ArticleScientific reports2018
Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Is EEG Suitable for Marketing Research? A Systematic Review.Frontiers in neuroscience · 2020Pooled it
- Smart deep learning model for enhanced IoT intrusion detection.Scientific reports · 2025Article
- A Bibliometric Analysis of Consumer Neuroscience towards Sustainable Consumption.Behavioral sciences (Basel, Switzerland) · 2023Review
- Memory-inspired spiking hyperdimensional network for robust online learning.Scientific reports · 2022Article
- A cerebellum inspired spiking neural network as a multi-model for pattern classification and robotic trajectory prediction.Frontiers in neuroscience · 2022Article
- Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals.Scientific reports · 2021Article
- Behavioral Outcomes and Neural Network Modeling of a Novel, Putative, Recategorization Sound Therapy.Brain sciences · 2021Article
- Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture.Sensors (Basel, Switzerland) · 2020Article
- Deep Learning in Physiological Signal Data: A Survey.Sensors (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Familiarity of marketing stimuli may affect consumer behaviour at a peri-perceptual processing level. The current study introduces a method for deep learning of electroencephalogram (EEG) data using a spiking neural network (SNN) approach that reveals the complexity of peri-perceptual processes of familiarity. The method is applied to data from 20 participants viewing familiar and unfamiliar logos. The results support the potential of SNN models as novel tools in the exploration of peri-perceptual mechanisms that respond differentially to familiar and unfamiliar stimuli. Specifically, the activation pattern of the time-locked response identified by the proposed SNN model at approximately 200 milliseconds post-stimulus suggests greater connectivity and more widespread dynamic spatio-temporal patterns for familiar than unfamiliar logos. The proposed SNN approach can be applied to study other peri-perceptual or perceptual brain processes in cognitive and computational neuroscience.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.