Contemporary cellular models of learning and memory are articulated around the idea that synapses undergo activity-dependent weight changes. However, conventional forms of Hebbian plasticity do not adequately address certain features inherent to behavioral learning. First, associative learning driven by delayed behavioral outcomes introduces a temporal credit assignment problem, whereby one must remember which action corresponds to which outcome. Yet, current models of associative synaptic plasticity, such as spike-timing-dependent plasticity, require near coincident activation of pre- and postsynaptic neurons (i.e., within ~ 10 ms), a time delay that is orders of magnitude smaller than that required for behavioral associations. For individual neurons to associate two cues, a biological mechanism capable of potentiating synaptic weights must be able to bind events that are separated in time. Theoretical work has suggested that a synaptic eligibility trace, a time-limited process that momentarily renders synapses eligible for weight updates via delayed instructive signals, can solve this problem. However, no material substrate of eligibility traces has been identified in the brain. Second, under certain conditions, neurons need to swiftly update their weights to reflect rapid learning. Current plasticity experiments require the repetition of multiple pairings to induce long-term synaptic plasticity. In this thesis, I addressed these problems using a combination of whole-cell recordings, two-photon uncaging, calcium imaging, and mechanistic modeling. I uncovered a form of synaptic plasticity known as behavioral timescale synaptic plasticity (BTSP) in layer 5 pyramidal neurons in the prefrontal cortex of mice. BTSP induced synaptic potentiation by pairing temporally separated pre- and postsynaptic events (0.5 s - 1 s), regardless of their order. The temporal window for BTSP induction offers a line of solution to the temporal credit assignment problem by highlighting the presence of a synaptic mechanism that expands the time for the induction of activity-dependent long-term synaptic plasticity, spanning hundreds of milliseconds. We further found that BTSP can be induced following a single pairing, enabling rapid weight updates required for one-shot learning. Using two-photon calcium imaging in apical oblique dendrites, I discovered a novel short-term and associative plasticity of calcium dynamics (STAPCD) that exhibited temporal characteristics mirroring the induction rules of BTSP. I identified a core set of molecular components crucial for both STAPCD and BTSP and developed a computational simulation that models the calcium dynamics as a latent memory trace of neural activity (i.e., eligibility traces). Together, we find that calcium handling by the endoplasmic reticulum enables synaptic weight updates upon receipt of delayed instructive signals, obeys rules of burst-dependent one-shot learning, and thus provides a mechanism that satisfies the requirements anticipated of eligibility traces. Collectively, these findings offer a neural mechanism for the binding of cellular events occurring in single shot and separated by behaviorally relevant temporal delays to induce potentiation at synapses, providing a cellular model of associative learning.