Speaker
Description
Experience unfolds continuously, so the activity patterns available to memory circuits are rarely independent. Classical Hebbian learning strengthens connections between neurons that are active together. In a recurrent network, this can bind correlated patterns into one expanding assembly. How can successive experiences remain distinct rather than collapse into a single memory?
Intracellular recordings in hippocampal area CA1 revealed an unexpected clue. A single dendritic plateau could create a place field in a silent neuron or relocate an existing field. The resulting behavioral timescale synaptic plasticity (BTSP) links presynaptic activity to a postsynaptic plateau across seconds rather than milliseconds and potentiates or depresses synapses depending on their existing strength. In CA3, targeted perturbations and modeling converged on a time-symmetric form of BTSP operating at recurrent synapses. Together, they showed how this rule can build memory-supporting attractor dynamics whose expression and updating remain controlled by external input.
Theoretical analysis showed that BTSP can reverse the effect of temporal correlation on memory storage. Under Hebbian learning, increasing correlation merges patterns and reduces memory capacity. With BTSP, potentiation near the plateau strengthens the selected representation, while depression in two temporal flanks weakens cross-pattern connections in both directions. This keeps successive representations distinct and allows attractor capacity to increase as correlation grows. These results raise broader questions. What does BTSP store when experience is hierarchically structured, and how do existing memories shape what is learned next? A plasticity rule measured in vivo can turn the continuity of experience from a source of interference into useful structure for continual learning.