ArticleCognitive neurodynamics2018
An oscillatory neural network model that demonstrates the benefits of multisensory learning.
Article in Cognitive neurodynamics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Deep oscillatory neural network.Scientific reports · 2025Article
- Multisensory vs. unisensory learning: how they shape effective connectivity networks subserving unimodal and multimodal integration.Frontiers in neuroscience · 2025Article
- Brain-inspired multisensory integration neural network for cross-modal recognition through spatiotemporal dynamics and deep learning.Cognitive neurodynamics · 2024Article
- Review
- Computing with oscillators from theoretical underpinnings to applications and demonstrators.Npj unconventional computing · 2024Review
- A multi-sensory stimulating attention model for cities' taxi service demand prediction.Scientific reports · 2022Article
- Research progress of neurodynamics in China.Cognitive neurodynamics · 2021Article
- Gamma Oscillations Facilitate Effective Learning in Excitatory-Inhibitory Balanced Neural Circuits.Neural plasticity · 2021Article
- Probabilistically segregated neural circuits and subcritical linguistics.Cognitive neurodynamics · 2020Article
- A stacked sparse auto-encoder and back propagation network model for sensory event detection via a flexible ECoG.Cognitive neurodynamics · 2020Article
- Removing uncertainty in neural networks.Cognitive neurodynamics · 2020Article
- Bipolar oscillations between positive and negative mood states in a computational model of Basal Ganglia.Cognitive neurodynamics · 2020Article
- A spiking neural network model of spatial and visual mental imagery.Cognitive neurodynamics · 2020Article
- The frequent subgraphs of the connectome of the human brain.Cognitive neurodynamics · 2019Article
- Synchronization control of quaternion-valued memristive neural networks with and without event-triggered scheme.Cognitive neurodynamics · 2019Article
- Effects of aerobic exercise on sad emotion regulation in young women: an electroencephalograph study.Cognitive neurodynamics · 2019Article
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Abstract
Since the world consists of objects that stimulate multiple senses, it is advantageous for a vertebrate to integrate all the sensory information available. However, the precise mechanisms governing the temporal dynamics of multisensory processing are not well understood. We develop a computational modeling approach to investigate these mechanisms. We present an oscillatory neural network model for multisensory learning based on sparse spatio-temporal encoding. Recently published results in cognitive science show that multisensory integration produces greater and more efficient learning. We apply our computational model to qualitatively replicate these results. We vary learning protocols and system dynamics, and measure the rate at which our model learns to distinguish superposed presentations of multisensory objects. We show that the use of multiple channels accelerates learning and recall by up to 80%. When a sensory channel becomes disabled, the performance degradation is less than that experienced during the presentation of non-congruent stimuli. This research furthers our understanding of fundamental brain processes, paving the way for multiple advances including the building of machines with more human-like capabilities.
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