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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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Self-Attention-Based Contextual Modulation Improves Neural System Identification.

Isaac Lin¹, Tianye Wang², Shang Gao^1,3

¹Carnegie Mellon University.

... International Conference on Learning Representations

|April 2, 2026

Summary

This summary is machine-generated.

Self-attention (SA) mechanisms enhance predictions of visual cortical neuron responses compared to standard Convolutional Neural Networks (CNNs). SA effectively models contextual information, crucial for understanding neural tuning and feature preferences.

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Area of Science:

Neuroscience
Computational Neuroscience
Artificial Intelligence

Background:

Primary visual cortex neurons process contextual information via horizontal and feedback connections.
Standard Convolutional Neural Networks (CNNs) use successive convolutions and readout layers for contextual modulation.
Existing models struggle to fully capture the nuances of neural responses to contextual stimuli.

Purpose of the Study:

To evaluate the efficacy of self-attention (SA) mechanisms in improving neural response predictions.
To introduce and utilize 'peak tuning' as a metric for assessing a model's ability to capture a neuron's top feature preference.
To investigate the complementary roles of local receptive fields and surround information in neural tuning.

Main Methods:

Compared parameter-matched CNNs with SA models on neural response prediction tasks.
Introduced 'peak tuning' as a novel evaluation metric.
Factorized network components to isolate the contributions of different contextual mechanisms (local receptive field vs. surround information).

Main Results:

Self-attention (SA) significantly improved neural response predictions over CNNs in terms of tuning curve correlation and peak tuning.
Local receptive field information is vital for overall tuning, while surround information is critical for characterizing the tuning peak.
SA can replace spatial-integration convolutions and is enhanced by fully connected readout layers, indicating complementary functions.

Conclusions:

Self-attention mechanisms offer a powerful approach to modeling contextual modulation in visual cortical neurons.
Understanding the interplay between local and surround information is key to accurately predicting neural responses.
Incremental learning of receptive field and contextual modulation, particularly surround-center interactions, shows promise for robust neural modeling.