical contexts of robust MIMUs based methods for assessing gait spatio-temporal parameters applicable across a number of different pathological gait patterns. First, considering the need of a solution the least obtrusive as possible, the validity of the single unit based approach was explored. A comparative evaluation of the performance of various methods reported in the literature for estimating gait temporal parameters using a single unit attached to the trunk first in normal gait and then in different pathological gait conditions was performed. Then, the second part of the research headed towards the development of new methods for estimating gait spatio-temporal parameters using shank worn MIMUs on different pathological subjects groups. In addition to the conventional gait parameters, new methods for estimating the changes of the direction of progression were explored. Finally, a new hardware solution and relevant methodology for estimating inter-feet distance during walking was proposed. Results of the technical validation of the proposed methods at different walking speeds and along different paths against a gold standard were reported and showed that the use of two MIMUs attached to the lower limbs associated with a robust method guarantee a much higher accuracy in determining gait spatio-temporal parameters. In conclusion, the proposed methods could be reliably applied to various abnormal gaits obtaining in some cases a comparable level of accuracy with respect to normal gait.

Spatio-temporal diversity of Megistozoobenthos in the Antalya Gulf and relationships with environmental features

Antalya Gulf is situated in the Levantine Sea, the second biggest and most eastern basin in the Mediterranean Sea. This area is an ultra-oligotrophic basin, strongly affected by anthropogenic inputs, in particular in the fishing areas. For this characteristic, in the Levantine Sea, there is a strong pressure on the natural resources and benthic assemblages. Furthermore, many alien species enter from Suez Canal and are well established in the area. All these pressures are leading to a degradation of the Levantine Sea. For this reason it is important to have tools to study and monitoring the functioning of the marine ecosystem. Benthic organisms are superior to many other biological groups for their response to environmental stresses. The variability of benthic assemblages on a site can reflect, in an integrative mode, the entire functioning of the marine ecosystem. In this study, that wants to analyze the spatial and temporal distribution of the benthic macrofaunal assemblages of Antalya Gulf, 90 benthic species divided in 8 taxa (Annelida, Cnidaria, Echinodermata, Echiura, Mollusca, Porifera, Sipunculida and Tunicata) were found. All the analyses conducted on the entire benthic class and later on Mollusca and Echinodermata separately highlighted the importance of depth on structuring benthic community.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

Spatio-temporal credit assignment in population learning

We present a model for plasticity induction in reinforcement learning which is based on a cascade of synaptic memory traces. In the cascade of these so called eligibility traces presynaptic input is first corre lated with postsynaptic events, next with the behavioral decisions and finally with the external reinforcement. A population of leaky integrate and fire neurons endowed with this plasticity scheme is studied by simulation on different tasks. For operant co nditioning with delayed reinforcement, learning succeeds even when the delay is so large that the delivered reward reflects the appropriateness, not of the immediately preceeding response, but of a decision made earlier on in the stimulus - decision sequence . So the proposed model does not rely on the temporal contiguity between decision and pertinent reward and thus provides a viable means of addressing the temporal credit assignment problem. In the same task, learning speeds up with increasing population si ze, showing that the plasticity cascade simultaneously addresses the spatial problem of assigning credit to the different population neurons. Simulations on other task such as sequential decision making serve to highlight the robustness of the proposed sch eme and, further, contrast its performance to that of temporal difference based approaches to reinforcement learning.

Spatio-Temporal Credit Assignment in Neuronal Population Learning

n learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.

Point Process Methodology for On-line Spatio-temporal Disease Surveillance

The AEGISS (Ascertainment and Enhancement of Gastrointestinal Infection Surveillance and Statistics) project aims to use spatio-temporal statistical methods to identify anomalies in the space-time distribution of non-specific, gastrointestinal infections in the UK, using the Southampton area in southern England as a test-case. In this paper, we use the AEGISS project to illustrate how spatio-temporal point process methodology can be used in the development of a rapid-response, spatial surveillance system. Current surveillance of gastroenteric disease in the UK relies on general practitioners reporting cases of suspected food-poisoning through a statutory notification scheme, voluntary laboratory reports of the isolation of gastrointestinal pathogens and standard reports of general outbreaks of infectious intestinal disease by public health and environmental health authorities. However, most statutory notifications are made only after a laboratory reports the isolation of a gastrointestinal pathogen. As a result, detection is delayed and the ability to react to an emerging outbreak is reduced. For more detailed discussion, see Diggle et al. (2003). A new and potentially valuable source of data on the incidence of non-specific gastro-enteric infections in the UK is NHS Direct, a 24-hour phone-in clinical advice service. NHS Direct data are less likely than reports by general practitioners to suffer from spatially and temporally localized inconsistencies in reporting rates. Also, reporting delays by patients are likely to be reduced, as no appointments are needed. Against this, NHS Direct data sacrifice specificity. Each call to NHS Direct is classified only according to the general pattern of reported symptoms (Cooper et al, 2003). The current paper focuses on the use of spatio-temporal statistical analysis for early detection of unexplained variation in the spatio-temporal incidence of non-specific gastroenteric symptoms, as reported to NHS Direct. Section 2 describes our statistical formulation of this problem, the nature of the available data and our approach to predictive inference. Section 3 describes the stochastic model. Section 4 gives the results of fitting the model to NHS Direct data. Section 5 shows how the model is used for spatio-temporal prediction. The paper concludes with a short discussion.