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Recognizing how climate change will impact populations can aid in making decisions about approaches for conservation of endangered species. The Blunt-nosed Leopard Lizard (Gambelia sila) is a federally endangered species that, despite protection, remains in extremely arid, hot areas and may be at risk of extirpation due to climate change. We collected data on the field-active body temperatures, preferred body temperatures, and upper thermal tolerance of G. sila. We then described available thermal habitat using biophysical models, which allowed us to (1) describe patterns in lizard body temperatures, microhabitat temperatures, and lizard microhabitat use, (2) quantify the lizards’ thermoregulatory accuracy, (3) calculate the number of hours they are currently thermally restricted in microhabitat use, (4) project how the number of restricted hours will change in the future as ambient temperatures rise, and (5) assess the importance of Giant Kangaroo Rat burrows and shade-providing shrubs in the current and projected future thermal ecology of G. sila. Lizards maintained fairly consistent daytime body temperatures over the course of the active season, and use of burrows and shrubs increased as the season progressed and ambient temperatures rose. During the hottest part of the year, lizards shuttled among kangaroo rat burrows, shrubs, and open habitat to maintain body temperatures below their upper thermal tolerance, but occasionally, higher than their preferred body temperature range. Lizards are restricted from staying in the open habitat for 75% of daylight hours and are forced to seek refuge under shrubs or burrows to avoid surpassing their upper thermal threshold. After applying climatic projections of 1 and 2˚C increases to 2018 ambient temperatures, G. sila will lose additional hours of activity time that could compound stressors faced by this population, potentially leading to extirpation. Finally, temperature-based activity estimation (TBAE) is an automated method for predicting surface activity and microhabitat use based on the temperature of an organism and its habitat. In an attempt to lessen impacts on sensitive species and costs, we assessed continuously logged field active body temperatures as a tool to predict the surface activity and microhabitat use of an endangered lizard (Blunt-nosed Leopard Lizard, Gambelia sila). We found that TBAE accurately predicts whether a lizard is above or below ground 75.7% of the time when calculated using air temperature, and 60.5% of the time when calculated using biophysical models. While surface activity was correctly predicted about 93% of the time using either method, accuracy in predicting below ground (burrow) occupancy was 62% for air temperature and 51% for biophysical models. Using biophysical model data, TBAE accurately predicts microhabitat use in 79% of observations in which lizards are in the sun, 47% in the shade, and 51% in burrows. Heliotherms bask in the sun, and thus body temperatures can shift rapidly when the animal moves to a new microhabitat. This sensitivity, makes TBAE a promising means of remotely monitoring animal activity, particularly for specific variables like emergence time and surface activity.

During neocortical development, network activity undergoes a dramatic transition from largely synchronized, so-called cluster activity, to a relatively sparse pattern around the time of eye-opening in rodents. Biophysical mechanisms underlying this sparsification phenomenon remain poorly understood. Here, we present a dynamic systems modeling study of a developing neural network that provides the first mechanistic insights into sparsification. We find that the rest state of immature networks is strongly affected by the dynamics of a transient, unstable state hidden in their firing activities, allowing these networks to either be silent or generate large cluster activity. We address how, and which, specific developmental changes in neuronal and synaptic parameters drive sparsification. We also reveal how these changes refine the information processing capabilities of an in vivo developing network, mainly by showing a developmental reduction in the instability of network’s firing activity, an effective availability of inhibition-stabilized states, and an emergence of spontaneous attractors and state transition mechanisms. Furthermore, we demonstrate the key role of GABAergic transmission and depressing glutamatergic synapses in governing the spatiotemporal evolution of cluster activity. These results, by providing a strong link between experimental observations and model behavior, suggest how adult sparse coding networks may emerge developmentally.

Dans ce projet nous nous intéresserons à la création d’un nouveau modèle permettant de simuler des enregistrements extracellulaires de l’activité électrique neurale dans le système nerveux périphérique. Ce modèle fut développé pour permettre une meilleure compréhension de l’impact des différentes propriétés des interfaces sur la qualité des signaux recueillis. Ce projet fut en particulier conduit pour répondre au contexte actuel qui voit le développement de nombreuses applications dans le domaine des neuro-prothèses et autres interfaces neurales à but biomédical. Nos intentions étaient de fournir un nouvel outil permettant de mieux comprendre les particularités des interfaces existantes ou d’aider à leur amélioration et à la planification de futures innovations. Ce modèle est construit comme la synthèse de la compréhension actuelle des différents rouages biophysiques impactant les enregistrements. Sa structure peut être perçue comme l’assemblage de différents sous-systèmes interconnectés et représentant chacun une dimension du processus. Il s’avère particulièrement efficace pour l’analyse comparative des performances entre diffèrent types/géométries d’électrodes invasives. Dans ce document, nous nous efforcerons d’expliquer en détail la structure et les paramètres de notre modèle. Nous décrirons ensuite les différents tests que nous avons entrepris pour sa validation expérimentale, ainsi que les différentes voies d’applications que nous avons commencé à explorer. Nous finirons par décrire les améliorations qui nous sont apparues comme nécessaires ou possibles et par une discussion sur les ouvertures futures offerte à ce domaine de recherche. / Neural interfaces are becoming a newly dynamic and promising field especially thanks to the numerous applications they could have in the biomedical domain. A great deal of these applications requires a monitoring of targeted neural activity. Among the different technologies available for such recording practice, chronic electrodes implanted in the peripheral nervous system offer a good compromise on the resolution versus invasiveness technological constraint. A large array of electrodes has been developed in this intention but there is still only a limited comprehension of their recording principles and weakness. This makes difficult any targeted improvement of the electrodes and led this field to be mainly dominated by a trial and error empirical approach simultaneously costly in funds, animal lives and time. In particular, intrafascicular electrodes, while providing exiting results for stimulation, have often failed in recordings. These electrodes typically show interesting recording performance right after implantation but have rapid decline of their efficacy up to the points that they often become useless after a few weeks. Such performance proves problematic as they drastically limit the transfer of experimental results to human applications. The extent of our work has been the development of a theoretical framework for the study of implantable electrodes. Our goal here has been to construct a model that could be used as a platform to better understand implanted electrode and compare their performance and possible improvement. We focused our work on intrafascicular electrode for the peripheral nervous system. However, our procedure could easily be applied to other type of interface. During this project we first constructed a detailed model of the recording biophysical process happening at the peripheral nerve electrical interface. This model encompasses all the mechanism known to influence the quality and shape of neural activity recordings. We have then recreated within our model specific controlled experiments and by comparing the properties of the simulated recording with their experimental counterparts demonstrated the potency of our approach to produce bio-plausible signals. This validated our model as an in silico alternative to compare and test electrodes. We then further developed this model to also simulate some of the changes happening in the nerve post implantation. In particular, we found that the growth of the fibrotic scar could already explain a large part of the signal degradation happening in the first weeks. Then to demonstrate the adaptability of this model we used it to compare the performance of the main type of electrodes implanted nowadays peripherally. Finally, as the main weakness of our model relied in its relative complexity and the related long computing time, we started to analyze how this model could be simplified without losing the precision necessary for the intended applications. In conclusion, this project led to the creation of a model which in its current form can be used as an in silico platform to test and compare electrodes. This will facilitate the planning and development of future peripheral neural interface by proving both more economical and informative that current strategies. Conjointly, we opened the way to future improvement of our model, leading to more practicality.

During neocortical development, network activity undergoes a dramatic transition from largely synchronized, so-called cluster activity, to a relatively sparse pattern around the time of eye-opening in rodents. Biophysical mechanisms underlying this sparsification phenomenon remain poorly understood. Here, we present a dynamic systems modeling study of a developing neural network that provides the first mechanistic insights into sparsification. We find that the rest state of immature networks is strongly affected by the dynamics of a transient, unstable state hidden in their firing activities, allowing these networks to either be silent or generate large cluster activity. We address how, and which, specific developmental changes in neuronal and synaptic parameters drive sparsification. We also reveal how these changes refine the information processing capabilities of an in vivo developing network, mainly by showing a developmental reduction in the instability of network’s firing activity, an effective availability of inhibition-stabilized states, and an emergence of spontaneous attractors and state transition mechanisms. Furthermore, we demonstrate the key role of GABAergic transmission and depressing glutamatergic synapses in governing the spatiotemporal evolution of cluster activity. These results, by providing a strong link between experimental observations and model behavior, suggest how adult sparse coding networks may emerge developmentally.