Computational neural models are essential tools for neuroscientists to study the functional roles of single neurons or neural circuits. With the recent advances in experimental techniques, there is a growing demand to build up neural models at single neuron or large-scale circuit levels. A long-standing challenge to build up such models lies in tuning the free parameters of the models to closely reproduce experimental recordings. There are many advanced machine-learning-based methods developed recently for parameter tuning, but many of them are task-specific or requires onerous manual interference. There lacks a general and fully-automated method since now. Here, we present a Long Short-Term Memory (LSTM)-based deep learning method, General Neural Estimator (GNE), to fully automate the parameter tuning procedure, which can be directly applied to both single neuronal models and large-scale neural circuits. We made comprehensive comparisons with many advanced methods, and GNE showed outstanding performance on both synthesized data and experimental data. Finally, we proposed a roadmap centered on GNE to help guide neuroscientists to computationally reconstruct single neurons and neural circuits, which might inspire future brain reconstruction techniques and corresponding experimental design. The code of our work will be publicly available upon acceptance of this paper.

A content delivery network (CDN) improves the accessing performance and availability of websites via its globally distributed network infrastructures, which contributes to the thriving of CDN-powered websites on the Internet. Because CDNpowered websites normally operate important businesses or critical services, attackers are mostly interested in taking down these high-value websites, to achieve severe damage with maximum influence. Because the CDN absorbs distributed attacking traffic with its massive bandwidth resources, it is commonly believed that CDN vendors provide effective DoS protection for the CDNpowered websites. However, we reveal that implementation or protocol weaknesses in the forwarding mechanisms of the CDN can be exploited to break this CDN protection. By sending crafted but legal requests, an attacker can launch an efficient DoS attack against the website origin behind it. In particular, we present three CDN threats in this study. By abusing the HTTP/2 requestconverting behavior and HTTP pre-POST behavior of a CDN, an attacker can saturate the CDN-origin bandwidth and exhaust the connection limits of the origin. What is more concerning is that some CDN vendors use only a small set of traffic forwarding IPs with lower IP-churning rates to establish connections with the origin. This characteristic provides a great opportunity for an attacker to effectively degrade the global availability of a website just by cutting off specific CDN-origin connections. In this work, we examine the CDN request-forwarding behaviors across six well-known CDN vendors and perform real-world experiments to evaluate the severity of the threats. Because the threats are caused by flawed trade-offs made by the CDN vendors between usability and security, we discuss possible mitigation and received positive feedback after responsible disclosure to the aforementioned CDN vendors.

No abstract

Computational modeling is an essential approach in neuroscience for linking neural mechanisms to experimental observations. Recent advanced machine learning techniques, such as deep learning, leverage synthetic data generated from computational models to reveal underlying neural mechanisms from experimental data. However, despite significant progress, one unsolved problem in these methods is that the synthetic data differ substantially from experimental data, leading to severely biased results. To this end, we introduce the Domain Adaptive Neural Inference framework to construct synthetic data that closely resemble the distribution of experimental data and use the matching synthetic data to predict the neural mechanisms of experimental data. We demonstrate the accuracy, efficiency, and versatility of our framework in various experimental observations, including inferring single-neuron biophysics across mouse brain regions from intracellular recordings in the Allen Cell Types Database; inferring biophysical properties of a microcircuit of Cancer Borealis from extracellular recordings; and inferring monosynaptic connectivity of mouse CA1 networks from in vivo multi-electrode extracellular recordings. The framework outperforms state-of-the-art methods in every application, and can potentially be generalized to a wide range of computational modeling approaches in biosciences.