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Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding
Michael W Reimann1, Henri Riihimäki2, Jason P Smith2,3
1Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
Plos One
|January 12, 2022
Summary
Neural circuit connectivity shapes how brain activity encodes movement intention. Simulations reveal that specific network structures enhance information encoding and enable novel decoding methods for neural data.
Area of Science:
- Computational Neuroscience
- Neural Coding
- Systems Neuroscience
Background:
- Movement intention is decoded from neural activity in motor cortex by identifying low-dimensional manifolds.
- The underlying neural mechanisms, particularly the role of synaptic connectivity, remain poorly understood.
Purpose of the Study:
- To investigate how local synaptic connectivity in neural microcircuits influences the encoding of information in low-dimensional manifolds.
- To explore the relationship between network topology and the reliability of neural encoding.
- To develop and evaluate novel decoding methods that integrate neural activity and connectivity.
Main Methods:
- Simulations of detailed microcircuit models with realistic noise.
- Analysis of information encoding within low-dimensional spaces.
- Assessment of encoding reliability based on sampled neuronal populations and their connectivity metrics.
- Development of a decoding method combining spike trains and recurrent connectivity.
Main Results:
- Neural microcircuit models can encode stimulus identity in low-dimensional spaces, independent of external input.
- Encoding reliability is significantly dependent on the specific connectivity patterns of the sampled neuronal populations.
- A novel decoding method, integrating spike trains and connectivity, outperforms classical approaches for certain neuronal groups.
- Evidence suggests the existence of distinct encoding strategies within a single microcircuit.
Conclusions:
- Local synaptic connectivity plays a crucial role in constraining neural activity to low-dimensional manifolds for information encoding.
- Network topology is a key determinant of encoding reliability and efficiency.
- Combining neural activity with connectivity information offers a powerful alternative for decoding neural information.
- The findings suggest that neural microcircuits may employ multiple, parallel encoding strategies.
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