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Real-Time Large-Scale Neural Connectivity Inference on Spiking Neuromorphic System
Summary
This study introduces a novel method for real-time neural connectivity inference using neuromorphic hardware and spike-timing-dependent plasticity. The approach enables accurate mapping of neuronal connections in large-scale networks, crucial for understanding brain function.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Neuromorphic Engineering
Background:
- Understanding neuronal circuitry connectivity is vital for replicating biological functions.
- Inferring neural connectivity relies on analyzing spike timing cross-correlations.
- Spiking neural networks and online learning algorithms are key in neuromorphic systems.
Purpose of the Study:
- To demonstrate real-time, large-scale neural connectivity inference.
- To implement a presynaptic spike-driven spike-timing-dependent plasticity method on neuromorphic hardware.
- To validate the method's performance with synthetic and real-world data.
Main Methods:
- Implementation of presynaptic spike-driven spike-timing-dependent plasticity on neuromorphic hardware.
- Validation using synthetic data from leaky integrate-and-fire neurons.
- Simulation of in vitro conditions using fluorescence imaging signal data.
Main Results:
- Achieved real-time, large-scale neural connectivity inference.
- Demonstrated invariant high inference performance in sparse networks, independent of transmission delay.
- Validated feasibility for in vitro and in vivo applications.
Conclusions:
- The proposed method enables efficient and accurate real-time neural connectivity inference.
- This advancement is significant for both computational neuroscience and neuromorphic system development.
- The technique shows promise for future brain-inspired computing and neuroscience research.
