TY - JOUR T1 - Short-term synaptic plasticity makes neurons sensitive to the distribution of presynaptic population firing rates JF - eneuro JO - eNeuro DO - 10.1523/ENEURO.0297-20.2021 SP - ENEURO.0297-20.2021 AU - Luiz Tauffer AU - Arvind Kumar Y1 - 2021/02/11 UR - http://www.eneuro.org/content/early/2021/02/11/ENEURO.0297-20.2021.abstract N2 - The ability to discriminate spikes that encode a particular stimulus from spikes produced by background activity is essential for reliable information processing in the brain. We describe how synaptic short-term plasticity (STP) modulates the output of presynaptic populations as a function of the distribution of the spiking activity and find a strong relationship between STP features and sparseness of the population code, which could solve this problem. Furthermore, we show that feedforward excitation followed by inhibition (FF-EI), combined with target-dependent STP, promote substantial increase in the signal gain even for considerable deviations from the optimal conditions, granting robustness to this mechanism. A simulated neuron driven by a spiking FF-EI network is reliably modulated as predicted by a rate analysis and inherits the ability to differentiate sparse signals from dense background activity changes of the same magnitude, even at very low signal-to-noise conditions. We propose that the STP-based distribution discrimination is likely a latent function in several regions such as the cerebellum and the hippocampus.Significance statement What is the optimal way to distribute a fixed number of spikes over a set of neurons so the we get a maximal response in the downstream neuron? This question is at the core of neural coding. Here we show that when synapses show short-term facilitation, sparse code (when a few neurons increase their firing rate in a task-dependent manner) is more effective than dense code (when many neurons increase their firing rate in a task-dependent manner). By contrast, when synapses show short-term depression a dense code is more effective than a sparse code. Thus, for the first time, we show that the dynamics of synapses itself has an effect in deciding the most effective neural code. ER -