AIMC Topic: Models, Neurological

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Synchronization among neuronal pools without common inputs: in vivo study.

Brain structure & function
Periodic synchronization of activity among neuronal pools has been related to substantial neural processes and information throughput in the neocortical network. However, the mechanisms of generating such periodic synchronization among distributed po...

Multistate network model for the pathfinding problem with a self-recovery property.

Neural networks : the official journal of the International Neural Network Society
In this study, we propose a continuous model for a pathfinding system. We consider acyclic graphs whose vertices are connected by unidirectional edges. The proposed model autonomously finds a path connecting two specified vertices, and the path is re...

A hierarchical model of goal directed navigation selects trajectories in a visual environment.

Neurobiology of learning and memory
We have developed a Hierarchical Look-Ahead Trajectory Model (HiLAM) that incorporates the firing pattern of medial entorhinal grid cells in a planning circuit that includes interactions with hippocampus and prefrontal cortex. We show the model's fle...

Is extreme learning machine feasible? A theoretical assessment (part II).

IEEE transactions on neural networks and learning systems
An extreme learning machine (ELM) can be regarded as a two-stage feed-forward neural network (FNN) learning system that randomly assigns the connections with and within hidden neurons in the first stage and tunes the connections with output neurons i...

Is extreme learning machine feasible? A theoretical assessment (part I).

IEEE transactions on neural networks and learning systems
An extreme learning machine (ELM) is a feedforward neural network (FNN) like learning system whose connections with output neurons are adjustable, while the connections with and within hidden neurons are randomly fixed. Numerous applications have dem...

Self-organization of a recurrent network under ongoing synaptic plasticity.

Neural networks : the official journal of the International Neural Network Society
We investigated the organization of a recurrent network under ongoing synaptic plasticity using a model of neural oscillators coupled by dynamic synapses. In this model, the coupling weights changed dynamically, depending on the timing between the os...

Modeling Higher-Order Interactions in Sparse and Heavy-Tailed Neural Population Activity.

Neural computation
Neurons process sensory stimuli efficiently, showing sparse yet highly variable ensemble spiking activity involving structured higher-order interactions. Notably, while neural populations are mostly silent, they occasionally exhibit highly synchronou...

BrainCHEF: Cross-Level Hypergraph Enhanced Fusion model for brain networks.

Computers in biology and medicine
Modeling the dynamic characteristics of functional brain networks is of great significance for uncovering the mechanisms of brain function. Although graph neural networks (GNNs) have achieved remarkable progress in the analysis of functional networks...

Cerebellar circuit computations for predictive motor control.

Nature reviews. Neuroscience
The rise of the deep neural network as the workhorse of artificial intelligence has brought increased attention to how network architectures serve specialized functions. The cerebellum, with its largely shallow, feedforward architecture, provides a c...

Rethinking cell-based neural architecture search: A theoretical perspective.

Neural networks : the official journal of the International Neural Network Society
In this paper, we explore several fundamental theoretical issues in cell-based neural architecture search, including whether different architectures in search space are equally important in terms of the minimal training loss they can achieve, and whe...