Learning Spiking Neural Network from Easy to Hard task

Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models treat all samples equally, which does not align with the principles of human learning and overlooks the biological plausibility of SNNs. To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability. CL is a training strategy that advocates presenting easier data to models before gradually introducing more challenging data, mimicking the human learning process. We use a confidence-aware loss to measure and process the samples with different difficulty levels. By learning the confidence of different samples, the model reduces the contribution of difficult samples to parameter optimization automatically. We conducted experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are promising. To our best knowledge, this is the first proposal to enhance the biologically plausibility of SNNs by introducing CL

Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding

Decoding visual stimuli from brain recordings aims to deepen our understanding of the human visual system and build a solid foundation for bridging human and computer vision through the Brain-Computer Interface. However, reconstructing high-quality images with correct semantics from brain recordings is a challenging problem due to the complex underlying representations of brain signals and the scarcity of data annotations. In this work, we present MinD-Vis: Sparse Masked Brain Modeling with Double-Conditioned Latent Diffusion Model for Human Vision Decoding. Firstly, we learn an effective self-supervised representation of fMRI data using mask modeling in a large latent space inspired by the sparse coding of information in the primary visual cortex. Then by augmenting a latent diffusion model with double-conditioning, we show that MinD-Vis can reconstruct highly plausible images with semantically matching details from brain recordings using very few paired annotations. We benchmarked our model qualitatively and quantitatively; the experimental results indicate that our method outperformed state-of-the-art in both semantic mapping (100-way semantic classification) and generation quality (FID) by 66% and 41% respectively. An exhaustive ablation study was also conducted to analyze our framework.Comment: 8 pages, 9 figures, 2 tables, accepted by CVPR2023, see https://mind-vis.github.io/ for more informatio

Adaptiveness, Asynchrony, and Resource Efficiency in Parallel Stochastic Gradient Descent

Accelerated digitalization and sensor deployment in society in recent years poses critical challenges for associated data processing and analysis infrastructure to scale, and the field of big data, targeting methods for storing, processing, and revealing patterns in huge data sets, has surged. Artificial Intelligence (AI) models are used diligently in standard Big Data pipelines due to their tremendous success across various data analysis tasks, however exponential growth in Volume, Variety and Velocity of Big Data (known as its three V’s) in recent years require associated complexity in the AI models that analyze it, as well as the Machine Learning (ML) processes required to train them. In order to cope, parallelism in ML is standard nowadays, with the aim to better utilize contemporary computing infrastructure, whether it being shared-memory multi-core CPUs, or vast connected networks of IoT devices engaging in Federated Learning (FL).Stochastic Gradient Descent (SGD) serves as the backbone of many of the most popular ML methods, including in particular Deep Learning. However, SGD has inherently sequential semantics, and is not trivially parallelizable without imposing strict synchronization, with associated bottlenecks. Asynchronous SGD (AsyncSGD), which relaxes the original semantics, has gained significant interest in recent years due to promising results that show speedup in certain contexts. However, the relaxed semantics that asynchrony entails give rise to fundamental questions regarding AsyncSGD, relating particularly to its stability and convergence rate in practical applications.This thesis explores vital knowledge gaps of AsyncSGD, and contributes in particular to: Theoretical frameworks – Formalization of several key notions related to the impact of asynchrony on the convergence, guiding future development of AsyncSGD implementations; Analytical results – Asymptotic convergence bounds under realistic assumptions. Moreover, several technical solutions are proposed, targeting in particular: Stability – Reducing the number of non-converging executions and the associated wasted energy; Speedup – Improving convergence time and reliability with instance-based adaptiveness; Elasticity – Resource-efficiency by avoiding over-parallelism, and thereby improving stability and saving computing resources. The proposed methods are evaluated on several standard DL benchmarking applications and compared to relevant baselines from previous literature. Key results include: (i) persistent speedup compared to baselines, (ii) increased stability and reduced risk for non-converging executions, (iii) reduction in the overall memory footprint (up to 17%), as well as the consumed computing resources (up to 67%).In addition, along with this thesis, an open-source implementation is published, that connects high-level ML operations with asynchronous implementations with fine-grained memory operations, leveraging future research for efficient adaptation of AsyncSGD for practical applications

Streamlining Temporal Formal Verification over Columnar Databases

Recent findings demonstrate how database technology enhances the computation of formal verification tasks expressible in linear time logic for finite traces (LTLf). Human-readable declarative languages also help the common practitioner to express temporal constraints in a straightforward and accessible language. Notwithstanding the former, this technology is in its infancy, and therefore, few optimization algorithms are known for dealing with massive amounts of information audited from real systems. We, therefore, present four novel algorithms subsuming entire LTLf expressions while outperforming previous state-of-the-art implementations on top of KnoBAB, thus postulating the need for the corresponding, leading to the formulation of novel xtLTLf-derived algebraic operators

Graph-Transfromational Swarms : A Graph-Transformational Approach to Swarm Computation

Computer systems are becoming increasingly distributed and interconnected. Various emerging notions, such as smart grids, system of systems, industry 4.0 or cyber-physical systems have gained more and more importance during the last few years. All of them propose to solve engineering problems by using several autonomous components that act in parallel and are interconnected, foremost using Internet technologies. These emerging concepts look very promising, but also exhibit various technical challenges. For instance, how is it possible to develop decentralized control mechanisms that produce a desired emerging behavior to solve a given task or how to model such solutions in order to analyze their behavior in terms of complexity and correctness? These are two major questions that this thesis attempts to answer. Indeed, it provides graph-transformational swarms as a novel concept that combines the ideas and principles of swarms and swarm computing and the formal methods of graph transformation to model distributed systems. Graph-transformational swarms captures the advantages of swarms and swarm computing and of graph transformation

Mathematical foundations for a compositional account of the Bayesian brain

This dissertation reports some first steps towards a compositional account of active inference and the Bayesian brain. Specifically, we use the tools of contemporary applied category theory to supply functorial semantics for approximate inference. To do so, we define on the 'syntactic' side the new notion of Bayesian lens and show that Bayesian updating composes according to the compositional lens pattern. Using Bayesian lenses, and inspired by compositional game theory, we define fibrations of statistical games and classify various problems of statistical inference as corresponding sections: the chain rule of the relative entropy is formalized as a strict section, while maximum likelihood estimation and the free energy give lax sections. In the process, we introduce a new notion of 'copy-composition'. On the 'semantic' side, we present a new formalization of general open dynamical systems (particularly: deterministic, stochastic, and random; and discrete- and continuous-time) as certain coalgebras of polynomial functors, which we show collect into monoidal opindexed categories (or, alternatively, into algebras for multicategories of generalized polynomial functors). We use these opindexed categories to define monoidal bicategories of 'cilia': dynamical systems which control lenses, and which supply the target for our functorial semantics. Accordingly, we construct functors which explain the bidirectional compositional structure of predictive coding neural circuits under the free energy principle, thereby giving a formal mathematical underpinning to the bidirectionality observed in the cortex. Along the way, we explain how to compose rate-coded neural circuits using an algebra for a multicategory of linear circuit diagrams, showing subsequently that this is subsumed by lenses and polynomial functors. Because category theory is unfamiliar to many computational neuroscientists and cognitive scientists, we have made a particular effort to give clear, detailed, and approachable expositions of all the category-theoretic structures and results of which we make use. We hope that this dissertation will prove helpful in establishing a new "well-typed'' science of life and mind, and in facilitating interdisciplinary communication