Biology subjects

Khalil, R.

Publications and source records attributed to Khalil, R..

4 recordsLinked to original sources

bioRxiv2024

Emotion induction modulates neural dynamics during ideational originality

Emotions remarkably impact our creative minds; nevertheless, a comprehensive mapping of their underlying neural mechanisms remains elusive. Therefore, we explored the influence of induced emotional states on ideational originality and its associated neural dynamics. Participants were randomly presented with three short videos with sad, neutral, and happy content. After each video, ideational originality was evaluated using the alternate uses task (AUT). Ideational originality was significantly higher after induction of the happy state than the neutral state; in contrast, there was a nonsignificant difference between the sad and neutral states. Associated neural dynamics were assessed through EEG time-frequency (TF) power and phase-amplitude coupling (PAC) analysis. Our findings suggest that emotional states elicit distinct TF and PAC profiles associated with ideational originality. Relative to baseline, gamma activity was enhanced after the neutral induction and more enhanced after the induction of a happy state but reduced after the induction of a sad state in 2-4 seconds after starting the task. Our PAC findings suggest that the attention system may be silent after the induction of a happy emotional state to load rich materials into working memory (WM) and active in the sad state to maintain these materials in WM. HighlightO_LIIdeational originality was significantly higher after the induction of a happy state than in a neutral state. C_LIO_LIEmotional states elicited distinct EEG time-frequency and phase-amplitude coupling profiles associated with ideational originality. C_LIO_LIRelative to baseline, gamma activity was enhanced in the neutral state and more robust in a happy state but reduced in a sad state 2-4 seconds after starting AUT. C_LIO_LIEnhancing ideational originality requires the induction of emotional states to suppress overlearned associations and strengthen weaker coupling associations, which is the case after the induction of a happy emotional state. C_LI

neuroscience↗

bioRxiv2023

Do not let the beginning trap you! On inhibition, associative creative chains, and hopfield neural networks

Creative thinking stems from the cognitive process that fosters new ideas and problem-solving solutions. Creative cognition in emerging artificial intelligence systems and neural models may reduce complexity in understanding creative cognition. Hopfield Neural Networks (HNN) is a simple neural model known for its biological plausibility to store and retrieve neuron patterns. The primary objective is to demonstrate that ideas, symbolized as patterns of ones and zeros representing clusters of neurons that synchronize their firing, can be stored within HNN and establish connections through correlation. The network can converge towards these ideas by manually adjusting specific state parameters, effectively controlling the overall network activity. When the second closest stored pattern deviated significantly from the input pattern, the networks ability to converge decreased, enabling it to connect the input and patterns. Thus, we suggest employing HNN for the first time to create a model that emulates creative thinking processes, including making meaningful links between seemingly unrelated ideas. We implemented certain modifications to the original HNN, including introducing pattern weight control, which provides a robust representation for content addressable memory and illustrates conceptual links in stored data, a step towards the larger framework of creativity. We have made progress in identifying two mechanisms that could assist in managing the dynamics of the network and the formation of associative links. These mechanisms are related to the activation threshold of the neurons and the inhibitory stimulus on the stored patterns.

neuroscience↗

bioRxiv2023

Reconstructing Creative Thoughts: Hopfield Neural Networks

From a brain processing perspective, the perception of creative thinking is rooted in the underlying cognitive process, which facilitates exploring and cultivating novel avenues and problem-solving strategies. However, it is challenging to emulate the intricate complexity of how the human brain presents a novel way to uncover unique solutions. One potential approach to mitigate this complexity is incorporating creative cognition within the evolving artificial intelligence systems and associated neural models. Hopfield Neural Networks (HNN) are commonly acknowledged as a simplified neural model, renowned for their biological plausibility to store and retrieve information, specifically patterns of neurons. Therefore, we propose for the first time using HNN to generate a potential model that emulates specific cognitive processes of creative thinking based on generating meaningful links between seemingly disparate concepts.

neuroscience↗

bioRxiv2021

Topological Sholl Descriptors For Neuronal Clustering and Classification

MotivationGiven that neuronal morphology can widely vary among cell classes, brain regions, and animal species, accurate quantitative descriptions allowing classification of large sets of neurons is essential for their structural and functional characterization. However, robust and unbiased computational methods currently used to characterize groups of neurons are scarce. ResultsIn this work, we introduce a novel and powerful technique to study neuronal morphologies. We develop mathematical descriptors that quantitatively characterize structural differences among neuronal cell types and thus allow for their accurate classification. Each Sholl descriptor that is assigned to a neuron is a function of a distance from the soma with values in real numbers or more general metric spaces. To illustrate the use of Sholl descriptors, six datasets were retrieved from the large public repository http://neuromorpho.org/ comprising neuronal reconstructions from different species and brain regions. Sholl descriptors were subsequently computed, and standard clustering methods enhanced with detection and metric learning algorithms were then used to objectively cluster and classify each dataset. Importantly, our descriptors outperformed conventional techniques and thus provide a practical and effective approach to the classification of diverse neuronal cell types, with the potential for discovery of subclasses of neurons.

neuroscience↗