Real-Time Large-Scale Neural Connectivity Inference on Spiking Neuromorphic System.

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.