Alfredo Kirkwood, Johns Hopkins University, Baltimore
Rewarding cortical synapses
Maximizing reward and avoiding punishment is an important behavioral drive, and animals routinely learn what stimuli and actions predict favorable and aversive outcomes. In reward-based learning, synaptic modifications depend on a brief stimulus and a temporally delayed reward, which poses the question of how synaptic activity patterns associate with a delayed reward. A theoretical solution to this so-called distal reward problem has been the notion of ‘‘synaptic eligibility traces,’’ silent and transient synaptic tags that can be converted into lasting changes in synaptic strength by reward-linked neuromodulators. I will discuss experimental evidence for the Hebbian induction of distinct traces for LTP and LTD, and their subsequent transformation into lasting synaptic changes by specific monoaminergic receptors. The temporal properties of these transient traces allow stable learning in a recurrent neural network that accurately predicts the timing of the reward, further validating the induction and transformation of eligibility traces for LTP and LTD as a plausible synaptic substrate for reward-based learning.
Colloquium of the GRK "Sensory Computation in Neural Systems"
Raphael Holca-Lamare / Klaus Obermayer