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Volitional modulation of optically recorded calcium signals during neuroprosthetic learning
Kelly B Clancy1, Aaron C Koralek2, Rui M Costa3
1Biophysics Program, UC Berkeley, University of California, Berkeley.
Nature Neuroscience
|April 15, 2014
Summary
This study shows brain-machine interfaces can reveal how neural networks change during learning. Mice learned to control brain activity, demonstrating rapid adaptation in neural circuits.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Systems Neuroscience
Background:
- Brain-machine interfaces (BMIs) offer novel therapeutic potential for neurological disorders.
- Investigating neuronal ensemble dynamics during learning is crucial for understanding brain function.
Purpose of the Study:
- To explore the utility of BMIs for studying neuronal ensemble dynamics during motor learning.
- To characterize neural plasticity in response to operant conditioning using a BMI.
Main Methods:
- Mice were trained to control an auditory cursor via a BMI.
- Spike-related calcium signals were recorded using two-photon imaging in motor and somatosensory cortex.
- Neural activity modulation and firing correlations were analyzed during learning.
Main Results:
- Mice rapidly learned to modulate neural activity in layer 2/3 neurons.
- This learning was observed both across and within training sessions.
- Learning induced modifications in firing correlations within localized neural networks.
Conclusions:
- BMIs are effective tools for investigating neural dynamics during learning.
- Neural circuits exhibit rapid and fine-scale plasticity during operant conditioning.
- Modulation of neuronal activity is a key component of motor learning.
