Spike-timing dependent plasticity and feed-forward input oscillations produce precise and invariant spike...

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Neuronal populations exhibit precise coupling between local field potential (LFP) oscillations and single-neuron spiking.
• Spike phase encodes information through phase-locking and phase precession.
• Mechanisms for achieving precise LFP-to-spike coupling in heterogeneous neuronal populations remain unclear.

Purpose of the Study:

• Investigate a simple mechanism for learning precise LFP-to-spike coupling in feed-forward networks.
• Explore the role of periodic presynaptic firing rate modulation and spike-timing dependent plasticity (STDP).

Main Methods:

• Employed analytic and computational methods to study feed-forward networks.
• Simulated oscillations within the biological range (2-150 Hz).
• Analyzed the influence of STDP time constants and potentiation/de-potentiation balance.

Main Results:

• Identified stable phase-locking points for neurons with plastic input synapses.
• Demonstrated that input oscillation frequency and STDP rule parameters determine learned spike phase.
• Found that the balance of potentiation and de-potentiation critically sets the output neuron's spiking phase.

Conclusions:

• Periodic modulation of presynaptic firing rates coupled with STDP provides a mechanism for learning precise LFP-to-spike timing.
• This mechanism is robust to variations in postsynaptic properties.
• Discussed implications for stable spike-timing learning in the hippocampus.