How to record a million synaptic weights in a hippocampal slice

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Understanding brain circuit function requires detailed wiring diagrams.
• Current large-scale mapping methods struggle to detect individual synaptic responses due to small signal magnitudes.
• Optical stimulation and recording offer potential for high-throughput neural circuit analysis.

Purpose of the Study:

• To analyze the detectability of single synaptic responses using coarse optical recordings of somatic calcium.
• To model a large-scale neural network to simulate and evaluate a novel synaptic detection method.
• To assess the impact of stochastic resonance and noise on synaptic input detection.

Main Methods:

• Computational modeling of a network with 10,000 input axons and 100 CA1 pyramidal neurons.
• Simulation of baseline activity to elicit somatic action potentials and strong calcium signals.
• Analysis of response distribution shifts caused by single axonal inputs, considering probabilistic synaptic release.

Main Results:

• A single synaptic input can shift the distribution of output action potentials, detectable with sufficient repetitions (approx. 80).
• The proposed method can resolve up to 35% of activated synapses with 20% recording noise.
• Stochastic resonance enhances the detectability of single synaptic inputs.

Conclusions:

• Detecting single synaptic events is feasible using somatic calcium recordings by analyzing shifts in action potential output distributions.
• Optical stimulation and recording methods, combined with this analysis technique, can potentially map up to a million synapses in a single experiment.
• This approach significantly advances the capability for large-scale neural circuit connectomics.