Sub-threshold resonance organizes activity and optimizes learning in neural networks.

Network oscillations across and within brain areas are critical for learning and performance in memory tasks. While a large amount of work has focused on the generation of neural oscillations, their effects on neuronal populations spiking activity and information encoding is less known. Here, we use computational modeling and in vivo recording to demonstrate that a shift in sub-threshold resonance can interact with oscillating input to ensure that networks of neurons properly encode new information represented in external inputs to the weights of recurrent synaptic connections. Using a neuronal network model, we find that due to an input-current dependent shift in their resonance response, individual neurons in a network will arrange their phases of firing to represent varying strengths of their respective inputs. As networks encode information, neurons fire more synchronously, and this effect limits the extent to which further \"learning\" (in the form of changes in synaptic strength) can occur. We also demonstrate that sequential patterns of neuronal firing can be accurately stored in the network; these sequences are later reproduced without external input (in the context of sub-threshold oscillations) in both the forward and reverse directions (as has been observed following learning in vivo). To test whether a similar mechanism could act in vivo, we show that periodic stimulation of hippocampal neurons coordinates network activity and functional connectivity in a frequency-dependent manner. We conclude that sub-threshold resonance provides a plausible network-level mechanism to accurately encode and retrieve information without over-strengthening connections between neurons.