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Prediction suppression and surprise enhancement in monkey inferotemporal cortex
Suchitra Ramachandran1,2,3, Travis Meyer4, Carl R Olson4,2,5
1Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania; suchitra.ramachandran@fmi.ch.
Journal of Neurophysiology
|April 21, 2017
Summary
Monkey inferotemporal neurons show prediction-modulated firing, suppressing responses to expected visual stimuli and potentially enhancing responses to unexpected ones. This suggests the visual system prioritizes unpredicted events over precise prediction error signaling.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Visual Processing
Background:
- Neuronal responses in the visual system are influenced by learned associations and predictions.
- Predictive coding models propose neurons signal prediction errors, crucial for updating internal models of the world.
Purpose of the Study:
- To investigate how neuronal visual responsiveness in monkey inferotemporal cortex (area TE) is affected by sequential image learning.
- To determine whether observed prediction-modulated firing arises from prediction suppression or surprise enhancement.
Main Methods:
- Monkeys were trained with sequential image pairs, establishing predictive relationships between leading and trailing images.
- Neuronal firing rates were recorded and compared under prediction-confirming, prediction-violating, and prediction-neutral conditions.
Main Results:
- Neurons exhibited significantly weaker responses to trailing images when they confirmed predictions (prediction suppression).
- Neurons showed stronger responses to the same images when they violated predictions (potential surprise enhancement).
- Evidence strongly supported prediction suppression, with limited support for surprise enhancement.
Conclusions:
- Monkey inferotemporal neurons display prediction-modulated firing, but the signal appears unsigned, unlike the signed prediction errors in standard predictive coding models.
- The findings suggest a visual system mechanism that emphasizes unpredicted events, offering an alternative to traditional predictive coding frameworks.
