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

Repetitive activation of subpopulation of neurons in cortical networks leads to the formation of neuronal assemblies, which can guide learning and behavior. Recent technological advances have made the artificial induction of such assemblies feasible, yet how various patterns of activation can shape their emergence in different operating regimes is not clear. Here we studied this question in large-scale cortical networks composed of excitatory (E) and inhibitory (I) neurons. We found that the dynamics of the network in which neuronal assemblies are embedded is important for their induction. In networks with strong E-E coupling at the border of E-I balance, increasing the number of perturbed neurons enhanced the potentiation of ensembles. This was, however, accompanied by off-target potentiation of connections from unperturbed neurons. When strong E-E connectivity was combined with dominant E-I interactions, formation of ensembles became specific. Counter-intuitively, increasing the number of perturbed neurons in this regime decreased the average potentiation of individual synapses, leading to an optimal assembly formation at intermediate sizes. This was due to potent lateral inhibition in this regime, which also slowed down the formation of neuronal assemblies, resulting in a speed-accuracy trade-off in the performance of the networks in pattern completion and behavioral discrimination. Our results therefore suggest that the two regimes might be suited for different cognitive tasks, with fast regimes enabling crude detections and slow but specific regimes favoring finer discriminations. Functional connectivity inferred from recent experiments in mouse cortical networks seems to be consistent with the latter regime, but we show that recurrent and top-down mechanisms can dynamically modulate the networks to switch between different states. Our work provides a framework to study how neuronal perturbations can lead to network-wide plasticity under biologically realistic conditions, and sheds light on the design of future experiments to optimally induce behaviorally relevant neuronal assemblies.

/pdf/excitatory-inhibitory-balance-modulates-the-formation-and-21fbs65gn2.pdf

Excitatory-inhibitory balance modulates the formation and dynamics of neuronal assemblies in cortical networks

Connections between neurons are called synapses. Their strength is defined as the voltage amplitude, or slope, of a postsynpatic neuron response to a presynaptic action potential. The synapses can change in strength, i.e., they are plastic, and these changes can operate on different time scales. Long-term plasticity denotes the synaptic changes that last more than 20-30 minutes (see Fig. 1). This is opposed to short-term plasticity that lasts hundreds of milliseconds.

Long-Term Plasticity, Biophysical Models.

Real-life behavioral tasks are often complex and depend on abstract combinations of sensory stimuli and internal logic. To successfully learn these tasks, animals must pair actions or decisions to the task’s complex structure. The hippocampus has been shown to contain fields which represent complex environmental and task variables, including place, lap, evidence accumulation, etc. Altogether, these representations have been hypothesized to form a “cognitive map” which encodes the complex real-world structure underlying behavior. However, it is still unclear how biophysical plasticity mechanisms at the single cell level can lead to the population-wide evolution of task-relevant maps. In this work we present a biophysically plausible model comprised of a recurrent hippocampal network and an action network, in which the latent representational structure co-evolves with behavior in a task-dependent manner. We demonstrate that the network develops latent structures that are needed for solving the task and does not integrate latent structures which do not support task learning. We show that, in agreement with experimental data, cue-dependent “splitters” can only be induced at the single cell level if the task requires a split representation to solve. Finally, our model makes specific predictions on how biases in behavior result from experimentally testable biases in the underlying latent representation.

/pdf/latent-representations-in-hippocampal-network-model-co-ksydpqms.pdf

Latent Representations in Hippocampal Network Model Co-Evolve with Behavioral Exploration of Task Structure

Long-Term Plasticity, Biophysical Models

Precise and reliable spike times are thought to subserve multiple possible functions, including improving the accuracy of encoding stimuli or behaviours relative to other coding schemes. Indeed, repeating sequences of spikes with sub-millisecond precision exist in nature, such as the synfire chain of spikes in area HVC of the zebra-finch mating-song circuit. Here, we analyzed what impact precise and reliable spikes have on the encoding accuracy for both the zebra-finch and more generic neural circuits using computational modelling. Our results show that neural circuits can use precisely timed spikes to encode signals with a higher-order accuracy than a conventional rate code. Circuits with precisely timed and reliably emitted spikes increase their encoding accuracy linearly with network size, which is the hallmark signature of an efficient code. This qualitatively differs from circuits that employ a rate code which increase their encoding accuracy with the square-root of network size. However, this improved scaling is dependent on the spikes becoming more accurate and more reliable with larger networks. Finally, we discuss how to test this scaling relationship in the zebra mating song circuit using both neural data and song-spectrogram-based recordings while taking advantage of the natural fluctuation in HVC network size due to neurogenesis. The zebra-finch mating-song circuit may represent the most likely candidate system for the use of spike-timing-based, efficient coding strategies in nature.

/pdf/does-the-zebra-finch-mating-song-circuit-use-spike-times-1o80wbjqaw.pdf

Claudia Clopath

Papers

Excitatory-inhibitory balance modulates the formation and dynamics of neuronal assemblies in cortical networks

Long-Term Plasticity, Biophysical Models.

Latent Representations in Hippocampal Network Model Co-Evolve with Behavioral Exploration of Task Structure

Long-Term Plasticity, Biophysical Models

Does the Zebra Finch Mating Song Circuit Use Spike Times Efficiently