Exploring Anatomical Links Between the Crow’s Nidopallium Caudolaterale and its Song System

Crows are corvid songbirds that exhibit remarkable cognitive control over their actions, including their vocalizations. They can learn to vocalize on command and the activity of single neurons from the crow's associative telencephalic structure nidopallium caudolaterale (NCL) is correlated with the execution of this vocal and many non-vocal skilled behaviors. However, it remains unknown if specific anatomical adaptations that directly link the crow NCL to any of the nuclei of the crow's 'song system' exist. To address this issue, we used fluorescent tracers along with histological staining methods (Nissl-, myelin-, and anti tyrosine hydroxylase) to characterize the connectivity of the crow's NCL in relation to its song system nuclei. We found that the NCL sends dense projections into the dorsal intermediate arcopallium (AID) directly adjacent to and engulfing the robust nucleus of the arcopallium (RA), which is the telencephalic motor output of the song system. Similarly, we demonstrate dense NCL projections into the striatum surrounding the basal ganglia song nucleus 'area X'. Both of these descending projections mirror the projections of the nidopallial song nucleus HVC (proper name) into RA and area X, with extremely sparse NCL fibers extending into area X. Furthermore, we characterized the distribution of cells projecting from the lateral part of the magnocellular nucleus of the anterior nidopallium (MAN) to NCL. Notably, a separate medial population of MAN cells projects to HVC. These two sets of connections - MAN to NCL and MAN to HVC - run in parallel but do not overlap. Taken together, our findings support the hypothesis that the NCL is part of a 'general motor system' that parallels the song system but exhibits only minimal monosynaptic interconnections with it.

A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains.

Neurons in the brain are known to encode diverse information through their spiking activity, primarily reflecting external stimuli and internal states. However, whether individual neurons also embed information about their own anatomical location within their spike patterns remains largely unexplored. Here, we show that machine learning models can predict a neuron's anatomical location across multiple brain regions and structures based solely on its spiking activity. Analyzing high-density recordings from thousands of neurons in awake, behaving mice, we demonstrate that anatomical location can be reliably decoded from neuronal activity across various stimulus conditions, including drifting gratings, naturalistic movies, and spontaneous activity. Crucially, anatomical signatures generalize across animals and even across different research laboratories, suggesting a fundamental principle of neural organization. Examination of trained classifiers reveals that anatomical information is enriched in specific interspike intervals as well as responses to stimuli. Within the visual isocortex, anatomical embedding is robust at the level of layers and primary versus secondary but does not robustly separate individual secondary structures. In contrast, structures within the hippocampus and thalamus are robustly separable based on their spike patterns. Our findings reveal a generalizable dimension of the neural code, where anatomical information is multiplexed with the encoding of external stimuli and internal states. This discovery provides new insights into the relationship between brain structure and function, with broad implications for neurodevelopment, multimodal integration, and the interpretation of large-scale neuronal recordings. Immediately, it has potential as a strategy for in-vivo electrode localization.

Inferring illness causes recruits the animacy semantic network

Inferring the causes of illness is universal across human cultures and is essential for survival. Here we use this phenomenon as a test case for understanding the neural basis of implicit causal inference. Participants (n=20) undergoing fMRI read two-sentence vignettes that encouraged them to make causal inferences about illness or mechanical failure (causal control) as well as non-causal vignettes. All vignettes were about people and were matched on linguistic variables. The same participants performed localizers: language, logical reasoning, and mentalizing. Inferring illness causes selectively engaged a portion of precuneus (PC) previously implicated in the semantic representation of animates (e.g., people, animals). This region was near but not the same as PC responses to mental states, suggesting a neural mind/body distinction. No cortical areas responded to causal inferences across domains (i.e., illness, mechanical), including in individually localized language and logical reasoning networks. Together, these findings suggest that implicit causal inferences are supported by content-specific semantic networks that encode causal knowledge.

Transient cortical Beta-frequency oscillations associated with contextual novelty in high density mouse EEG

Beta-frequency oscillations (20-30 Hz) are prominent in both human and rodent electroencephalogram (EEG) recordings. Discrete epochs of beta (or Beta2) oscillations are prevalent in the hippocampus and other brain areas during exploration of novel environments. However, little is known about the spatial distribution and temporal relationships of beta oscillations across the cortex in response to novelty. To investigate this, mice fitted with 30-channel EEG-style multi-electrode arrays underwent a single recording session in a novel environment. While changes to spectral properties of cortical oscillations were minimal, there was a profound increase in the rate of beta bursts during the initial part of the recording session, when the environment was most novel. This was true across the cortex but most notable in recording channels situated above the retrosplenial cortex. Additionally, novelty was associated with greater connectivity between retrosplenial areas and the rest of the cortex, specifically in the beta frequency range. However, it was also found that the cortex in general, is highly modulated by environmental novelty. This data further suggests the retrosplenial cortex is an important hub for distinguishing environmental context and highlights the diversity of functions for beta oscillations across the brain, which can be observed using high-density EEG.

Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems

Latent dynamical systems have been widely used to characterize the dynamics of neural population activity in the brain. However, these models typically ignore the fact that the brain contains multiple cell types. This limits their ability to capture the functional roles of distinct cell classes, or to accurately predict the effects of cell-specific optogenetic perturbations on neural activity or behavior. To overcome these limitations, we introduce the "cell-type dynamical systems" (CTDS) model. This model extends latent linear dynamical systems to contain distinct latent variables for each cell class, with biologically inspired constraints on both dynamics and emissions. To illustrate our approach, we consider neural recordings with distinct excitatory (E) and inhibitory (I) populations. The CTDS model defines separate latents for E and I cells, and constrains the dynamics so that E (I) latents have a strictly positive (negative) effects on other latents. We applied CTDS to recordings from rat frontal orienting fields (FOF) and anterior dorsal striatum (ADS) during an auditory decision-making task. The model achieved higher accuracy than a standard linear dynamical system (LDS), and revealed that both E and I latents could be used to decode the animal's choice, showing that choice-related information is not restricted to a single cell class. We also performed in-silico optogenetic perturbation experiments in the FOF and ADS, and found that CTDS was able to replicate the causal effects of different perturbations on behavior, whereas a standard LDS model which lacks the ability to capture cell-specific perturbations did not. Crucially, our model allowed us to understand the effects of these perturbations by revealing the dynamics of different cell-specific latents. Finally, CTDS can also be used to identify cell types for neurons whose class labels are unknown in electrophysiological recordings. These results illustrate the power of the CTDS model to provide more accurate and more biologically interpretable descriptions of neural population dynamics and their relationship to behavior.

BDCC, Vol. 8, Pages 79: Trends and Challenges Towards Effective Data-Driven Decision Making in UK Small and Medium-Sized Enterprises: Case Studies and Lessons Learnt from the Analysis of 85 Small and Medium-Sized Enterprises

BDCC, Vol. 8, Pages 79: Trends and Challenges Towards Effective Data-Driven Decision Making in UK Small and Medium-Sized Enterprises: Case Studies and Lessons Learnt from the Analysis of 85 Small and Medium-Sized Enterprises

Big Data and Cognitive Computing doi: 10.3390/bdcc8070079

Authors: Abdel-Rahman H. Tawil Muhidin Mohamed Xavier Schmoor Konstantinos Vlachos Diana Haidar

The adoption of data science brings vast benefits to Small and Medium-sized Enterprises (SMEs) including business productivity, economic growth, innovation and job creation. Data science can support SMEs to optimise production processes, anticipate customers’ needs, predict machinery failures and deliver efficient smart services. Businesses can also harness the power of artificial intelligence (AI) and big data, and the smart use of digital technologies to enhance productivity and performance, paving the way for innovation. However, integrating data science decisions into an SME requires both skills and IT investments. In most cases, such expenses are beyond the means of SMEs due to their limited resources and restricted access to financing. This paper presents trends and challenges towards effective data-driven decision making for organisations based on a 3-year long study which covered more than 85 UK SMEs, mostly from the West Midlands region of England. In particular, this study attempts to find answers to several key research questions around data science and AI adoption among UK SMEs, and the advantages of digitalisation and data-driven decision making, as well as the challenges hindering their effective utilisation of these technologies. We also present two case studies that demonstrate the potential of digitisation and data science, and use these as examples to unveil challenges and showcase the wealth of currently available opportunities for SMEs.

Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions

Abstract

Robust clustering of high-dimensional data is an important topic because clusters in real datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based clustering often fail for high dimensional data, e.g., due to the number of free covariance parameters. A parametrization of the component scale matrices for the mixture of generalized hyperbolic distributions is proposed. This parameterization includes a penalty term in the likelihood. An analytically feasible expectation-maximization algorithm is developed by placing a gamma-lasso penalty constraining the concentration matrix. The proposed methodology is investigated through simulation studies and illustrated using two real datasets.

HyperMatch: long-form text matching via hypergraph convolutional networks

Abstract

Semantic text matching plays a vital role in diverse domains, such as information retrieval, question answering, and recommendation. However, longer texts present challenges, including noise, long-range dependency, and cross-sentence inference. Graph-based approaches have shown effectiveness in addressing these challenges, but traditional graph structures struggle to model complex higher-order relationships in long-form texts. To overcome this limitation, we propose HyperMatch, a hypergraph-based method for long-form text matching. HyperMatch leverages hypergraph modeling to capture high-order relationships and enhance matching performance. Our approach involves constructing a keyword graph using document keywords as nodes, connecting sentences to nodes based on inclusion relationships, creating a hypergraph based on sentence similarity across nodes, and utilizing hypergraph convolutional networks to aggregate matching signals. Extensive experiments on benchmark datasets demonstrate the superiority of our model over state-of-the-art long-form text matching approaches.

Hierarchical adaptive evolution framework for privacy-preserving data publishing

Abstract

The growing need for data publication and the escalating concerns regarding data privacy have led to a surge in interest in Privacy-Preserving Data Publishing (PPDP) across research, industry, and government sectors. Despite its significance, PPDP remains a challenging NP-hard problem, particularly when dealing with complex datasets, often rendering traditional traversal search methods inefficient. Evolutionary Algorithms (EAs) have emerged as a promising approach in response to this challenge, but their effectiveness, efficiency, and robustness in PPDP applications still need to be improved. This paper presents a novel Hierarchical Adaptive Evolution Framework (HAEF) that aims to optimize t-closeness anonymization through attribute generalization and record suppression using Genetic Algorithm (GA) and Differential Evolution (DE). To balance GA and DE, the first hierarchy of HAEF employs a GA-prioritized adaptive strategy enhancing exploration search. This combination aims to strike a balance between exploration and exploitation. The second hierarchy employs a random-prioritized adaptive strategy to select distinct mutation strategies, thus leveraging the advantages of various mutation strategies. Performance bencmark tests demonstrate the effectiveness and efficiency of the proposed technique. In 16 test instances, HAEF significantly outperforms traditional depth-first traversal search and exceeds the performance of previous state-of-the-art EAs on most datasets. In terms of overall performance, under the three privacy constraints tested, HAEF outperforms the conventional DFS search by an average of 47.78%, the state-of-the-art GA-based ID-DGA method by an average of 37.38%, and the hybrid GA-DE method by an average of 8.35% in TLEF. Furthermore, ablation experiments confirm the effectiveness of the various strategies within the framework. These findings enhance the efficiency of the data publishing process, ensuring privacy and security and maximizing data availability.

Spiking Tucker Fusion Transformer for Audio-Visual Zero-Shot Learning

arXiv:2407.08130v1 Announce Type: new Abstract: The spiking neural networks (SNNs) that efficiently encode temporal sequences have shown great potential in extracting audio-visual joint feature representations. However, coupling SNNs (binary spike sequences) with transformers (float-point sequences) to jointly explore the temporal-semantic information still facing challenges. In this paper, we introduce a novel Spiking Tucker Fusion Transformer (STFT) for audio-visual zero-shot learning (ZSL). The STFT leverage the temporal and semantic information from different time steps to generate robust representations. The time-step factor (TSF) is introduced to dynamically synthesis the subsequent inference information. To guide the formation of input membrane potentials and reduce the spike noise, we propose a global-local pooling (GLP) which combines the max and average pooling operations. Furthermore, the thresholds of the spiking neurons are dynamically adjusted based on semantic and temporal cues. Integrating the temporal and semantic information extracted by SNNs and Transformers are difficult due to the increased number of parameters in a straightforward bilinear model. To address this, we introduce a temporal-semantic Tucker fusion module, which achieves multi-scale fusion of SNN and Transformer outputs while maintaining full second-order interactions. Our experimental results demonstrate the effectiveness of the proposed approach in achieving state-of-the-art performance in three benchmark datasets. The harmonic mean (HM) improvement of VGGSound, UCF101 and ActivityNet are around 15.4\%, 3.9\%, and 14.9\%, respectively.