524 research outputs found

    Implementation of Arithmetic Operations by SN P Systems with Communication on Request

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    Spiking neural P systems (SN P systems, for short) are a class of distributed and parallel computing devices inspired from the way neurons communicate by means of spikes. In most of the SN P systems investigated so far, the system communicates on command, and the application of evolution rules depends on the contents of a neuron. However, inspired from the parallel-cooperating grammar systems, it is natural to consider the opposite strategy: the system communicates on request, which means spikes are requested from neighboring neurons, depending on the contents of the neuron. Therefore, SN P systems with communication on request were proposed, where the spikes should be moved from a neuron to another one when the receiving neuron requests that. In this paper, we consider implementing arithmetical operations by means of SN P systems with communication on request. Specifically, adder, subtracter and multiplier are constructed by using SN P systems with communication on request

    All-optical interrogation of neural circuits during behaviour

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    This thesis explores the fundamental question of how patterns of neural activity encode information and guide behaviour. To address this, one needs three things: a way to record neural activity so that one can correlate neuronal responses with environmental variables; a flexible and specific way to influence neural activity so that one can modulate the variables that may underlie how information is encoded; a robust behavioural paradigm that allows one to assess how modulation of both environmental and neural variables modify behaviour. Techniques combining all three would be transformative for investigating which features of neural activity, and which neurons, most influence behavioural output. Previous electrical and optogenetic microstimulation studies have told us much about the impact of spatially or genetically defined groups of neurons, however they lack the flexibility to probe the contribution of specific, functionally defined subsets. In this thesis I leverage a combination of existing technologies to approach this goal. I combine two-photon calcium imaging with two-photon optogenetics and digital holography to generate an “all-optical” method for simultaneous reading and writing of neural activity in vivo with high spatio-temporal resolution. Calcium imaging allows for cellular resolution recordings from neural populations. Two-photon optogenetics allows for targeted activation of individual cells. Digital holography, using spatial light modulators (SLMs), allows for simultaneous photostimulation of tens to hundreds of neurons in arbitrary spatial locations. Taken together, I demonstrate that this method allows one to map the functional signature of neurons in superficial mouse barrel cortex and to target photostimulation to functionally-defined subsets of cells. I develop a suite of software that allows for quick, intuitive execution of such experiments and I combine this with a behavioural paradigm testing the effect of targeted perturbations on behaviour. In doing so, I demonstrate that animals are able to reliably detect the targeted activation of tens of neurons, with some sensitive to as few as five cortical cells. I demonstrate that such learning can be specific to targeted cells, and that the lower bound of perception shifts with training. The temporal structure of such perturbations had little impact on behaviour, however different groups of neurons drive behaviour to different extents. In order to probe which characteristics underly such variation, I tested whether the sensory response strength or correlation structure of targeted ensembles influenced their behavioural salience. Whilst these final experiments were inconclusive, they demonstrate their feasibility and provide us with some key actionable improvements that could further strengthen the all-optical approach. This thesis therefore represents a significant step forward towards the goal of combining high resolution readout and perturbation of neural activity with behaviour in order to investigate which features of the neural code are behaviourally relevant

    A neurobiological and computational analysis of target discrimination in visual clutter by the insect visual system.

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    Some insects have the capability to detect and track small moving objects, often against cluttered moving backgrounds. Determining how this task is performed is an intriguing challenge, both from a physiological and computational perspective. Previous research has characterized higher-order neurons within the fly brain known as 'small target motion detectors‘ (STMD) that respond selectively to targets, even within complex moving surrounds. Interestingly, these cells still respond robustly when the velocity of the target is matched to the velocity of the background (i.e. with no relative motion cues). We performed intracellular recordings from intermediate-order neurons in the fly visual system (the medulla). These full-wave rectifying, transient cells (RTC) reveal independent adaptation to luminance changes of opposite signs (suggesting separate 'on‘ and 'off‘ channels) and fast adaptive temporal mechanisms (as seen in some previously described cell types). We show, via electrophysiological experiments, that the RTC is temporally responsive to rapidly changing stimuli and is well suited to serving an important function in a proposed target-detecting pathway. To model this target discrimination, we use high dynamic range (HDR) natural images to represent 'real-world‘ luminance values that serve as inputs to a biomimetic representation of photoreceptor processing. Adaptive spatiotemporal high-pass filtering (1st-order interneurons) shapes the transient 'edge-like‘ responses, useful for feature discrimination. Following this, a model for the RTC implements a nonlinear facilitation between the rapidly adapting, and independent polarity contrast channels, each with centre-surround antagonism. The recombination of the channels results in increased discrimination of small targets, of approximately the size of a single pixel, without the need for relative motion cues. This method of feature discrimination contrasts with traditional target and background motion-field computations. We show that our RTC-based target detection model is well matched to properties described for the higher-order STMD neurons, such as contrast sensitivity, height tuning and velocity tuning. The model output shows that the spatiotemporal profile of small targets is sufficiently rare within natural scene imagery to allow our highly nonlinear 'matched filter‘ to successfully detect many targets from the background. The model produces robust target discrimination across a biologically plausible range of target sizes and a range of velocities. We show that the model for small target motion detection is highly correlated to the velocity of the stimulus but not other background statistics, such as local brightness or local contrast, which normally influence target detection tasks. From an engineering perspective, we examine model elaborations for improved target discrimination via inhibitory interactions from correlation-type motion detectors, using a form of antagonism between our feature correlator and the more typical motion correlator. We also observe that a changing optimal threshold is highly correlated to the value of observer ego-motion. We present an elaborated target detection model that allows for implementation of a static optimal threshold, by scaling the target discrimination mechanism with a model-derived velocity estimation of ego-motion. Finally, we investigate the physiological relevance of this target discrimination model. We show that via very subtle image manipulation of the visual stimulus, our model accurately predicts dramatic changes in observed electrophysiological responses from STMD neurons.Thesis (Ph.D.) - University of Adelaide, School of Molecular and Biomedical Science, 200

    Towards model-based control of Parkinson's disease

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    Modern model-based control theory has led to transformative improvements in our ability to track the nonlinear dynamics of systems that we observe, and to engineer control systems of unprecedented efficacy. In parallel with these developments, our ability to build computational models to embody our expanding knowledge of the biophysics of neurons and their networks is maturing at a rapid rate. In the treatment of human dynamical disease, our employment of deep brain stimulators for the treatment of Parkinson’s disease is gaining increasing acceptance. Thus, the confluence of these three developments—control theory, computational neuroscience and deep brain stimulation—offers a unique opportunity to create novel approaches to the treatment of this disease. This paper explores the relevant state of the art of science, medicine and engineering, and proposes a strategy for model-based control of Parkinson’s disease. We present a set of preliminary calculations employing basal ganglia computational models, structured within an unscented Kalman filter for tracking observations and prescribing control. Based upon these findings, we will offer suggestions for future research and development

    Network dynamics in the neural control of birdsong

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    Sequences of stereotyped actions are central to the everyday lives of humans and animals, from the kingfisher's dive to the performance of a piano concerto. Lashley asked how neural circuits managed this feat nearly 6 decades ago, and to this day it remains a fundamental question in neuroscience. Toward answering this question, vocal performance in the songbird was used as a model to study the performance of learned, stereotyped motor sequences. The first component of this work considers the song motor cortical zone HVC in the zebra finch, an area that sends precise timing signals to both the descending motor pathway, responsible for stereotyped vocal performance in the adult, and the basal ganglia, which is responsible for both motor variability and song learning. Despite intense interest in HVC, previous research has exclusively focused on describing the activity of small numbers of neurons recorded serially as the bird sings. To better understand HVC network dynamics, both single units and local field potentials were sampled across multiple electrodes simultaneously in awake behaving zebra finches. The local field potential and spiking data reveal a stereotyped spatio-temporal pattern of inhibition operating on a 30 ms time-scale that coordinates the neural sequences in principal cells underlying song. The second component addresses the resilience of the song circuit through cutting the motor cortical zone HVC in half along one axis. Despite this large-scale perturbation, the finch quickly recovers and sings a near-perfect song within a single day. These first two studies suggest that HVC is functionally organized to robustly generate neural dynamics that enable vocal performance. The final component concerns a statistical study of the complex, flexible songs of the domesticated canary. This study revealed that canary song is characterized by specific long-range correlations up to 7 seconds long-a time-scale more typical of human music than animal vocalizations. Thus, the neural sequences underlying birdsong must be capable of generating more structure and complexity than previously thought

    Bayesian inference in neural circuits and synapses

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    Bayesian inference describes how to reason optimally under uncertainty. As the brain faces considerable uncertainty, it may be possible to understand aspects of neural computation using Bayesian inference. In this thesis, I address several questions within this broad theme. First, I show that con dence reports may, in some circumstances be Bayes optimal, by taking a \doubly Bayesian" strategy: computing the Bayesian model evidence for several di erent models of participant's behaviour, one of which is itself Bayesian. Second, I address a related question concerning features of the probability distributions realised by neural activity. In particular, it has been show that neural activity obeys Zipf's law, as do many other statistical distributions. We show the emergence of Zipf's law is in fact unsurprising, as it emerges from the existence of an underlying latent variable: ring rate. Third, I show that synaptic plasticity can be formulated as a Bayesian inference problem, and I give neural evidence in support of this proposition, based on the hypothesis that neurons sample from the resulting posterior distributions. Fourth, I consider how oscillatory excitatory-inhibitory circuits might perform inference by relating these circuits to a highly effective method for probabilistic inference: Hamiltonian Monte Carlo

    Cortical circuits for visual processing and epileptic activity propagation

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    The thesis focuses on the relationship between cortical connectivity and cortical function. The first part investigates how the fine scale connectivity between visual neurons determines their functional responses during physiological sensory processing. The second part ascertains how the mesoscopic scale connectivity between brain areas constrains the spread of abnormal activity during the propagation of focal cortical seizures. Part 1: Neurons in the primary visual cortex (V1) are tuned to retinotopic location, orientation and direction of motion. Such selectivity stems from the integration of inputs from hundreds of presynaptic neurons distributed across cortical layers. Yet, the functional principles that organize such presynaptic networks have only begun to be understood. To uncover them, I used monosynaptic rabies virus tracing to target a single pyramidal neuron in L2/3 (starter neuron) and trace its presynaptic partners. I combined this approach with two-photon microscopy in V1 to investigate the relationship between the activity of the starter cell, its presynaptic neurons and the surrounding excitatory population across cortical layers in awake animals. Part 2: Focal epilepsy involves excessive and synchronous cortical activity that propagates both locally and distally. Does this propagation follow the same functional circuits as normal cortical activity? I induced focal seizures in primary visual cortex (V1) of awake mice, and compared their propagation to the retinotopic organization of V1 and higher visual areas. I measured activity through simultaneous local field potential recordings and widefield calcium imaging, and observed prolonged seizures that were orders of magnitude larger than normal visual responses. I demonstrate that seizure start as standing waves (synchronous elevated activity in the focal V1 region and in corresponding retinotopic locations in higher areas) and then propagate both locally and into distal regions. These regions matched each other in retinotopy. I conclude that seizure propagation respects the connectivity underlying normal visual processing
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