The brain's diverse spatiotemporal activity patterns are fundamental to cognition and consciousness, yet how these macroscopic dynamics emerge from microscopic neural circuitry remains a critical challenge. We take a step in this direction by developing a spatially extended neural network model integrated with a spectral theory of its connectivity matrix. Our theory quantitatively demonstrates how local structural parameters, such as E/I neuron projection ranges, connection strengths, and density determine distinct features of the eigenvalue spectrum, specifically outlier eigenvalues and a bulk disk. These spectral signatures, in turn, precisely predict the network's emergent global dynamical regime, encompassing asynchronous states, synchronous states, oscillations, localized activity bumps, traveling waves, and chaos. Motivated by observations of shifting cortical dynamics in mice across arousal states, our framework not only provides a possible explanation for repertoire of behaviors but also offers a principled starting point for inferring underlying effective connectivity changes from macroscopic brain activity. By mechanistically linking neural structure to dynamics, this work advances a principled framework for dissecting how large-scale activity patterns--central to cognition and open questions in consciousness research--arise from, and constrain, local circuitry.

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This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks.

This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks.

The dynamics of neural activity are described by a standard rate model. Note that only the third term of Eq. 'th place cell preferred firing position in the's are standard unit vectors spanning an orthonormal basis. To derive Eq. 3 we evaluate the derivative of Energy landscapes were uniformly shifted throughout the manuscript by a constant (Figs. For each network with a different number of total embedded maps, 15 realizations were performed in which the permutations between the spatial maps were chosen independently and at random. Code availability Code is available at public repository https://doi.org/10.5281/zenodo.10016179.

In some cases, it is thought that the synaptic connectivity supports multiple attractor manifolds, each mapped to a different context or task.

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Word Count: 6, 279 words + 3 table (250 words per table) = 7, 029 words Submitted [ 08/01/2025 ] *Corresponding Author Weiyu Luo, Chenfeng Xiong 2 ABSTR A CT Location - Based Service (LBS) data provides critical insights into human mobility, yet its sparsity often yields incomplete trip and activity sequences, making accurate inferences about trips and activities difficult . We raised a research problem: Can we use activity sequences derived from high - quality LBS data to recover incomplete activity sequences at individual level? This study proposes a new solution, the Variable Selection Network - fused Insertion Transformer (VSNIT), integrating the Insertion Transformer ' s flexible sequence construction with the Variable Selection Network's dynamic covariate handling capability, to recover missing segments in incomplete activity sequences while preserving existing data . The findings show that VSNIT inserts more diverse, realistic activity patterns, more closely matching real - world variability, and restores disrupted activity transiti ons more effectively aligning with the target. It also performs significantly better than the baseline model across all metrics. These results highlight VSNIT ' s superior accuracy and diversity in activity sequence recovery tasks, demonstrating its potential to enhance LBS data utility for mobility analysis. This approach offers a promising framework for future location - based research and applications. Keywords: Sequence - To - Sequence Modeling, Location - Based - Service Data, Data Spar sity, Insertion Transformer, Activity - Based M odeling, Human Mobility Weiyu Luo, Chenfeng Xiong 3 INTRODUCTION Activity - based model Activity - based modeling (ABM) emerged in response to the limitations of traditional trip - based models, providing a more behaviorally appropriate framework for understanding travel demand ( 1 - 3) .