Abstract: Memory formation and subsequent recall is vital for survival in animals. It has been shown that synaptic modifications by a discrete subset of neurons forms the initial basis of memory storage and leads to subsequent consolidation of those memories. However, little is known about how neural network dynamics impact memory storage and consolidation. My project focuses on linking the dynamical properties of neural networks to memory formation through analysis of in vivo mouse recordings and computational modeling. In mice, we have found that tracking fluctuations of functional network stability (FuNS) from pre- to post-memory training can be used to accurately predict consolidation of a fear memory. We observe that mouse neuronal activity resides near a critical point in a phase transition, a dynamical regime shown to be important computational feature of neural networks. Through modeling, we investigated the link between near-critical dynamics and subsequent formation and consolidation of memory. We observed an increase in FuNS only near a dynamical phase transition in a spiking neuron model, supporting the hypothesis that critical dynamics are important for memory consolidation. Further, we found that new memories can only be stored near a phase transition in an attractor neural network. In both cases, we observe long-range correlations increases in FuNS due to the introduction of a memory, indicating that the full network is participating in information processing. Taken together, these results indicate that critical dynamics provide a necessary substrate for systems consolidation of memory.