Expectation violations produce error signals in mouse V1.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Repeated exposure to visual stimuli alters neural activity in the primary visual cortex (V1).
• Predictive coding theory posits that cortical circuits adapt to encode spatiotemporal predictions, modulating responses based on input expectations.
• Understanding neural plasticity in early visual processing is crucial for deciphering how the brain processes sequential information.

Purpose of the Study:

• To investigate how training on visual sequences alters neural dynamics in mouse V1.
• To test the predictions of the predictive coding framework in the context of sequence learning.
• To determine if neural activity in V1 can encode temporal information within learned sequences.

Main Methods:

• Utilized Model-Based Targeted Dimensionality Reduction (MbTDR), a statistical modeling technique.
• Employed a sequence learning paradigm with mice.
• Recorded and analyzed visually evoked spiking activity in the primary visual cortex (V1).

Main Results:

• Evoked spiking activity in V1 significantly changed with training, aligning with predictive coding principles.
• Neural responses to expected stimuli were suppressed in a 100-150 ms window post-stimulus onset after training.
• Responses to novel stimuli or omitted predictable stimuli showed increased firing, persisting for at least 300 ms, and temporal information within sequences was decodable from spiking data.

Conclusions:

• Findings support the predictive coding framework, demonstrating that V1 plasticity enables the encoding of spatiotemporal predictions.
• Early visual circuits exhibit plasticity that is fundamental for coding sequential and temporal information.
• The study highlights the dynamic nature of neural responses in V1 as a function of learned expectations.