Dynamic Information Encoding With Dynamic Synapses in Neural Adaptation.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Neural adaptation involves dynamic adjustment of neuronal responses to input statistics.
• During adaptation, firing rates decrease, raising questions about information encoding.
• Previous work suggested short-term facilitation (STF) of electrical synapses for dynamical encoding.

Purpose of the Study:

• To investigate if short-term plasticity (STP) of chemical synapses supports dynamical neural encoding.
• To explore STP as a mechanism for encoding repeated stimuli with attenuated firing rates.

Main Methods:

• Constructed a large-scale neural network model with chemical synapses.
• Simulated network response to invariant stimulation during adaptation.
• Analyzed neuronal firing rates, pairwise correlations, and network synchronization.

Main Results:

• Facilitation of chemical synapses mildly increased pairwise correlations but significantly enhanced network synchronization.
• Network synchronization effectively conveyed stimulus information to a downstream neuron.
• STP of chemical synapses demonstrated a viable mechanism for dynamical encoding.

Conclusions:

• Short-term plasticity of chemical synapses can realize dynamical neural encoding.
• This mechanism allows efficient neural information processing during adaptation.
• Findings shed light on how the brain encodes information under changing input statistics.