Trophic Transfer of Macroalgal Fatty Acids in Two Urchin Species: Digestion, Egestion, and Tissue Building

Sea urchins are ecosystem engineers of nearshore benthic communities because of their influence on the abundance and distribution of macroalgal species. Urchins are notoriously inefficient in assimilation of their macroalgal diets, so their fecal production can provide a nutritional subsidy to benthic consumers that cannot capture and handle large macroalgae. We studied the assimilation of macroalgal diets by urchins by analyzing the profiles of trophic biomarkers such as fatty acids (FAs). We tracked macroalgal diet assimilation in both Strongylocentrotus droebachiensis and S. purpuratus. Juvenile S. droebachiensis and adult S. purpuratus were maintained for 180 and 70 days, respectively, on one of three monoculture diets from three algal phyla: Nereocystis luetkeana, Pyropia sp., or Ulva sp. We then analyzed FA profiles of the macroalgal tissue fed to urchins as well as urchin gonad, gut, digesta, and egesta (feces) to directly evaluate trophic modification and compare nutritional quality of urchin food sources, urchin tissues, and fecal subsidies. In the S. purpuratus assay, there were significantly more total lipids in the digesta and egesta than in the algae consumed. The FA profiles of urchin tissues differed among urchin species, all diets, and tissue types. Despite these differences, we observed similar patterns in the relationships between the urchin and macroalgal tissues for both species. Egesta produced by urchins fed each of the three diets were depleted with respect to the concentration of important long chain polyunsaturated fatty acids (LCPUFAs), but did not differ significantly from the source alga consumed. Both urchin species were shown to synthesize and selectively retain both the precursor and resulting LCPUFAs involved in the synthesis of the LCPUFAs 20:4ω6 and 20:5ω3. S. droebachiensis and S. purpuratus exhibited consistent patterns in the respective depletion and retention of precursor FAs and resulting LCPUFAs of Pyropia and Ulva tissues, suggesting species level control of macroalgal digestion or differential tissue processing by gut microbiota. For both S. droebachiensis and S. purpuratus, macroalgal diet was a surprisingly strong driver of urchin tissue fatty acids; this indicates the potential of fatty acids for future quantitative trophic estimates of urchin assimilation of algal phyla in natural settings

Neuromorphic reinforcement learning

Living organisms are able to successfully perform challeng- ing tasks such as perception, classification, association, and control. In hope for similar successes in artificial systems, neuromorphic engineering uses neurophysiological models of perception and information processing in biological sys- tems to emulate their functions but also resemble their struc- ture [1]. In this abstract, we focus on the basal ganglia (BG), brain region in control of primitive functions of the nervous system, and specifically on their involvement in action selec- tion and reinforcement learning (RL). We hypothesize that neuromorphic-inspired systems will greatly benefit the RL community

The role of feedback in maintaining robustness and modulation across scales: Insights from cellular and network neurophysiology

The brain is a complex system made of many components acting at very dif- ferent resolution levels, from the microsecond and nanometer scales with ion channels to hours and brain-wide scale with proteins. The brain dynamics and functions emerge from the interactions between these resolution levels. Math- ematical modeling is a powerful ally to uncover some of the brain organizing principles and mechanisms. From this perspective, the question of which cel- lular details must be retained at the network level is largely open. Motifs simplify systems by approximating the wiring diagram and by taking advantage of the timescale separation between processes. Yet, motifs study each resolution level separately and neglect couplings between levels. This approach falls short of system-level questions and multiresolution intrinsic properties. The present dissertation aims at narrowing the gap by looking at the inter- play between resolution levels. We propose to extract essential elements, in the form of feedback loops, to be maintained from one resolution to the next in the hope of a better understanding of brain functions and diseases. The focus is on the spatiotemporal upscaling from the neuron to the network level and, in particular, on the maintenance of modulation and robustness properties across scales. This approach is used in a two-neuron network and is extended to a prospective multiresolution excitability framework. The main contributions of this dissertation are the following. We identify the key role of a cellular feedback loop for network oscillation robustness and modulation. Rhythms are crucial in the brain functioning but much awaits to be understood regarding their control, regulation, and function. In a mutually-inhibitory network, we isolate an essential cellular property—a positive feedback loop in the slow timescale—to be retained at the network level to ensure modulation and robustness of network oscillations. We highlight the peculiar role that a cellular feedback loop can play for the regulation of network switches. We identify that a cellular positive feedback loop brings localization properties, both temporally and spatially, to network oscillations. The emerging picture suggests a basal ganglia network model valid both in healthy movement-related oscillations and in parkinsonian conditions. Multiresolution excitability emerges due to localization properties of ex- citable systems: different excitability resolution windows can be superposed and interact, generating multiresolution systems. In each window, the system is characterized via its transfer properties and input-output behavior. Signal processing properties appear in these multiresolution systems and endow mul- tiresolution objects with gating and multiplex signaling capabilities. In conclusion, the present dissertation provides novel insights on the impor- tance of the interplay between cellular and network levels. This multiresolution motif perspective is thought to be general and not specific to neuroscience. Fi- nally, exploiting the concept in multiresolution technologies is suggested.Le cerveau est un système complexe composé d’éléments actifs à des niveaux de résolution très différents, de la microseconde et nanomètre avec les canaux ioniques, à l’heure et l’échelle du cerveau avec les protéines. La dynamique et les fonctions du cerveau émergent des interactions entre les différents niveaux de résolution. La modélisation mathématique est un allié puissant pour dévoiler certains des principes organisationnels et mécanismes cérébraux. Dans ce contexte, la question de savoir quels détails cellulaires doivent être conservés au niveau du réseau reste largement ouverte. Les motifs décomplexifient les systèmes en simplifiant le schéma de connexion et en exploitant la séparation des échelles de temps entre processus. Cependant, les motifs étudient chaque niveau séparément et négligent les couplages entre niveaux. Cette approche passe à coté des questions systémiques et des proprié- tés intrinsèques de multirésolution. Cette thèse a pour but de rapprocher les deux domaines en étudiant les in- teractions entre niveaux de résolution. Nous proposons d’extraire les éléments principaux, sous la forme de boucles de feedback, à maintenir d’une résolu- tion à la suivante dans l’espoir d’une meilleure compréhension des functions et maladies cérébrales. L’accent est placé sur le changement d’échelle spatiotemporelle, du niveau neuronal au niveau réseau, et en particulier sur le maintien des propriétés de modulation et de robustesse à travers les échelles. Cette approche est utilisée dans le cas particulier d’un réseau de deux neurones et est étendue à un cadre théorique plus spéculatif d’excitabilité multirésolution. Les principales contributions de cette thèse sont les suivantes. Nous identifions le rôle clé joué par une boucle de feedback cellulaire pour la modulation et la robustesse des oscillations réseaux. Les rythmes sont cruciaux pour le fonctionnement du cerveau mais leurs contrôles, régulations, et fonc- tions sont loin d’être compris. Dans un réseau avec inhibition mutuelle, nous isolons une propriété cellulaire essentielle—une boucle de feedback positive dans l’échelle lente—à maintenir au niveau réseau afin d’assurer des oscillations réseaux modulables et robustes. Nous soulignons le rôle particulier qu’une boucle de feedback cellulaire peut jouer dans la régulation des interrupteurs réseaux. Nous identifions qu’une boucle de feedback positive apporte des propriétés de localisation, à la fois temporellement et spatialement, aux oscillations réseaux. Ces propriétés suggèrent un nouveau modèle réseau des ganglions de la base, valide dans l’état sain pour les oscillations liées au mouvement ainsi que dans l’état parkinsonien. L’excitabilité multirésolution émerge dû aux propriétés de localisation des systèmes excitables : des fenêtres d’excitabilité de résolution différente sont superposées et interagissent, créant des systèmes multirésolutions. Dans chaque fenêtre, le système est caractérisé par ses propriétés de transfert et par son com- portement entrée-sortie. Des propriétés de traitement du signal apparaissent dans ces systèmes multirésolutions et dotent les objets multirésolutions de ca- pacités de blocage et de multiplexage des signaux. En résumé, cette thèse offre un nouvel aperçu de l’importance du couplage entre le niveau cellulaire et réseau. Le concept de motif multirésolution semble être général et non limité aux neurosciences. Enfin, l’exploitation du concept en technologies multirésolutions est suggérée

Contrasting the role of Ih and ICaT currents in post-inhibitory rebound mechanisms in reciprocal-inhibitory networks

Models with reciprocal inhibition are ubiquitous in the literature. For instance, common rhythmic motor behaviors produced by central pattern generators (CPGs) involve half-center oscillators, which consist of two inhibitory neurons that are not endogenous oscillators, but produce rhythmic outputs when reciprocally connected (Marder & Calabrese 1996). Models of thalamocortical spindle oscillations also suggest that the rhythm originates from the thalamic reticular nucleus, which consists in interacting inhibitory nonoscillatory neurons (Wang & Rinzel 1992)

Kalman-filter based decoder in spiking neural networks

The initial focus of the project is on implementing an existing Kalman-filter based decoder algorithm that controls a 2D computer cursor [GIL2010] into Neurogrids 2D silicon- neuron arrays. Neurogrid realizes such functions at negligible energetic cost [BOA2010]. This document gives the necessary background for a Kalman-filter based decoder implementation in a spiking neurl network (SNN)