861 research outputs found

    Development of variable and robust brain wiring patterns in the fly visual system

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    Precise generation of synapse-specific neuronal connections are crucial for establishing a robust and functional brain. Neuronal wiring patterns emerge from proper spatiotemporal regulation of axon branching and synapse formation during development. Several neuropsychiatric and neurodevelopmental disorders exhibit defects in neuronal wiring owing to synapse loss and/or dys-regulated axon branching. Despite decades of research, how the two inter-dependent cellular processes: axon branching and synaptogenesis are coupled locally in the presynaptic arborizations is still unclear. In my doctoral work, I investigated the possible role of EGF receptor (EGFR) activity in coregulating axon branching and synapse formation in a spatiotemporally restricted fashion, locally in the medulla innervating Dorsal Cluster Neuron (M- DCN)/LC14 axon terminals. In this work I have explored how genetically encoded EGFR randomly recycles in the axon branch terminals, thus creating an asymmetric, non-deterministic distribution pattern. Asymmetric EGFR activity in the branches acts as a permissive signal for axon branch pruning. I observed that the M-DCN branches which stochastically becomes EGFR ‘+’ during development are synaptogenic, which means they can recruit synaptic machineries like Syd1 and Bruchpilot (Brp). My work showed that EGFR activity has a dual role in establishing proper M-DCN wiring; first in regulating primary branch consolidation possibly via actin regulation prior to synaptogenesis. Later in maintaining/protecting the levels of late Active Zone (AZ) protein Brp in the presynaptic branches by suppressing basal autophagy level during synaptogenesis. When M-DCNs lack optimal EGFR activity, the basal autophagy level increases resulting in loss of Brp marked synapses which is causal to increased exploratory branches and post-synaptic target loss. Lack of EGFR activity affects the M-DCN wiring pattern that makes adult flies more active and behave like obsessive compulsive in object fixation assay. In the second part of my doctoral work, I have asked how non-genetic factors like developmental temperature affects adult brain wiring. To test that, I increased or decreased rearing temperature which is known to inversely affect pupal developmental rate. We asked if all the noisy cellular processes of neuronal assembly: filopodial dynamics, axon branching, synapse formation and postsynaptic connections scale up or down accordingly. I observed that indeed all the cellular processes slow down at lower developmental temperature and vice versa, which changes the DCN wiring pattern accordingly. Interestingly, behavior of flies adapts to their developmental temperature, performing best at the temperature they have been raised at. This shows that optimal brain function is an adaptation of robust brain wiring patterns which are specified by noisy developmental processes. In conclusion, my doctoral work helps us better understand the developmental regulation of axon branching and synapse formation for establishing precise brain wiring pattern. We need all the cell intrinsic developmental processes to be highly regulated in space and time. It is infact a combinatorial effect of such stochastic processes and external factors that contribute to the final outcome, a functional and robust adult brain

    Evaluating wild and commercial populations of Bombus terrestris ssp. audax (Harris, 1780): from genotype to phenotype.

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    Bees, including bumblebees, are highly valued for the pollination services they provide to natural ecosystems and agricultural crops. However, many bee species are facing declines, likely a result of habitat loss, pesticide use and climate change. Additionally, the use of imported commercial bumblebee colonies for crop pollination poses several risks to wild pollinators, including competition, hybridisation and pathogen spillover. A stock-take is needed of wild bees on both genetic and functional levels to identify vulnerable populations, detect local adaptations and to prevent further pollinator losses. We examine wild Irish B. terrestris ssp. audax on genomic, proteomic, and behavioural levels with reference to British and commercial populations to deepen our understanding of the selective processes acting on wild and domesticated bumblebee populations. We find that wild Irish and British populations of B. t. audax are distinctive on genomic levels and exhibit differential signatures of selection. We also find putative evidence for genetic distinctions between wild and commercial populations. A genomic examination of canonical immune genes in wild, Irish bumblebees highlighted several genes undergoing positive, purifying and possibly balancing selection, possibly reflecting their functional diversity and indicating recent adaptation. We uncover distinctions in the proteomes of wild and commercial lineages of lab-reared worker bee fat bodies and brains, as well as in the proteomic responses of these organs to pesticide exposure and infection. Finally, distinctions in the growth dynamics of wild and commercial lineages of B. t. audax colonies were identified alongside differences in the bacterial and fungal gut microbiomes of lab-reared wild and commercial workers. Overall, the findings of this thesis provide novel insights into the genetic, physiological, and behavioural distinctions between wild and domesticated populations of B. t. audax which will likely have major implications for how we conserve valuable genetic resources and manage commercial bumblebee imports

    Neural information processing in the Drosophila motion vision pathway

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    Detecting the direction of image motion is an essential component of visual computation. An individual photoreceptor, however, does not explicitly represent the direction in which the image is shifting. Comparing neighboring photoreceptor signals over time is used to extract directional motion information from the photoreceptor array in the circuit downstream. To implement direction selectivity, two opposing models have been proposed. In both models, one input line is asymmetrically delayed compared to the other, followed by a non-linear interaction between the two input lines. The Hassenstein-Reichardt (HR) model proposes an enhancement in the preferred direction (PD): the preferred side signal is delayed and then amplified by multiplying it with the other input signal. In contrast, the Barlow-Levick (BL) detector proposes a null direction (ND) suppression, whereby the null side signal is delayed and the other input is divided by it. The motion information is computed in parallel ON and OFF pathways. T4 and T5 are the first direction-selective neurons found in the ON and in the OFF pathway, respectively. Four subtypes of T4 and T5 cells exist each responding selectively to one of the four cardinal directions: front-to-back, back-to-front, upwards, and downwards, respectively. In the first manuscript, we found that both preferred direction enhancement and null direction suppression are implemented in the dendrites of all four subtypes of both T4 and T5 cells to compute the direction of motion. We, therefore, propose a hybrid model combining both PD enhancement on the preferred side and ND suppression on the null side. This combined strategy ensures a high degree of direction selectivity already at the first stage of calculating motion direction. Further processing, in addition to synaptic mechanisms on the dendrites of T4 cells, can improve the direction selectivity of the T4 cells' output signals. Such processing might involve: 1.) transformation from voltage to calcium, and 2.) from calcium to neurotransmitter release. In the second manuscript, we used in vivo two-photon imaging of genetically encoded voltage and calcium indicators, Arclight and GCaMP6f respectively, to measure responses in Drosophila direction-selective T4 neurons. Comparison between Arclight and GCaMP6f signals revealed calcium signals to have a significantly higher direction selectivity compared to voltage signals. Using these recordings we built a model which transforms T4 voltage responses into calcium responses. The model reproduced experimentally measured calcium responses across different visual stimuli using various temporal filtering steps and a stationary non-linearity. These findings provided a mechanistic underpinning of the voltage-to-calcium transformation and showed how this processing step, in addition to synaptic mechanisms on the dendrites of T4 cells, enhances direction selectivity in the output signal of T4 neurons. The two manuscripts included in this thesis are presented chronologically and were published in peer-reviewed journals

    Computational roles of cortico-cerebellar loops in temporal credit assignment

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    Animal survival depends on behavioural adaptation to the environment. This is thought to be enabled by plasticity in the neural circuit. However, the laws which govern neural plasticity are unclear. From a functional aspect, it is desirable to correctly identify, or assign “credit” for, the neurons or synapses responsible for the task decision and subsequent performance. In the biological circuit, the intricate, non-linear interactions involved in neural networks makes appropriately assigning credit to neurons highly challenging. In the temporal domain, this is known as the temporal credit assignment (TCA) problem. This Thesis considers the role the cerebellum – a powerful subcortical structure with strong error-guided plasticity rules – as a solution to TCA in the brain. In particular, I use artificial neural networks as a means to model and understand the mechanisms by which the cerebellum can support learning in the neocortex via the cortico-cerebellar loop. I introduce two distinct but compatible computational models of cortico-cerebellar interaction. The first model asserts that the cerebellum provides the neocortex predictive feedback, modeled in the form of error gradients, with respect to its current activity. This predictive feedback enables better credit assignment in the neocortex and effectively removes the lock between feedforward and feedback processing in cortical networks. This model captures observed long-term deficits associated with cerebellar dysfunction, namely cerebellar dysmetria, in both the motor and non-motor domain. Predictions are also made with respect to alignment of cortico-cerebellar activity during learning and the optimal task conditions for cerebellar contribution. The second model also looks at the role of the cerebellum in learning, but now considers its ability to instantaneously drive the cortex towards desired task dynamics. Unlike the first model, this model does not assume any local cortical plasticity need take place at all and task-directed learning can effectively be outsourced to the cerebellum. This model captures recent optogenetic studies in mice which show the cerebellum as a necessary component for the maintenance of desired cortical dynamics and ensuing behaviour. I also show that this driving input can eventually be used as a teaching signal for the cortical circuit, thereby conceptually unifying the two models. Overall, this Thesis explores the computational role of the cerebellum and cortico-cerebellar loops for task acquisition and maintenance in the brain

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Biological Protein Patterning Systems across the Domains of Life: from Experiments to Modelling

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    Distinct localisation of macromolecular structures relative to cell shape is a common feature across the domains of life. One mechanism for achieving spatiotemporal intracellular organisation is the Turing reaction-diffusion system (e.g. Min system in the bacterium Escherichia coli controlling in cell division). In this thesis, I explore potential Turing systems in archaea and eukaryotes as well as the effects of subdiffusion. Recently, a MinD homologue, MinD4, in the archaeon Haloferax volcanii was found to form a dynamic spatiotemporal pattern that is distinct from E. coli in its localisation and function. I investigate all four archaeal Min paralogue systems in H. volcanii by identifying four putative MinD activator proteins based on their genomic location and show that they alter motility but do not control MinD4 patterning. Additionally, one of these proteins shows remarkably fast dynamic motion with speeds comparable to eukaryotic molecular motors, while its function appears to be to control motility via interaction with the archaellum. In metazoa, neurons are highly specialised cells whose functions rely on the proper segregation of proteins to the axonal and somatodendritic compartments. These compartments are bounded by a structure called the axon initial segment (AIS) which is precisely positioned in the proximal axonal region during early neuronal development. How neurons control these self-organised localisations is poorly understood. Using a top-down analysis of developing neurons in vitro, I show that the AIS lies at the nodal plane of the first non-homogeneous spatial harmonic of the neuron shape while a key axonal protein, Tau, is distributed with a concentration that matches the same harmonic. These results are consistent with an underlying Turing patterning system which remains to be identified. The complex intracellular environment often gives rise to the subdiffusive dynamics of molecules that may affect patterning. To simulate the subdiffusive transport of biopolymers, I develop a stochastic simulation algorithm based on the continuous time random walk framework, which is then applied to a model of a dimeric molecular motor. This provides insight into the effects of subdiffusion on motor dynamics, where subdiffusion reduces motor speed while increasing the stall force. Overall, this thesis makes progress towards understanding intracellular patterning systems in different organisms, across the domains of life

    Mecanismos involucrados en la plasticidad sináptica de la corteza somatosensorial y de la corteza entorrinal y el giro dentado del hipocampo de ratón

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    Programa de Doctorado en NeurocienciasLínea de Investigación: Bases moleculares y celulares de la plasticidad neuronal. Mecanismos moleculares y celulares relacionados con la plasticidad neuronal a largo plazoClave Programa: DNFCódigo Línea: 94Una de las propiedades más importantes del sistema nervioso es la plasticidad. Durante el desarrollo postnatal del sistema nervioso, existen períodos críticos de plasticidad sináptica en los cuales la interacción con el ambiente es fundamental para que se lleve a cabo el reordenamiento y refinamiento correcto de las conexiones sinápticas, permitiendo así establecer los circuitos sinápticos que serán responsables de una correcta fisiología en la etapa adulta. En la presente memoria, se han estudiado los mecanismos celulares y moleculares involucrados en la plasticidad sináptica en la sinapsis establecida entre las células de la capa 4 (L4) y la capa 2/3 (L2/3) de la corteza somatosensorial primaria y la establecida entre las capas 2 (L2) y 4 (L4) de la corteza entorrinal (EC) medial (MPP) y lateral (LPP) y las células granulares (GC) del giro dentado (DG) de la formación hipocampal de ratón en diferentes etapas del desarrollo postnatal. En la sinapsis L4-L2/3 de corteza somatosensorial se ha estudiado una forma de spike timing-dependent long-term depression (t-LTD) que está presente hasta la cuarta semana postnatal (hasta P27) y que desaparece a P28. Investigando los mecanismos involucrados en la pérdida de esta forma de t-LTD durante el desarrollo se ha encontrado que esta pérdida de t-LTD está mediada por un aumento de los niveles de activación de receptores presinápticos de adenosina del tipo 1 (A1Rs) que va aumentando con la maduración postnatal. La adenosina que activa estos A1Rs es liberada por los astrocitos, por lo que esta pérdida de t-LTD con el desarrollo requiere de señalización astrocitaria. A edades mayores a P38 (P38-60), el protocolo que induce t-LTD hasta P27 induce en su lugar spike timing-dependent long-term potentiation (t-LTP). Esta t-LTP se ha encontrado que requiere de actividad en la célula postsináptica (entrada de Ca2+ a través de canales de Ca2+ dependientes de voltaje de tipo L y fabricación y liberación de óxido nítrico -NO-)., NMDAR y mGluR situados en la célula presináptica, siendo así, una forma de t-LTD de expresión presináptica. En las sinapsis L4, L2-GC de las MPP y LPP se ha descubierto que existe spike timing-dependent LTD (t-LTD) tanto cuando los axones vienen de la MPP como cuando vienen de la LPP. Estas t-LTDs son de expresión presináptica. La t-LTD de las sinapsis MPP-GC está presente hasta la tercera semana de desarrollo postnatal, requiere de receptores de tipo NMDA y mGluR presinápticos, síntesis y liberación de endocannabinoides por parte de la célula postsináptica y de actividad astrocitaria liberando glutamato, mientras que la t-LTD de las sinapsis LPP-GC no desaparece con la edad y no requiere NMDAR pero, al igual que la MPP-GC, sí requiere de mGluR presinápticos, fabricación y liberación de endocannabinoides por parte de la célula postsináptica y actividad astrocitaria liberando glutamato.Universidad Pablo de Olavide de Sevilla. Departamento de Fisiología, Anatomía y Biología Celula

    Single Biological Neurons as Temporally Precise Spatio-Temporal Pattern Recognizers

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    This PhD thesis is focused on the central idea that single neurons in the brain should be regarded as temporally precise and highly complex spatio-temporal pattern recognizers. This is opposed to the prevalent view of biological neurons as simple and mainly spatial pattern recognizers by most neuroscientists today. In this thesis, I will attempt to demonstrate that this is an important distinction, predominantly because the above-mentioned computational properties of single neurons have far-reaching implications with respect to the various brain circuits that neurons compose, and on how information is encoded by neuronal activity in the brain. Namely, that these particular "low-level" details at the single neuron level have substantial system-wide ramifications. In the introduction we will highlight the main components that comprise a neural microcircuit that can perform useful computations and illustrate the inter-dependence of these components from a system perspective. In chapter 1 we discuss the great complexity of the spatio-temporal input-output relationship of cortical neurons that are the result of morphological structure and biophysical properties of the neuron. In chapter 2 we demonstrate that single neurons can generate temporally precise output patterns in response to specific spatio-temporal input patterns with a very simple biologically plausible learning rule. In chapter 3, we use the differentiable deep network analog of a realistic cortical neuron as a tool to approximate the gradient of the output of the neuron with respect to its input and use this capability in an attempt to teach the neuron to perform nonlinear XOR operation. In chapter 4 we expand chapter 3 to describe extension of our ideas to neuronal networks composed of many realistic biological spiking neurons that represent either small microcircuits or entire brain regions

    The Timescales of Transformation Across Brain Structures in the Thalamocortical Circuit

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    Sensory processing requires reliable transmission of sensory information across multiple brain regions, from peripheral sensors, through sub-cortical structures, to sensory cortex, ultimately producing the sensory representations that drive perception and behavior. Despite decades of research, we do not yet have a mechanistic understanding of how neural representations are transformed across these critical brain structures. This is primarily due to the fact that what we know at the circuit level has been mainly derived from electrophysiological recordings targeted at single regions and upon gross anatomical connection patterns across brain regions without specific, precise knowledge of synaptic connectivity. To fill this gap in knowledge and to uncover how signaling changes across brain regions in response to changes in the sensory environment, this thesis work has two primary contributions. First, we developed a work-flow of topographic mapping and histological validation for extracellular multi-electrode recordings of neurons in the thalamocortical circuit in rodents, followed by a novel statistical approach for inferring synaptic connectivity across the brain regions. Specifically, we developed a signal-detection based classification of synaptic connectivity in the thalamus and S1 cortex, with an assessment of classification confidence that is scalable to the large-scale recording approaches that are emerging in the field. Utilizing this experimental and computational framework, we next investigated the neural mechanisms that underlie an important sensory phenomenon that emerges in this early sensory circuit: rapid sensory adaptation. While this phenomenon has been well-studied over very rapid timescales of hundreds of milliseconds, other studies suggest that longer time scales of 10’s of seconds may also be relevant. Here, we demonstrated that the thalamus and the thalamorecipient layer 4 excitatory and inhibitory neurons in S1 exhibit differential adaptation dynamics, and that the neuronal dynamics across these different regions and cell types show common signatures of multiple timescales in response to sensory adaptation. We characterized the adaptation profiles at the TC junction and further identified several mechanisms that potentially underlie the adaptation effects on the circuit dynamics, including synaptic depression of the TC synapse in identified monosynaptically connected thalamic and cortical neurons, and changes in spike timing and synchronization in the thalamic population. These mechanisms together mediate a dynamic trade-off in the theoretical detectability and discriminability of stimulus inputs. These results suggest that adaptation of the thalamocortical circuit across timescales results from a complex interaction between distinct mechanisms, and notably the engagement of different mechanisms can shift depending on the timescale of environmental changes.Ph.D
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