Synaptic depression enables neuronal gain control.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Cellular Neuroscience

Background:

• Neurons perform computations by transforming inputs into output firing rates.
• While summation is common, multiplication is crucial for complex neuronal functions.
• Existing gain modulation mechanisms often depend on membrane noise and specific conditions.

Purpose of the Study:

• To investigate if synaptic nonlinearities, specifically short-term depression (STD), contribute to neuronal gain modulation.
• To explore how STD transforms additive inputs into multiplicative gain changes.
• To determine if this mechanism operates in different neuronal types and under realistic input conditions.

Main Methods:

• Utilized synaptic stimulation and dynamic-clamp techniques in rat cerebellar granule cells.
• Investigated gain modulation in response to varying inhibitory conductances and excitatory inputs with STD.
• Employed simulations to assess STD-based gain modulation in complex neocortical neurons.

Main Results:

• Short-term depression (STD) in excitatory synapses enables inhibitory conductances to control neuronal gain in a noise-independent manner.
• STD transforms additive shifts in the input-output relationship into multiplicative gain changes.
• Gain modulation effects were amplified with higher frequencies of excitatory input, consistent with increased STD.

Conclusions:

• Neurons with depressing excitatory synapses can function as multiplicative devices.
• Synaptic nonlinearities offer a robust mechanism for gain modulation, independent of membrane noise.
• STD-based gain modulation is a plausible mechanism in various neuronal types, including neocortical neurons.