Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.

/pdf/model-agnostic-meta-learning-for-fast-adaptation-of-deep-1uogjkn6mb.pdf

Model-agnostic meta-learning for fast adaptation of deep networks

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks which they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on the MNIST hand written digit dataset and by learning several Atari 2600 games sequentially.

Overcoming catastrophic forgetting in neural networks

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially.

https://www.pnas.org/content/pnas/114/13/3521.full.pdf

中枢神経系疾患の治療は正常細胞（ニューロン）の機能維持を目的とするが，脳血管障害のように機能障害の原因が細胞の死滅に基づくことは多い．一方，脳腫瘍の治療においては薬物療法や放射線療法といった腫瘍細胞の死滅を目標とするものが大きな位置を占める．いずれの場合にも，細胞死の機序を理解することは各種病態や治療法の理解のうえで重要である．現在のところ最も研究の進んでいる細胞死の型はアポトーシスである．そのなかで重要な位置を占めるミトコンドリアにおける反応および抗アポトーシス因子について概要を紹介する．

Neuroscience 細胞死：最近の知見

Dopamine (DA) releasing neurons in the midbrain learn response patterns that represent reward prediction error (RPE). Typically, models proposing a mechanistic explanation for how dopamine neurons learn to exhibit RPE are based on temporal difference (TD) learning, a machine learning algorithm. However, mechanistic models motivated by TD learning face two significant hurdles. First, TD based models typically require rather unrealistic components, such as long and robust temporal chains of feature specific neurons which tile, a priori, each interval from a given stimulus to a given reward. Secondly, various predictions of TD clash with experimental observations of how RPE evolves over learning. Here, we present a biophysically plausible plastic network model of spiking neurons, that learns RPEs and can replicate results observed in multiple experiments. This model, coined FLEX ( F lexibly L earned E rrors in E x pected Reward), learns feature specific representations of time, allowing for neural representations of stimuli to adjust their timing and relation to rewards in an online manner. Following learning, model dopamine neurons in FLEX report a distribution of response types, as observed experimentally and as used in machine learning. Dopamine firing in our model reflects an RPE before and after learning but not necessarily during learning, allowing the model to reconcile seemingly inconsistent experiments and make unique predictions that contrast those of TD.

Learning and Expression of Dopaminergic Reward Prediction Error via Plastic Representations of Time

This perspective piece came about through the Generative Adversarial Collaboration (GAC) series of workshops organized by the Computational Cognitive Neuroscience (CCN) conference in 2020. We brought together a number of experts from the field of theoretical neuroscience to debate emerging issues in our understanding of how learning is implemented in biological recurrent neural networks. Here, we will give a brief review of the common assumptions about biological learning and the corresponding findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks commonly used in artificial intelligence. We will then outline the key issues discussed in the workshop: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. Finally, we conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help to bring clarity to these issues.

CCN GAC Workshop: Issues with learning in biological recurrent neural networks.

Neural activity is often low dimensional and dominated by only a few prominent neural covariation patterns. It has been hypothesised that these covariation patterns could form the building blocks used for fast and flexible motor control. Supporting this idea, recent experiments have shown that monkeys can learn to adapt their neural activity in motor cortex on a timescale of minutes, given that the change lies within the original low-dimensional subspace, also called neural manifold. However, the neural mechanism underlying this within-manifold adaptation remains unknown. Here, we show in a computational model that modification of recurrent weights, driven by a learned feedback signal, can account for the observed behavioural difference between within- and outside-manifold learning. Our findings give a new perspective, showing that recurrent weight changes do not necessarily lead to change in the neural manifold. On the contrary, successful learning is naturally constrained to a common subspace.

/pdf/neural-manifold-under-plasticity-in-a-goal-driven-learning-p9nvkb5rpz.pdf

Neural manifold under plasticity in a goal driven learning behaviour

Our understanding of hearing and speech recognition rests on controlled experiments requiring simple stimuli. However, these stimuli often lack the characteristics of complex sounds such as speech. We propose an approach that combines neural modelling with machine learning to determine relevant low-level auditory features. Our approach bridges the gap between detailed neuronal models that capture specific auditory responses, and research on the statistics of real-world speech data and speech recognition. First, we introduce a feature detection model with a modest number of parameters that is compatible with auditory physiology. In order to objectively determine relevant feature detectors within the model parameter space, the model is tested in a speech classification task, using a simple classifier that approximates the information bottleneck. This framework allows us to determine the best model parameters and their neurophysiological and psychoacoustic implications. We show that our model can capture a variety of well-studied features (such as amplitude modulations and onsets) and allows us to unify concepts from different areas of hearing research. Our approach has various potential applications. Firstly, it could lead to new, testable experimental hypotheses for understanding hearing. Moreover, promising features could be directly applied as a new acoustic front-end for speech recognition systems.

A Unifying Framework for Neuro-Inspired, Data-Driven Detection of Low-Level Auditory Features

The predictive coding hypothesis proposes that top-down predictions are compared with incoming bottom-up sensory information, with prediction errors signaling the discrepancies between these inputs. While this hypothesis explains the presence of prediction errors, recent experimental studies suggest that prediction error signals can emerge within a local circuit, that is, from bottom-up sensory input alone. In this paper, we test whether local circuits alone can generate predictive signals by training a recurrent spiking network using local plasticity rules. Our network model replicates prediction errors resembling various experimental results, such as a biphasic pattern of prediction errors and context-specific representation of error signals. Our findings shed light on how synaptic plasticity can shape prediction errors and enables the acquisition and updating of an internal model of sensory input within a recurrent neural network.

/pdf/learning-predictive-signals-within-a-local-recurrent-circuit-2fu11jfn.pdf

Claudia Clopath

Papers

Learning and Expression of Dopaminergic Reward Prediction Error via Plastic Representations of Time

CCN GAC Workshop: Issues with learning in biological recurrent neural networks.

Neural manifold under plasticity in a goal driven learning behaviour

A Unifying Framework for Neuro-Inspired, Data-Driven Detection of Low-Level Auditory Features

Learning predictive signals within a local recurrent circuit