This week’s SAND lab meeting will feature Ching Fang (PhD student) and Emily Mackevicius (Postdoc) from Columbia. They will give back-to-back presentations on their recent preprint with Larry Abbott and Dmitriy Aronov (see title and abstract below).
Starting at 11am, Emily will give a general introduction and tutorial on the successor representation in neuroscience. Then, around 1pm Ching will give a talk diving into the details of their preprint. We will break for lunch somewhere in between.
Everyone is more than welcome to sit in and participate! If anyone wants to schedule 1:1 meetings with Ching or Emily please reach out to Alex Williams or Noah Dlugacz.
Title: Neural learning rules for generating flexible predictions and computing the successor representation
Abstract: The predictive nature of the hippocampus is thought to be useful for memory-guided cognitive behaviors. Inspired by the reinforcement learning literature, this notion has been formalized as a predictive map called the successor representation (SR). The SR captures a number of observations about hippocampal activity. However, the algorithm does not provide a neural mechanism for how such representations arise. Here, we show the dynamics of a recurrent neural network naturally calculate the SR when the synaptic weights match the transition probability matrix. Interestingly, the predictive horizon can be flexibly modulated simply by changing the network gain. We derive simple, biologically plausible learning rules to learn the SR in a recurrent network. We test our model with realistic inputs and match hippocampal data recorded during random foraging. Taken together, our results suggest that the SR is more accessible in neural circuits than previously thought and can support a broad range of cognitive functions.