Properties Of Codes Research Articles

Cholinergic regulation of dendritic Ca2+ spikes controls firing mode of hippocampal CA3 pyramidal neurons

Active dendritic integrative mechanisms such as regenerative dendritic spikes enrich the information processing abilities of neurons and fundamentally contribute to behaviorally relevant computations. Dendritic Ca2+ spikes are generally thought to produce plateau-like dendritic depolarization and somatic complex spike burst (CSB) firing, which can initiate rapid changes in spatial coding properties of hippocampal pyramidal cells (PCs). However, here we reveal that a morpho-topographically distinguishable subpopulation of rat and mouse hippocampal CA3PCs exhibits compound apical dendritic Ca2+ spikes with unusually short duration that do not support the firing of sustained CSBs. These Ca2+ spikes are mediated by L-type Ca2+ channels and their time course is restricted by A- and M-type K+ channels. Cholinergic activation powerfully converts short Ca2+ spikes to long-duration forms, and facilitates and prolongs CSB firing. We propose that cholinergic neuromodulation controls the ability of a CA3PC subtype to generate sustained plateau potentials, providing a state-dependent dendritic mechanism for memory encoding and retrieval.

Improved rate-distance trade-offs for quantum codes with restricted connectivity

Abstract For quantum error-correcting codes to be realizable, it is important that the qubits subject to the code constraints exhibit some form of limited connectivity. The works of Bravyi and Terhal (2009 New J. Phys. 11 043029) (BT) and Bravyi et al (2010 Phys. Rev. Lett. 104 050503) (BPT) established that geometric locality constrains code properties—for instance [ [ n , k , d ] ] quantum codes defined by local checks on the D-dimensional lattice must obey k d 2 / ( D − 1 ) ⩽ O ( n ) . Baspin and Krishna (2022 Quantum 6 711) studied the more general question of how the connectivity graph associated with a quantum code constrains the code parameters. These trade-offs apply to a richer class of codes compared to the BPT and BT bounds, which only capture geometrically-local codes. We extend and improve this work, establishing a tighter dimension-distance trade-off as a function of the size of separators in the connectivity graph. We also obtain a distance bound that covers all stabilizer codes with a particular separation profile, rather than only LDPC codes.

Automating Unrealizability Logic: Hoare-Style Proof Synthesis for Infinite Sets of Programs

Automated verification of all members of a (potentially infinite) set of programs has the potential to be useful in program synthesis, as well as in verification of dynamically loaded code, concurrent code, and language properties. Existing techniques for verification of sets of programs are limited in scope and unable to create or use interpretable or reusable information about sets of programs. The consequence is that one cannot learn anything from one verification problem that can be used in another. Unrealizability logic (UL), proposed by Kim et al. as the first Hoare-style proof system to prove properties over sets of programs (defined by a regular tree grammar), presents a theoretical framework that can express and use reusable insight. In particular, UL features nonterminal summaries ---inductive facts that characterize recursive nonterminals (analogous to procedure summaries in Hoare logic). In this work, we design the first UL proof synthesis algorithm, implemented as Wuldo. Specifically, we decouple the problem of deciding how to apply UL rules from the problem of synthesizing/checking nonterminal summaries by computing proof structure in a fully syntax-directed fashion. We show that Wuldo, when provided nonterminal summaries, can express and prove verification problems beyond the reach of existing tools, including establishing how infinitely many programs behave on infinitely many inputs. In some cases, Wuldo can even synthesize the necessary nonterminal summaries. Moreover, Wuldo can reuse previously proven nonterminal summaries across verification queries, making verification 1.96 times as fast as when summaries are instead proven from scratch.

Encoding of vibrotactile stimuli by mechanoreceptors in rodent glabrous skin.

Somatosensory coding in rodents has been mostly studied in the whisker system and hairy skin, whereas the function of low-threshold mechanoreceptors (LTMRs) in rodent glabrous skin has received scant attention, unlike in primates where glabrous skin has been the focus. The relative activation of different LTMR subtypes carries information about vibrotactile stimuli, as does the rate and temporal patterning of LTMR spikes. Rate coding depends on the probability of a spike occurring on each stimulus cycle (reliability) whereas temporal coding depends on the timing of spikes relative to the stimulus cycle (precision). Using in vivo extracellular recordings in male rats and mice of either sex, we measured the reliability and precision of LTMR responses to tactile stimuli including sustained pressure and vibration. Similar to other species, rodent LTMRs were separated into rapid-adapting (RA) or slow-adapting (SA) based on their response to sustained pressure. However, unlike the dichotomous frequency preference characteristic of RA1 and RA2/Pacinian afferents in other species, rodent RAs fell along a continuum. Fitting generalized linear models (GLMs) to experimental data reproduced the reliability and precision of rodent RAs. The resulting model parameters highlight key mechanistic differences across the RA spectrum; specifically, the integration window of different RAs transitions from wide to narrow as tuning preferences across the population move from low to high frequencies. Our results show that rodent RAs can support both rate and temporal coding, but their heterogeneity suggests that co-activation patterns play a greater role in population coding than for dichotomously tuned primate RAs.Significance Statement Our sense of touch starts with activation of nerve fibres in the skin. Although response properties of various fibre types are well-established in other species (e.g. primates), quantitative characterization in rats and mice is limited. To fill this gap, we performed a comprehensive electrophysiological investigation into the coding properties of tactile fibres in rodent non-hairy skin and then simulated these fibres to explain differences in their responses. We show that rodent tactile fibres resemble those from other species, but that their heterogeneity at the population level may differ, with potentially important implications for encoding of touch. Simulations reveal intrinsic mechanisms that support this heterogeneity and provide a useful tool to explore somatosensation in rodents.

Category boundaries modulate memory in a place-cell-like manner

Concepts describe how instances of the same kind are related, enabling the categorization and interpretation of new information.1,2 How concepts are represented is a longstanding question. Category boundaries have been considered defining features of concept representations, which can guide categorical inference,3 with fMRI evidence showing category-boundary signals in the hippocampus.4,5 The underlying neural mechanism remains unclear. The hippocampal-entorhinal system, known for its spatially tuned neurons that form cognitive maps of space,6,7 may support conceptual knowledge formation, with place cells encoding locations in conceptual space.4,8,9,10,11 Physical boundaries anchor spatial representations and boundary shifts affect place and grid fields,12,13,14,15,16 as well as human spatial memory,17,18,19 along manipulated dimensions. These place cell responses are likely driven by boundary vector cells, which respond to boundaries at specific allocentric distances and directions,20,21,22,23 the neural correlates of which have been identified in the subiculum and entorhinal cortex20,24,25. We hypothesize similar patterns of memory adaptations in response to shifting category boundaries. Our findings show that after category boundary shifts, participants' memory for category exemplars distorts along the changed dimension, mirroring place field deformations. We demonstrate that the boundary vector cell model of place cell firing best accounts for these distortions compared with alternative geometric explanations. Our study highlights a role of category boundaries in human cognition and establishes a new complementary link between hippocampal coding properties with respect to boundaries and human concept representation, bridging spatial and conceptual domains.

Vul-LMGNNs: Fusing language models and online-distilled graph neural networks for code vulnerability detection

Code Language Models (codeLMs) and Graph Neural Networks (GNNs) are widely used in code vulnerability detection. However, a critical yet often overlooked issue is that GNNs primarily rely on aggregating information from adjacent nodes, limiting structural information transfer to single-layer updates. In code graphs, nodes and relationships typically require cross-layer information propagation to fully capture complex program logic and potential vulnerability patterns. Furthermore, while some studies utilize codeLMs to supplement GNNs with code semantic information, existing integration methods have not fully explored the potential of their collaborative effects.To address these challenges, we introduce Vul-LMGNNs that integrates pre-trained CodeLMs with GNNs, leveraging knowledge distillation to facilitate cross-layer propagation of both code semantic knowledge and structural information. Specifically, Vul-LMGNNs utilizes Code Property Graphs (CPGs) to incorporate code syntax, control flow, and data dependencies, while employing gated GNNs to extract structural information in the CPG. To achieve cross-layer information transmission, we implement an online knowledge distillation (KD) program that enables a single student GNN to acquire structural information extracted from a simultaneously trained counterpart through an alternating training procedure. Additionally, we leverage pre-trained CodeLMs to extract semantic features from code sequences. Finally, we propose an ”implicit-explicit” joint training framework to better leverage the strengths of both CodeLMs and GNNs. In the implicit phase, we utilize CodeLMs to initialize the node embeddings of each student GNN. Through online knowledge distillation, we facilitate the propagation of both code semantics and structural information across layers. In the explicit phase, we perform linear interpolation between the CodeLM and the distilled GNN to learn a late fusion model. The proposed method, evaluated across four real-world vulnerability datasets, demonstrated superior performance compared to 17 state-of-the-art approaches. Our source code can be accessed via GitHub: https://github.com/Vul-LMGNN/vul-LMGGNN.

Decomposition-Estimation-Reconstruction: An Automatic and Accurate Neuron Extraction Paradigm.

The extraction of spatiotemporal neuron activity from calcium imaging videos plays a crucial role in unraveling the coding properties of neurons. While existing neuron extraction approaches have shown promising results, disturbing and scattering background and unused depth still impede their performance. To address these limitations, we develop an automatic and accurate neuron extraction paradigm, dubbed as decomposition-estimation-reconstruction (DER), consisting of D-procedure, E-procedure, and R-procedure. Specifically, the D-procedure first decomposes the raw data into a low-rank background and a sparse neuron signal, and regularizes L0 -norm priors of intensity and gradient of the neuron signal to suppress blurring and artifact effects. Then, the E-procedure estimates the depth-dependent transmission of the neuron signal based on its bright and dark channel priors. The R-procedure finally integrates the depth estimation of the neuron signal as a content-importance weight into a constrained non-negative matrix decomposition framework, which facilitates accurate neuron locations to boost the quality of extracted neurons. These three procedures are coupled in a cascade manner, where the former copes with calcium imaging data to facilitate the subsequent one. Comprehensive experiments on neuron extraction from calcium imaging videos demonstrate the superiority of our DER paradigm in both qualitative results and quantitative assessments over state-of-the-art methods.

2P-NucTag: on-demand phototagging for molecular analysis of functionally identified cortical neurons.

Neural circuits are characterized by genetically and functionally diverse cell types. A mechanistic understanding of circuit function is predicated on linking the genetic and physiological properties of individual neurons. However, it remains highly challenging to map the transcriptional properties to functionally heterogeneous neuronal subtypes in mammalian cortical circuits in vivo. Here, we introduce a high-throughput two-photon nuclear phototagging (2P-NucTag) approach optimized for on-demand and indelible labeling of single neurons via a photoactivatable red fluorescent protein following in vivo functional characterization in behaving mice. We demonstrate the utility of this function-forward pipeline by selectively labeling and transcriptionally profiling previously inaccessible 'place' and 'silent' cells in the mouse hippocampus. Our results reveal unexpected differences in gene expression between these hippocampal pyramidal neurons with distinct spatial coding properties. Thus, 2P-NucTag opens a new way to uncover the molecular principles that govern the functional organization of neural circuits.

Impaired Dynamics of Positional and Contextual Neural Coding in an Alzheimer's Disease Rat Model.

The hippocampal representation of space, formed by the collective activity of populations of place cells, is considered as a substrate of spatial memory. Alzheimer's disease (AD), a widespread severe neurodegenerative condition of multifactorial origin, typically exhibits spatial memory deficits among its early clinical signs before more severe cognitive impacts develop. To investigate mechanisms of spatial memory impairment in a double transgenic rat model of AD. In this study, we utilized 9-12-month-old double-transgenic TgF344-AD rats and age-matched controls to analyze the spatial coding properties of CA1 place cells. We characterized the spatial memory representation, assessed cells' spatial information content and direction-specific activity, and compared their population coding in familiar and novel conditions. Our findings revealed that TgF344-AD animals exhibited lower precision in coding, as evidenced by reduced spatial information and larger receptive zones. This impairment was evident in maps representing novel environments. While controls instantly encoded directional context during their initial exposure to a novel environment, transgenics struggled to incorporate this information into the newly developed hippocampal spatial representation. This resulted in impairment in orthogonalization of stored activity patterns, an important feature directly related to episodic memory encoding capacity. Overall, the results shed light on the nature of impairment at both the single-cell and population levels in the transgenic AD model. In addition to the observed spatial coding inaccuracy, the findings reveal a significantly impaired ability to adaptively modify and refine newly stored hippocampal memory patterns.

ON QUANTUM CODES CONSTRUCTION FROM CONSTACYCLIC CODES OVER THE RING I_q[u,v] / <u^2-a^2, v^2-a^2, uv-vu>

This paper focuses on studying the properties of constacyclic codes, quantum error-correcting codes. The code is studied over a specific mathematical structure called the ring $\mathfrak{S}$, which is defined as $\mathfrak{S}=\mathfrak{I}_q[\mathfrak{u},\mathfrak{v}]/\langle \mathfrak{u}^2-\alpha^2,~ \mathfrak{v}^2-\alpha^2,~\mathfrak{u}\mathfrak{v}-\mathfrak{v}\mathfrak{u} \rangle$, where $\mathfrak{I}_q$ is a finite field of $q$ elements, $\alpha$ be the nonzero elements of the field $\mathfrak{I}_q$ and $q$ is a power of an odd prime $p$ such that $q=p^m, ~\textup{for}~ m \ge 1$. The paper also introduces a Gray map and use it to decompose constacyclic codes over the ring $\mathfrak{S}$ into a direct sum of constacyclic codes over $\mathfrak{I}_q$. We construct new and better quantum error-correcting codes over the ring $\mathfrak{S}$ (cf.; Table 1 and Table 2). Moreover, we also obtain best known linear codes as well as best dimension linear codes (cf.; Table 4).

General Structure and Properties of Cyclic Codes Over \(GF_2\)

This article provides a comprehensive analysis of cyclic codes in GF(2), focusing on their general structure and key properties. Cyclic codes are characterized by their generator polynomials, which define their structure and play a crucial role in encoding and decoding processes. The cyclical shift property, inherent in cyclic codes, facilitates efficient implementation using shift register circuits, making them practical for real-world applications. The discussion highlights the algebraic properties that distinguish cyclic codes from other linear block codes, emphasizing their ability to detect and correct errors.

Grid Cells in Cognition: Mechanisms and Function.

The activity patterns of grid cells form distinctively regular triangular lattices over the explored spatial environment and are largely invariant to visual stimuli, animal movement, and environment geometry. These neurons present numerous fascinating challenges to the curious (neuro)scientist: What are the circuit mechanisms responsible for creating spatially periodic activity patterns from the monotonic input-output responses of single neurons? How and why does the brain encode a local, nonperiodic variable-the allocentric position of the animal-with a periodic, nonlocal code? And, are grid cells truly specialized for spatial computations? Otherwise, what is their role in general cognition more broadly? We review efforts in uncovering the mechanisms and functional properties of grid cells, highlighting recent progress in the experimental validation of mechanistic grid cell models, and discuss the coding properties and functional advantages of the grid code as suggested by continuous attractor network models of grid cells.

Hidden code vulnerability detection: A study of the Graph-BiLSTM algorithm

Context:The accelerated growth of the Internet and the advent of artificial intelligence have led to a heightened interdependence of open source products, which has in turn resulted in a rise in the frequency of security incidents. Consequently, the cost-effective, fast and efficient detection of hidden code vulnerabilities in open source software products has become an urgent challenge for both academic and engineering communities. Objectives:In response to this pressing need, a novel and efficient code vulnerability detection model has been proposed: the Graph-Bi-Directional Long Short-Term Memory Network Algorithm (Graph-BiLSTM). The algorithm is designed to enable the detection of vulnerabilities in Github’s code commit records on a large scale, at low cost and in an efficient manner. Methods:In order to extract the most effective code vulnerability features, state-of-the-art vulnerability datasets were compared in order to identify the optimal training dataset. Initially, the Joern tool was employed to transform function-level code blocks into Code Property Graphs (CPGs). Thereafter, structural features (degree centrality, Katz centrality, and closeness centrality) of these CPGs were computed and combined with the embedding features of the node sequences to form a two-dimensional feature vector space for the function-level code blocks. Subsequently, the BiLSTM network algorithm was employed for the automated extraction and iterative model training of a substantial number of vulnerability code samples. Finally, the trained algorithmic model was applied to code commit records of open-source software products on GitHub, achieving effective detection of hidden code vulnerabilities. Conclusion:Experimental results indicate that the PrimeVul dataset represents the most optimal resource for vulnerability detection. Moreover, the Graph-BiLSTM model demonstrated superior performance in terms of accuracy, training cost, and inference time when compared to state-of-the-art algorithms for the detection of vulnerabilities in open-source software code on GitHub. This highlights the significant value of the model for engineering applications.

Classical product code constructions for quantum Calderbank-Shor-Steane codes

Several notions of code products are known in quantum error correction, such as hypergraph products, homological products, lifted products, balanced products, to name a few. In this paper we introduce a new product code construction which is a natural generalization of classical product codes to quantum codes: starting from a set of component Calderbank-Shor-Steane (CSS) codes, a larger CSS code is obtained where both X parity checks and Z parity checks are associated to classical product codes. We deduce several properties of product CSS codes from the properties of the component codes, including bounds to the code distance, and show that built-in redundancies in the parity checks result in so-called meta-checks which can be exploited to correct syndrome read-out errors. We then specialize to the case of single-parity-check (SPC) product codes which in the classical domain are a common choice for constructing product codes. Logical error rate simulations of a SPC 3-fold product CSS code having parameters [[512,174,8]] are shown under both a maximum likelihood decoder for the erasure channel and belief propagation decoding for depolarizing noise. We compare the results with other codes of comparable length and dimension, including a code from the family of asymptotically good Tanner codes. We observe that our reference product CSS code outperforms all the other examined codes.

Formal design, verification and implementation of robotic controller software via RoboChart and RoboTool

Current practice in simulation and implementation of robot controllers is usually undertaken with guidance from high-level design diagrams and pseudocode. Thus, no rigorous connection between the design and the development of a robot controller is established. This paper presents a framework for designing robotic controllers with support for automatic generation of executable code and automatic property checking. A state-machine based notation, RoboChart, and a tool (RoboTool) that implements the automatic generation of code and mathematical models from the designed controllers are presented. We demonstrate the application of RoboChart and its related tool through a case study of a robot performing an exploration task. The automatically generated code is platform independent and is used in both simulation and two different physical robotic platforms. Properties are formally checked against the mathematical models generated by RoboTool, and further validated in the actual simulations and physical experiments. The tool not only provides engineers with a way of designing robotic controllers formally but also paves the way for correct implementation of robotic systems.

Corrections to “Concentration Properties of Random Codes”

Corrections to “Concentration Properties of Random Codes”

Efficient Static Vulnerability Analysis for JavaScript with Multiversion Dependency Graphs

While static analysis tools that rely on Code Property Graphs (CPGs) to detect security vulnerabilities have proven effective, deciding how much information to include in the graphs remains a challenge. Including less information can lead to a more scalable analysis but at the cost of reduced effectiveness in identifying vulnerability patterns, potentially resulting in classification errors. Conversely, more information in the graph allows for a more effective analysis but may affect scalability. For example, scalability issues have been recently highlighted in ODGen, the state-of-the-art CPG-based tool for detecting Node.js vulnerabilities. This paper examines a new point in the design space of CPGs for JavaScript vulnerability detection. We introduce the Multiversion Dependency Graph (MDG), a novel graph-based data structure that captures the state evolution of objects and their properties during program execution. Compared to the graphs used by ODGen, MDGs are significantly simpler without losing key information needed for vulnerability detection. We implemented Graph.js, a new MDG-based static vulnerability scanner specialized in analyzing npm packages and detecting taint-style and prototype pollution vulnerabilities. Our evaluation shows that Graph.js outperforms ODGen by significantly reducing both the false negatives and the analysis time. Additionally, we have identified 49 previously undiscovered vulnerabilities in npm packages.

Robust variability of grid cell properties within individual grid modules enhances encoding of local space.

Although grid cells are one of the most well studied functional classes of neurons in the mammalian brain, the assumption that there is a single grid orientation and spacing per grid module has not been carefully tested. We investigate and analyze a recent large-scale recording of medial entorhinal cortex to characterize the presence and degree of heterogeneity of grid properties within individual modules. We find evidence for small, but robust, variability and hypothesize that this property of the grid code could enhance the ability of encoding local spatial information. Performing analysis on synthetic populations of grid cells, where we have complete control over the amount heterogeneity in grid properties, we demonstrate that variability, of a similar magnitude to the analyzed data, leads to significantly decreased decoding error, even when restricted to activity from a single module. Our results highlight how the heterogeneity of the neural response properties may benefit coding and opens new directions for theoretical and experimental analysis of grid cells.

An asymptotic property of quaternary additive codes

An asymptotic property of quaternary additive codes

Repair and DNA Polymerase Bypass of Clickable Pyrimidine Nucleotides.

Clickable nucleosides, most often 5-ethynyl-2'-deoxyuridine (EtU), are widely used in studies of DNA replication in living cells and in DNA functionalization for bionanotechology applications. Although clickable dNTPs are easily incorporated by DNA polymerases into the growing chain, afterwards they might become targets for DNA repair systems or interfere with faithful nucleotide insertion. Little is known about the possibility and mechanisms of these post-synthetic events. Here, we investigated the repair and (mis)coding properties of EtU and two bulkier clickable pyrimidine nucleosides, 5-(octa-1,7-diyn-1-yl)-U (C8-AlkU) and 5-(octa-1,7-diyn-1-yl)-C (C8-AlkC). In vitro, EtU and C8-AlkU, but not C8-AlkC, were excised by SMUG1 and MBD4, two DNA glycosylases from the base excision repair pathway. However, when placed into a plasmid encoding a fluorescent reporter inactivated by repair in human cells, EtU and C8-AlkU persisted for much longer than uracil or its poorly repairable phosphorothioate-flanked derivative. DNA polymerases from four different structural families preferentially bypassed EtU, C8-AlkU and C8-AlkC in an error-free manner, but a certain degree of misincorporation was also observed, especially evident for DNA polymerase β. Overall, clickable pyrimidine nucleotides could undergo repair and be a source of mutations, but the frequency of such events in the cell is unlikely to be considerable.