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

Sensory and motor learning reorganizes neocortical circuitry, particularly manifested in the strength of excitatory synapses. Prior studies suggest reduced inhibition can facilitate glutamatergic synapse plasticity during learning, but the role of specific inhibitory neurons in this process has not been well-documented. Here we investigate whether inhibition from parvalbumin (PV)-expressing neurons is altered in primary somatosensory cortex in mice trained in a whisker-based reward-association task. Anatomical and electrophysiological analyses show PV input to L2/3, but not L5, pyramidal (Pyr) neurons is rapidly suppressed during early stages of sensory training, effects that are reversed after longer training periods. Importantly, sensory stimulation without reward does not alter PV-mediated inhibition. Computational modeling indicates that reduced PV inhibition in L2/3 selectively enables an increase in translaminar recurrent activity, also observed during SAT. PV disinhibition in superficial layers of the neocortex may be one of the earliest changes in learning-dependent rewiring of the cortical column. Impact statement Tactile learning is associated with reduced PV inhibition in superficial layers of somatosensory cortex. Modeling studies suggest that PV disinhibition can support prolonged recurrent activity initiated by thalamic input.

/pdf/transient-and-layer-specific-reduction-in-neocortical-pv-nrxa885hwb.pdf

Transient and layer-specific reduction in neocortical PV inhibition during sensory association learning

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased towards the average of past observations. It is assumed to be an optimal strategy by the brain, and commonly thought of as an expression of the brain’s ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed, and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats, suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience – producing short-term sensory history biases – naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli, and are not due to a gradual shift of the memory towards the sensory distribution’s mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

/pdf/from-recency-to-central-tendency-biases-in-working-memory-a-rg4khn6u.pdf

From recency to central tendency biases in working memory: a unifying network model

Standard models for Spike Timing-Dependent Plasticity (STDP) take into account pair interactions of presynaptic and postsynaptic spike times [1, 2]. However, a number of pairing experiments [3, 4] show that pair interactions are not sufficient to capture Long-Term Potentation and Depression (LTP/LTD). Moreover, by construction, simple spike-based model fail in voltage clamp experiments, where LTP or LTD can be induced by coincidence of presynaptic spikes arrival with depolarization of the postsynaptic membrane [4], [5]. Here we present a triplet model which takes into account the presynaptic spike times and the postsynaptic membrane potential, filtered with three different time constants. For spike induced experiments, this model can formally be reduced to the triplet rule proposed by Pfister et al. [6], and yields similar results to, for example, frequency dependent pairing experiment by Sjöström et al. [4] (Fig 1). Moreover, it also reproduces the behavior of the ABS rule [5], i.e. no synaptic changes are observed under presynaptic stimulation when the postsynaptic potential is hyperpolarized; while small depolarization leads to LTD and strong depolarization to LTP (Fig 2). Additionally, this model can describe the hybrid experiment by Sjöström et al. where low-frequency potentiation that is normally absent in pair STDP experiments is rescued by depolarization [4]. This novel model can be used with hard bound, soft bound or any other arbitrary weight dependence.

A unified voltage-based model for STDP, LTP and LTD

The brain learns an internal model of the environment through sensory experiences, which is essential for high-level cognitive processes. Recent studies show that spontaneous activity reflects such learned internal model. Although computational studies have proposed that Hebbian plasticity can learn the switching dynamics of replayed activities, it is still challenging to learn dynamic spontaneous activity that obeys the statistical properties of sensory experience. Here, we propose a pair of biologically plausible plasticity rules for excitatory and inhibitory synapses in a recurrent spiking neural network model to embed stochastic dynamics in spontaneous activity. The proposed synaptic plasticity rule for excitatory synapses seeks to minimize the discrepancy between stimulus-evoked and internally predicted activity, while inhibitory plasticity maintains the excitatory-inhibitory balance. We show that the spontaneous reactivation of cell assemblies follows the transition statistics of the model’s evoked dynamics. We also demonstrate that simulations of our model can replicate recent experimental results of spontaneous activity in songbirds, suggesting that the proposed plasticity rule might underlie the mechanism by which animals learn internal models of the environment. Significance Statement While spontaneous activity in the brain is often seen as simple background noise, recent work has hypothesized that spontaneous activity instead reflects the brain’s learnt internal model. While several studies have proposed synaptic plasticity rules to generate structured spontaneous activities, the mechanism of learning and embedding transition statistics in spontaneous activity is still unclear. Using a computational model, we investigate the synaptic plasticity rules that learn dynamic spontaneous activity obeying appropriate transition statistics. Our results shed light on the learning mechanism of the brain’s internal model, which is a crucial step towards a better understanding of the role of spontaneous activity as an internal generative model of stochastic processes in complex environments.

/pdf/embedding-stochastic-dynamics-of-the-environment-in-26zx93rx.pdf

Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity

Memories are thought to be stored in neural ensembles known as engrams that are specifically reactivated during memory recall. Recent studies have found that memory engrams of two events that happened close in time tend to overlap in the hippocampus and the amygdala, and these overlaps have been shown to support memory linking. It has been hypothesised that engram overlaps arise from the mechanisms that regulate memory allocation itself, involving neural excitability, but the exact process remains unclear. Indeed, most theoretical studies focus on synaptic plasticity and little is known about the role of intrinsic plasticity, which could be mediated by neural excitability and serve as a complementary mechanism for forming memory engrams. Here, we developed a rate-based recurrent neural network that includes both synaptic plasticity and neural excitability. We obtained structural and functional overlap of memory engrams for contexts that are presented close in time, consistent with experimental studies. Moreover, we showed that enhancing the initial excitability of a subset of neurons just before presenting a context biases the memory allocation to these neurons. We then explored the role of inhibition as a way of controlling competition among neurons from two ensembles. This work suggests mechanisms underlying the role of intrinsic excitability in memory allocation and linking, and yields predictions regarding the dynamics of memory engrams.

/pdf/intrinsic-neural-excitability-induces-time-dependent-overlap-2hl0dw2f.pdf

Claudia Clopath

Papers

Transient and layer-specific reduction in neocortical PV inhibition during sensory association learning

From recency to central tendency biases in working memory: a unifying network model

A unified voltage-based model for STDP, LTP and LTD

Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity

Intrinsic neural excitability induces time-dependent overlap of memory engrams