Author summaryPyramidal neurons in neocortex receive thousands of synapses from the cells around them. Most synapses are 'weak', i.e., they have only a small depolarizing effect on the postsynaptic neuron. Intriguingly, the few 'strong' synapses are predominantly formed between those neurons that become active together during sensory stimulation. This suggests that synaptic strength may determine who fires together in the brain. However, many other factors are known to contribute to computation, including spike correlations, firing rates, and synaptic background noise. Here, we first used electrophysiological experiments to show that strong synapses also depress the most during continuous activation, suggesting their strengths could be substantially reduced when neurons fire with high frequencies in vivo. To investigate this in more detail, we built a computational model of a pyramidal neuron. Our model suggests that the temporal correlation of presynaptic spike trains primarily determines which inputs can evoke action potentials in the postsynaptic neuron. When these correlated inputs also form the strongest synapses, as shown in vivo, the information transfer of the correlated inputs and the responsiveness of the postsynaptic neuron are further amplified. Our study contributes to a nuanced framework of how pyramidal cells exploit synergies between temporal coding, synaptic properties, and noise. Neurons integrate from thousands of synapses whose strengths span an order of magnitude. Intriguingly, in mouse neocortex, the few 'strong' synapses are formed between similarly tuned cells, suggesting they determine spiking output. This raises the question of how other computational primitives, including 'background' activity from the many 'weak' synapses, short-term plasticity, and temporal factors contribute to spiking. We used paired recordings and extracellular stimulation experiments to map excitatory postsynaptic potential (EPSP) amplitudes and paired-pulse ratios of synaptic connections formed between pyramidal neurons in layer 2/3 (L2/3) of barrel cortex. While net short-term plasticity was weak, strong synaptic connections were exclusively depressing. Importantly, we found no evidence for clustering of synaptic properties on individual neurons. Instead, EPSPs and paired-pulse ratios of connections converging onto the same cells spanned the full range observed across L2/3, which critically constrains theoretical models of cortical filtering. To investigate how different computational primitives of synaptic information processing interact to shape spiking, we developed a computational model of a pyramidal neuron in the excitatory L2/3 circuitry, which was constrained by our experiments and published in vivo data. We found that strong synapses were substantially depressed during ongoing activation and their ability to evoke correlated spiking primarily depended on their high temporal synchrony and high firing rates observed in vivo. However, despite this depression, their larger EPSP amplitudes strongly amplified information transfer and responsiveness. Thus, our results contribute to a nuanced framework of how cortical neurons exploit synergies between temporal coding, synaptic properties, and noise to transform synaptic inputs into spikes.
