scholarly journals Gating by Memory: a Theory of Learning in the Cerebellum

2021 ◽  
Author(s):  
Mike Gilbert
Keyword(s):  
Purkinje Cells ◽  
Computational Simulation ◽  
Rate Code ◽  
Parallel Fibre ◽  
State Response ◽  
Proposed Model ◽  
Cell Firing ◽  
Functional States ◽  
Model Training ◽  
Synaptic Learning

AbstractThis paper presents a model of learning by the cerebellar circuit. In the traditional and dominant learning model, training teaches finely graded parallel fibre synaptic weights which modify transmission to Purkinje cells and to interneurons that inhibit Purkinje cells. Following training, input in a learned pattern drives a training-modified response. The function is that the naive response to input rates is displaced by a learned one, trained under external supervision. In the proposed model, there is no weight-controlled graduated balance of excitation and inhibition of Purkinje cells. Instead, the balance has two functional states—a switch—at synaptic, whole cell and microzone level. The paper is in two parts. The first is a detailed physiological argument for the synaptic learning function. The second uses the function in a computational simulation of pattern memory. Against expectation, this generates a predictable outcome from input chaos (real-world variables). Training always forces synaptic weights away from the middle and towards the limits of the range, causing them to polarise, so that transmission is either robust or blocked. All conditions teach the same outcome, such that all learned patterns receive the same, rather than a bespoke, effect on transmission. In this model, the function of learning is gating—that is, to select patterns that trigger output merely, and not to modify output. The outcome is memory-operated gate activation which operates a two-state balance of weight-controlled transmission. Group activity of parallel fibres also simultaneously contains a second code contained in collective rates, which varies independently of the pattern code. A two-state response to the pattern code allows faithful, and graduated, control of Purkinje cell firing by the rate code, at gated times.

scholarly journals IntroVNMT: An Introspective Model for Variational Neural Machine Translation

2020 ◽  
Vol 34 (05) ◽  
pp. 8830-8837
Author(s):  
Xin Sheng ◽  
Linli Xu ◽  
Junliang Guo ◽  
Jingchang Liu ◽  
Ruoyu Zhao ◽  
...  
Keyword(s):  
Machine Translation ◽  
Latent Variables ◽  
Image Synthesis ◽  
Target Language ◽  
Generative Adversarial Network ◽  
Neural Machine Translation ◽  
Adversarial Network ◽  
Proposed Model ◽  
Model Training ◽  
High Level

We propose a novel introspective model for variational neural machine translation (IntroVNMT) in this paper, inspired by the recent successful application of introspective variational autoencoder (IntroVAE) in high quality image synthesis. Different from the vanilla variational NMT model, IntroVNMT is capable of improving itself introspectively by evaluating the quality of the generated target sentences according to the high-level latent variables of the real and generated target sentences. As a consequence of introspective training, the proposed model is able to discriminate between the generated and real sentences of the target language via the latent variables generated by the encoder of the model. In this way, IntroVNMT is able to generate more realistic target sentences in practice. In the meantime, IntroVNMT inherits the advantages of the variational autoencoders (VAEs), and the model training process is more stable than the generative adversarial network (GAN) based models. Experimental results on different translation tasks demonstrate that the proposed model can achieve significant improvements over the vanilla variational NMT model.

Transient Response of Inductrack Systems for Maglev Transport: Part II—Solution and Dynamic Analysis

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Ruiyang Wang ◽  
Bingen Yang
Keyword(s):  
Steady State ◽  
Dynamic Analysis ◽  
Transient Response ◽  
Numerical Procedure ◽  
Steady State Response ◽  
Governing Equations ◽  
Transient Model ◽  
State Response ◽  
Proposed Model ◽  
Non Linear

Abstract In Part I of this two-part paper, a new benchmark transient model of Inductrack systems is developed. In this Part II, the proposed model, which is governed by a set of non-linear integro-differential governing equations, is used to predict the dynamic response of Inductrack systems. In the development, a state-space representation of the non-linear governing equations is established and a numerical procedure with a specific moving circuit window for transient solutions is designed. The dynamic analysis of Inductrack systems with the proposed model has two major tasks. First, the proposed model is validated through comparison with the noted steady-state results in the literature. Second, the transient response of an Inductrack system is simulated and analyzed in several typical dynamic scenarios. The steady-state response results predicted by the new model agree with those obtained in the previous studies. On the other hand, the transient response simulation results reveal that an ideal steady-state response can hardly exist in those investigated dynamic scenarios. It is believed that the newly developed transient model provides a useful tool for dynamic analysis of Inductrack systems and for in-depth understanding of the complicated electro-magneto-mechanical interactions in this type of dynamic systems.

scholarly journals Spike burst–pause dynamics of Purkinje cells regulate sensorimotor adaptation

2018 ◽  
Author(s):  
Niceto R. Luque ◽  
Francisco Naveros ◽  
Richard R. Carrillo ◽  
Eduardo Ros ◽  
Angelo Arleo
Keyword(s):  
Purkinje Cell ◽  
Purkinje Cells ◽  
Vestibular Nuclei ◽  
Response Patterns ◽  
Spike Burst ◽  
Vor Adaptation ◽  
Cell Firing ◽  
Cerebellar Purkinje Cells ◽  
Oculomotor Adaptation

AbstractCerebellar Purkinje cells mediate accurate eye movement coordination. However, it remains unclear how oculomotor adaptation depends on the interplay between the characteristic Purkinje cell response patterns, namely tonic, bursting, and spike pauses. Here, a spiking cerebellar model assesses the role of Purkinje cell firing patterns in vestibular ocular reflex (VOR) adaptation. The model captures the cerebellar microcircuit properties and it incorporates spike-based synaptic plasticity at multiple cerebellar sites. A detailed Purkinje cell model reproduces the three spike-firing patterns that are shown to regulate the cerebellar output. Our results suggest that pauses following Purkinje complex spikes (bursts) encode transient disinhibition of targeted medial vestibular nuclei, critically gating the vestibular signals conveyed by mossy fibres. This gating mechanism accounts for early and coarse VOR acquisition, prior to the late reflex consolidation. In addition, properly timed and sized Purkinje cell bursts allow the ratio between long-term depression and potentiation (LTD/LTP) to be finely shaped at mossy fibre-medial vestibular nuclei synapses, which optimises VOR consolidation. Tonic Purkinje cell firing maintains the consolidated VOR through time. Importantly, pauses are crucial to facilitate VOR phase-reversal learning, by reshaping previously learnt synaptic weight distributions. Altogether, these results predict that Purkinje spike burst-pause dynamics are instrumental to VOR learning and reversal adaptation.Author SummaryCerebellar Purkinje cells regulate accurate eye movement coordination. However, it remains unclear how cerebellar-dependent oculomotor adaptation depends on the interplay between Purkinje cell characteristic response patterns: tonic, high-frequency bursting, and post-complex spike pauses. We explore the role of Purkinje spike burst-pause dynamics in VOR adaptation. A biophysical model of Purkinje cell is at the core of a spiking network model, which captures the cerebellar microcircuit properties and incorporates spike-based synaptic plasticity mechanisms at different cerebellar sites. We show that Purkinje spike burst-pause dynamics are critical for (1) gating the vestibular-motor response association during VOR acquisition; (2) mediating the LTD/LTP balance for VOR consolidation; (3) reshaping synaptic efficacy distributions for VOR phase-reversal adaptation; (4) explaining the reversal VOR gain discontinuities during sleeping.

scholarly journals ECG Heartbeat Classification Based on an Improved ResNet-18 Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Enbiao Jing ◽  
Haiyang Zhang ◽  
ZhiGang Li ◽  
Yazhi Liu ◽  
Zhanlin Ji ◽  
...  
Keyword(s):  
State Of The Art ◽  
Classification Performance ◽  
Classification Models ◽  
Heartbeat Classification ◽  
Ecg Signals ◽  
Residual Structure ◽  
Proposed Model ◽  
Model Training ◽  
Electrocardiogram Ecg

Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better classification performance. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrate that the model achieves higher accuracy (96.50%) compared to other state-of-the-art classification models, while specifically for the ventricular ectopic heartbeat class, its sensitivity is 93.83% and the precision is 97.44%.

scholarly journals Maturation of Purkinje cell firing properties relies on neurogenesis of excitatory neurons

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Meike E van der Heijden ◽  
Elizabeth P Lackey ◽  
Ross Perez ◽  
Fatma S Ișleyen ◽  
Amanda M Brown ◽  
...  
Keyword(s):  
Purkinje Cell ◽  
Purkinje Cells ◽  
Time Window ◽  
Mouse Development ◽  
Cerebellar Neurons ◽  
Cell Functions ◽  
Excitatory Neurons ◽  
Cell Firing ◽  
Firing Properties

Preterm infants that suffer cerebellar insults often develop motor disorders and cognitive difficulty. Excitatory granule cells, the most numerous neuron type in the brain, are especially vulnerable and likely instigate disease by impairing the function of their targets, the Purkinje cells. Here, we use regional genetic manipulations and in vivo electrophysiology to test whether excitatory neurons establish the firing properties of Purkinje cells during postnatal mouse development. We generated mutant mice that lack the majority of excitatory cerebellar neurons and tracked the structural and functional consequences on Purkinje cells. We reveal that Purkinje cells fail to acquire their typical morphology and connectivity, and that the concomitant transformation of Purkinje cell firing activity does not occur either. We also show that our mutant pups have impaired motor behaviors and vocal skills. These data argue that excitatory cerebellar neurons define the maturation time-window for postnatal Purkinje cell functions and refine cerebellar-dependent behaviors.

scholarly journals Predicting Self-harm Incidents Following Inpatient Visits using Disease Comorbidity Network

2020 ◽  
Author(s):  
Zhongzhi Xu ◽  
Qingpeng Zhang ◽  
Paul Siu Fai Yip
Keyword(s):  
Deep Learning ◽  
Screening Strategy ◽  
Public Hospitals ◽  
Baseline Model ◽  
C Statistic ◽  
Proposed Model ◽  
Self Harm ◽  
Model Training ◽  
Screening And Prevention ◽  
Individual Self

AbstractSelf-harm is serious but preventable, particularly if the risk can be identified early. But the early detection of self-harm individuals is so far not satisfied. This study aims to develop and test a comorbidity network-enhanced deep learning framework to improve the prediction of individual self-harm within 12 months after hospital discharge. Between January 1, 2007, and December 31, 2010, we obtained 2,323 patients with self-harm clinical record from 1,764,094 inpatients across 44 public hospitals in Hong Kong and 46,460 randomly sampled population controls from those same hospitals. Eighty percent (80%) of the study sample was randomly selected for model training, and the remaining 20% was set aside for model testing. The proposed comorbidity network-enhanced model was compared with a baseline deep learning model for self-harm prediction. The C-statistic, precision and sensitivity were used to evaluate the prediction accuracy of the proposed model and the baseline model. Experiments demonstrated that the proposed comorbidity network-enhanced model outperformed baseline model in identifying patients who would self-harm within 12 months (C-statistic of proposed model 0.89). The precision was 0.54 for positive cases and 0.98 for negative cases, whilst the sensitivity was 0.72 for positive cases and 0.96 for negative cases. Results indicated that it is critical to consider the general disease comorbidity patterns in self-harm screening and prevention programs. The model also extracted the most predictive diagnoses, and pairs of comorbid diagnoses which provide medical professionals with an effective screening strategy.

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