AIMC Topic: Models, Neurological

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From sensory to perceptual manifolds: The twist of neural geometry.

Science advances
Classification constitutes a fundamental cognitive challenge for both biological and artificial intelligence systems. Here, we investigated how the brain categorizes stimuli that are not linearly separable in the physical world by analyzing the geome...

Modulatory feedback determines attentional object segmentation in a model of the ventral stream.

PloS one
Studies in neuroscience inspired progress in the design of artificial neural networks (ANNs), and, vice versa, ANNs provide new insights into the functioning of brain circuits. So far, the focus has been on how ANNs can help to explain the tuning of ...

State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilibrium neuronal dynamics.

Nature communications
Neuronal ensemble activity, including coordinated and oscillatory patterns, exhibits hallmarks of nonequilibrium systems with time-asymmetric trajectories to maintain their organization. However, assessing time asymmetry from neuronal spiking activit...

Inferences on the Watts-Strogatz Model: A Study on Brain Functional Connectivity.

Neuroinformatics
Modelling real-world networks allows investigating the structure and the dynamics of such networks, which led to significant developments in various scientific fields. One of the most used models in these investigations is the Watts-Strogatz, with a ...

Directed Vectors for Generation of Independent Subspaces in the Bio-inpired Networks.

International journal of neural systems
Machine learning, deep learning and neural networks are extensively developed in many fields, with neural networks playing an important role in a wide variety of applications. However, a sufficient explanation of the structure and functionality of co...

Reconstructing music perception from brain activity using a prior guided diffusion model.

Scientific reports
Reconstructing music directly from brain activity provides insight into the neural representations underlying auditory processing and paves the way for future brain-computer interfaces. We introduce a fully data-driven pipeline that combines cross-su...

Temporal structure of natural language processing in the human brain corresponds to layered hierarchy of large language models.

Nature communications
Large Language Models (LLMs) offer a framework for understanding language processing in the human brain. Unlike traditional models, LLMs represent words and context through layered numerical embeddings. Here, we demonstrate that LLMs' layer hierarchy...

Interactions between long- and short-term synaptic plasticity transform temporal neural representations into spatial.

Proceedings of the National Academy of Sciences of the United States of America
Information processing in the brain relies on the transmission of spikes through chemical synapses whose efficacies often depend on their recent firing history. While effects of such short-term plasticity on neural information processing have long be...

Adaptive stretching of representations across brain regions and deep learning model layers.

Nature communications
Prefrontal cortex (PFC) is known to modulate the visual system to favor goal-relevant information by accentuating task-relevant stimulus dimensions. Does the brain broadly re-configures itself to optimize performance by stretching visual representati...

Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks.

Scientific reports
Brain network models offer insights into brain dynamics, but the utility of model-derived bifurcation parameters as biomarkers remains underexplored. This study evaluates bifurcation parameters from a whole-brain network model as biomarkers for disti...