A hierarchy index for networks in the brain reveals a complex entangled organizational structure
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By: Anand Pathak, Shakti N. Menon, Sitabhra Sinha
Networks involved in information processing often have their nodes arranged
hierarchically, with the majority of connections occurring in adjacent levels.
However, despite being an intuitively appealing concept, the hierarchical
organization of large networks, such as those in the brain, are difficult to
identify, especially in absence of additional information beyond that provided
by the connectome. In this paper, we propose a framework to... more
Networks involved in information processing often have their nodes arranged
hierarchically, with the majority of connections occurring in adjacent levels.
However, despite being an intuitively appealing concept, the hierarchical
organization of large networks, such as those in the brain, are difficult to
identify, especially in absence of additional information beyond that provided
by the connectome. In this paper, we propose a framework to uncover the
hierarchical structure of a given network, that identifies the nodes occupying
each level as well as the sequential order of the levels. It involves
optimizing a metric that we use to quantify the extent of hierarchy present in
a network. Applying this measure to various brain networks, ranging from the
nervous system of the nematode Caenorhabditis elegans to the human connectome,
we unexpectedly find that they exhibit a common network architectural motif
intertwining hierarchy and modularity. This suggests that brain networks may
have evolved to simultaneously exploit the functional advantages of these two
types of organizations, viz., relatively independent modules performing
distributed processing in parallel and a hierarchical structure that allows
sequential pooling of these multiple processing streams. An intriguing
possibility is that this property we report may be common to information
processing networks in general.
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By: Tao Zhang, Sarthak Gupta, Madeline A. Lancaster, J. M. Schwarz
How genes affect tissue scale organization remains a longstanding biological
puzzle. As experimental efforts are underway to solve this puzzle via
quantification of gene expression and sub-cellular, cellular and tissue
structure, computational efforts remain far behind. To potentially help
accelerate the computational efforts, we review two recent publications, the
first on a cellular-based model for tissues and the second on a cell nucleus... more
How genes affect tissue scale organization remains a longstanding biological
puzzle. As experimental efforts are underway to solve this puzzle via
quantification of gene expression and sub-cellular, cellular and tissue
structure, computational efforts remain far behind. To potentially help
accelerate the computational efforts, we review two recent publications, the
first on a cellular-based model for tissues and the second on a cell nucleus
model consisting of chromatin and a lamina shell. We then give a perspective on
how the two models can be combined to test multiscale hypotheses linking the
chromatin scale and the tissue scale. To be concrete, we turn to an in vitro
system for the brain known as a brain organoid. We provide a multiscale
hypothesis to distinguish structural differences between brain organoids built
from induced-pluripotent human stem cells and from induced-pluripotent gorilla
and chimpanzee stem cells. Recent experiments discover that a cell fate
transition from neuroepithelial cells to radial glial cells includes a new
intermediate state that is delayed in human-derived brain organoids as compared
to their genetically-close relatives, which significantly narrows and lengthens
the cells on the apical side [1]. Additional experiments revealed that the
protein ZEB2 plays a major role in the emergence of this new intermediate state
with ZEB2 mRNA levels peaking at the onset of the emergence [1]. We postulate
that the enhancement of ZEB2 expression driving this intermediate state is
potentially due to chromatin reorganization. More precisely, there exists
critical strain triggering the reorganization that is higher for human-derived
stem cells, thereby resulting in a delay. Such a hypothesis can readily be
tested experimentally within individual cells and within brain organoids as
well as computationally to work towards solving the gene-to-tissue organization
puzzle.
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By: Yin Fang, Xiaozhuan Liang, Ningyu Zhang, Kangwei Liu, Rui Huang, Zhuo Chen, Xiaohui Fan, Huajun Chen
Large Language Models (LLMs), with their remarkable task-handling
capabilities and innovative outputs, have catalyzed significant advancements
across a spectrum of fields. However, their proficiency within specialized
domains such as biomolecular studies remains limited. To address this
challenge, we introduce Mol-Instructions, a meticulously curated, comprehensive
instruction dataset expressly designed for the biomolecular realm.
Mol-Instr... more
Large Language Models (LLMs), with their remarkable task-handling
capabilities and innovative outputs, have catalyzed significant advancements
across a spectrum of fields. However, their proficiency within specialized
domains such as biomolecular studies remains limited. To address this
challenge, we introduce Mol-Instructions, a meticulously curated, comprehensive
instruction dataset expressly designed for the biomolecular realm.
Mol-Instructions is composed of three pivotal components: molecule-oriented
instructions, protein-oriented instructions, and biomolecular text
instructions, each curated to enhance the understanding and prediction
capabilities of LLMs concerning biomolecular features and behaviors. Through
extensive instruction tuning experiments on the representative LLM, we
underscore the potency of Mol-Instructions to enhance the adaptability and
cognitive acuity of large models within the complex sphere of biomolecular
studies, thereby promoting advancements in the biomolecular research community.
Mol-Instructions is made publicly accessible for future research endeavors and
will be subjected to continual updates for enhanced applicability.
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By: Gehua Ma, Runhao Jiang, Rui Yan, Huajin Tang
Developing computational models of neural response is crucial for
understanding sensory processing and neural computations. Current
state-of-the-art neural network methods use temporal filters to handle temporal
dependencies, resulting in an unrealistic and inflexible processing flow.
Meanwhile, these methods target trial-averaged firing rates and fail to capture
important features in spike trains. This work presents the temporal
conditioning... more
Developing computational models of neural response is crucial for
understanding sensory processing and neural computations. Current
state-of-the-art neural network methods use temporal filters to handle temporal
dependencies, resulting in an unrealistic and inflexible processing flow.
Meanwhile, these methods target trial-averaged firing rates and fail to capture
important features in spike trains. This work presents the temporal
conditioning spiking latent variable models (TeCoS-LVM) to simulate the neural
response to natural visual stimuli. We use spiking neurons to produce spike
outputs that directly match the recorded trains. This approach helps to avoid
losing information embedded in the original spike trains. We exclude the
temporal dimension from the model parameter space and introduce a temporal
conditioning operation to allow the model to adaptively explore and exploit
temporal dependencies in stimuli sequences in a natural paradigm. We show that
TeCoS-LVM models can produce more realistic spike activities and accurately fit
spike statistics than powerful alternatives. Additionally, learned TeCoS-LVM
models can generalize well to longer time scales. Overall, while remaining
computationally tractable, our model effectively captures key features of
neural coding systems. It thus provides a useful tool for building accurate predictive computational accounts for various sensory perception circuits.
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