1,099 research outputs found

    Computational techniques to interpret the neural code underlying complex cognitive processes

    Get PDF
    Advances in large-scale neural recording technology have significantly improved the capacity to further elucidate the neural code underlying complex cognitive processes. This thesis aimed to investigate two research questions in rodent models. First, what is the role of the hippocampus in memory and specifically what is the underlying neural code that contributes to spatial memory and navigational decision-making. Second, how is social cognition represented in the medial prefrontal cortex at the level of individual neurons. To start, the thesis begins by investigating memory and social cognition in the context of healthy and diseased states that use non-invasive methods (i.e. fMRI and animal behavioural studies). The main body of the thesis then shifts to developing our fundamental understanding of the neural mechanisms underpinning these cognitive processes by applying computational techniques to ana lyse stable large-scale neural recordings. To achieve this, tailored calcium imaging and behaviour preprocessing computational pipelines were developed and optimised for use in social interaction and spatial navigation experimental analysis. In parallel, a review was conducted on methods for multivariate/neural population analysis. A comparison of multiple neural manifold learning (NML) algorithms identified that non linear algorithms such as UMAP are more adaptable across datasets of varying noise and behavioural complexity. Furthermore, the review visualises how NML can be applied to disease states in the brain and introduces the secondary analyses that can be used to enhance or characterise a neural manifold. Lastly, the preprocessing and analytical pipelines were combined to investigate the neural mechanisms in volved in social cognition and spatial memory. The social cognition study explored how neural firing in the medial Prefrontal cortex changed as a function of the social dominance paradigm, the "Tube Test". The univariate analysis identified an ensemble of behavioural-tuned neurons that fire preferentially during specific behaviours such as "pushing" or "retreating" for the animal’s own behaviour and/or the competitor’s behaviour. Furthermore, in dominant animals, the neural population exhibited greater average firing than that of subordinate animals. Next, to investigate spatial memory, a spatial recency task was used, where rats learnt to navigate towards one of three reward locations and then recall the rewarded location of the session. During the task, over 1000 neurons were recorded from the hippocampal CA1 region for five rats over multiple sessions. Multivariate analysis revealed that the sequence of neurons encoding an animal’s spatial position leading up to a rewarded location was also active in the decision period before the animal navigates to the rewarded location. The result posits that prospective replay of neural sequences in the hippocampal CA1 region could provide a mechanism by which decision-making is supported

    Modern computing: Vision and challenges

    Get PDF
    Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

    Integrating statistical and machine learning approaches to identify receptive field structure in neural populations

    Full text link
    Neural coding is essential for understanding how the activity of individual neurons or ensembles of neurons relates to cognitive processing of the world. Neurons can code for multiple variables simultaneously and neuroscientists are interested in classifying neurons based on the variables they represent. Building a model identification paradigm to identify neurons in terms of their coding properties is essential to understanding how the brain processes information. Statistical paradigms are capable of methodologically determining the factors influencing neural observations and assessing the quality of the resulting models to characterize and classify individual neurons. However, as neural recording technologies develop to produce data from massive populations, classical statistical methods often lack the computational efficiency required to handle such data. Machine learning (ML) approaches are known for enabling efficient large scale data analysis; however, they require huge training data sets, and model assessment and interpretation are more challenging than for classical statistical methods. To address these challenges, we develop an integrated framework, combining statistical modeling and machine learning approaches to identify the coding properties of neurons from large populations. In order to evaluate our approaches, we apply them to data from a population of neurons in rat hippocampus and prefrontal cortex (PFC), to characterize how spatial learning and memory processes are represented in these areas. The data consist of local field potentials (LFP) and spiking data simultaneously recorded from the CA1 region of hippocampus and the PFC of a male Long Evans rat performing a spatial alternation task on a W-shaped track. We have examined this data in three separate but related projects. In one project, we build an improved class of statistical models for neural activity by expanding a common set of basis functions to increase the statistical power of the resulting models. In the second project, we identify the individual neurons in hippocampus and PFC and classify them based on their coding properties by using statistical model identification methods. We found that a substantial proportion of hippocampus and PFC cells are spatially selective, with position and velocity coding, and rhythmic firing properties. These methods identified clear differences between hippocampal and prefrontal populations, and allowed us to classify the coding properties of the full population of neurons in these two regions. For the third project, we develop a supervised machine learning classifier based on convolutional neural networks (CNNs), which use classification results from statistical models and additional simulated data as ground truth signals for training. This integration of statistical and ML approaches allows for statistically principled and computationally efficient classification of the coding properties of general neural populations

    Organic Bioelectronics Development in Italy: A Review

    Get PDF
    In recent years, studies concerning Organic Bioelectronics have had a constant growth due to the interest in disciplines such as medicine, biology and food safety in connecting the digital world with the biological one. Specific interests can be found in organic neuromorphic devices and organic transistor sensors, which are rapidly growing due to their low cost, high sensitivity and biocompatibility. This trend is evident in the literature produced in Italy, which is full of breakthrough papers concerning organic transistors-based sensors and organic neuromorphic devices. Therefore, this review focuses on analyzing the Italian production in this field, its trend and possible future evolutions

    Type 3 adenylyl cyclase, neuronal primary cilia, and hippocampus-dependent memory formation

    Get PDF
    Primary cilia are microtubule-based cellular antennae present in most vertebrate cells including neurons. Neuronal primary cilia have abundant expression of G-protein coupled receptors (GPCRs) and downstream cAMP signaling components such as type 3 adenylyl cyclase (AC3). The deflects of neuronal cilia is associated with many memory-related disorders, such as intellectual disability. Thus far, little is known about how neuronal primary cilia regulate neuronal activity and affect hippocampal memory formation. Episodic memory is thought to be encoded by sparsely distributed memory-eligible neurons in the hippocampus and neocortex. However, it is not clear how memory-eligible neurons interact with one another to form and retrieve a memory. The objectives of my dissertation are to determine the roles of AC3 in regulating cortical protein phosphorylation, to examine the cellular mechanism of episodic memory formation, and to examine how neuronal primary cilia regulate trace fear memory formation. Project 1: Compare protein phosphorylation levels in the prefrontal cortex between AC3 knockout (KO) and wildtype (WT) mice. AC3 represents a key enzyme mediating ciliary cAMP signaling in neurons and is genetically associated with major depressive disorder (MDD) and autism spectrum disorders (ASD). The major downstream effector protein of cAMP in cells is protein kinase A (PKA), whose activation leads to the phosphorylation of numerous proteins to propagate the signaling downstream. In my mass spectrometry-based phosphoproteomic study using conditional AC3 KO mice, I identified thousands of peptides from prefrontal cortical tissues, some of which are differentially phosphorylated in AC3 WT and KO samples. In addition, this effort led to identification of over two hundred proteins, whose phosphorylation were sex-biased. Surprisingly, a high percentage of these targets (31%) are autism-associated proteins/genes. Hence, this study provides the first phosphoproteomic evidence suggesting that sex-biased protein phosphorylation may contribute to the sexual dimorphism of autism. Project 2: Investigate how hippocampal neurons are recruited to interact with each other to encode a trace fear memory. Using in vivo calcium imaging in freely behaving mice, I found that a small portion of highly active hippocampal neurons (termed primed neurons) are actively engaged in memory formation and retrieval. I found that induction of activity synchronization among primed neurons from random dynamics is critical for trace memory formation and retrieval. My work has provided direct in vivo evidence to challenge the long-held paradigm that activation and re-activation of memory cells encodes and retrieves memory, respectively. These findings support a new mechanistic model for associative memory formation, in that primed neurons connect with each other to forge a new circuit, bridging a conditional stimulus with an unconditional stimulus. Project 3: Develop an analytical method to identify primed neurons and determine the roles of neuronal primary cilia on hippocampal neuronal priming and trace memory formation. Neuronal primary cilia are “cellular antennae” which sense and transduce extracellular signals into neuronal soma. However, to date little is known about how neuronal primary cilia influence neuronal functions and hippocampal memory. I utilized conditional Ift88 knockout mice (to ablate cilia) as loss-of-function models. I found that inducible conditional Ift88 KOs display more severe learning deficits compared to their littermate controls. Cilia-ablated mice showed reduced overall neuronal activity, decreased number of primed neurons, and failed to form burst synchronization. These data support the conclusion that alteration of neuronal primary cilia impairs trace fear memory by decreasing hippocampal neuronal priming and the formation of burst synchronization. This study also provides evidence to support the importance of burst synchronization among primed neurons on memory formation and retrieval

    Exploring space situational awareness using neuromorphic event-based cameras

    Get PDF
    The orbits around earth are a limited natural resource and one that hosts a vast range of vital space-based systems that support international systems use by both commercial industries, civil organisations, and national defence. The availability of this space resource is rapidly depleting due to the ever-growing presence of space debris and rampant overcrowding, especially in the limited and highly desirable slots in geosynchronous orbit. The field of Space Situational Awareness encompasses tasks aimed at mitigating these hazards to on-orbit systems through the monitoring of satellite traffic. Essential to this task is the collection of accurate and timely observation data. This thesis explores the use of a novel sensor paradigm to optically collect and process sensor data to enhance and improve space situational awareness tasks. Solving this issue is critical to ensure that we can continue to utilise the space environment in a sustainable way. However, these tasks pose significant engineering challenges that involve the detection and characterisation of faint, highly distant, and high-speed targets. Recent advances in neuromorphic engineering have led to the availability of high-quality neuromorphic event-based cameras that provide a promising alternative to the conventional cameras used in space imaging. These cameras offer the potential to improve the capabilities of existing space tracking systems and have been shown to detect and track satellites or ‘Resident Space Objects’ at low data rates, high temporal resolutions, and in conditions typically unsuitable for conventional optical cameras. This thesis presents a thorough exploration of neuromorphic event-based cameras for space situational awareness tasks and establishes a rigorous foundation for event-based space imaging. The work conducted in this project demonstrates how to enable event-based space imaging systems that serve the goals of space situational awareness by providing accurate and timely information on the space domain. By developing and implementing event-based processing techniques, the asynchronous operation, high temporal resolution, and dynamic range of these novel sensors are leveraged to provide low latency target acquisition and rapid reaction to challenging satellite tracking scenarios. The algorithms and experiments developed in this thesis successfully study the properties and trade-offs of event-based space imaging and provide comparisons with traditional observing methods and conventional frame-based sensors. The outcomes of this thesis demonstrate the viability of event-based cameras for use in tracking and space imaging tasks and therefore contribute to the growing efforts of the international space situational awareness community and the development of the event-based technology in astronomy and space science applications

    Consumer Neuroscience e Brand Relationship: misurare l’associazione implicita tra il Sé del consumatore e il brand.

    Get PDF
    Il presente elaborato si focalizza sulla connessione tra Consumer Neuroscience e Brand Relationship con un focus specifico sul Sé del consumatore, analizzato attraverso uno strumento di misurazione indiretta del comportamento. L’obiettivo è stato quello di contribuire alla validazione e all’utilizzo nel contesto italiano di un SC-IAT per lo studio dell’associazione tra Sé e brand, interpretandone i risultati tramite un’analisi di matrice neuroscientifica su stimoli brand-related. Il vantaggio di questo strumento, rispetto allo IAT tradizionale, è quello di poter ‘fotografare’ un’istantanea sulla relazione senza la necessità di utilizzare una dimensione comparativa. Misurando direttamente la forza dell’associazione tra il concetto del brand e quello del Sé. Per farlo, l’autore è passato attraverso fasi distinte che hanno prima indagato gli aspetti puramente psicometrici dello strumento, per dedicarsi successivamente a un test neuroscientifico. I risultati hanno evidenziato delle buone performance del SC-IAT, così pensato, suggerendo approfondimenti futuri e applicazioni a brand dalla differente architettura. Inoltre, l’analisi neurofisiologica ha evidenziato come lo strumento possa risultare efficace nel fornire un’interpretazione aggiuntiva agli indicatori neurofisiologici testati durante la visualizzazione di uno stimolo relativo al brand
    corecore