Sara Matias

The neural circuit implementation of distributional reinforcement learning

This study investigates how distributional reinforcement learning is implemented in the basal ganglia by analyzing dopamine neuron activity and striatal representations.

A hardwired neural circuit for temporal difference learning

The paper demonstrates that TD learning computations are hardwired into a dopamine circuit by showing optogenetic stimulation of NAc D1 neurons drives dopamine neuron spiking according to TD error.

Interpretable deep learning for deconvolutional analysis of neural signals

This paper introduces an interpretable deep learning method for deconvolving neural signals, demonstrating its application in analyzing brain activity across different regions and recording modalities.

An opponent striatal circuit for distributional reinforcement learning

The paper identifies a striatal circuit that encodes reward distributional information, showing D1 and D2 MSNs encode different reward distribution tails.

The role of prospective contingency in the control of behavior and dopamine signals during associative learning

This study investigates how dopamine signals in the ventral striatum relate to contingency during associative learning in mice, finding that TD learning models with ITI state representations explain the results better than contingency-based models.

A gradual backward shift of dopamine responses during associative learning

This paper shows that dopamine responses shift backward in time during associative learning, aligning with TD prediction error models.

Firing patterns of serotonin neurons underlying cognitive flexibility

This study identifies firing patterns of serotonin neurons in mice during reversal learning, showing they respond to both positive and negative prediction errors and differ from dopamine neurons in learning rates, supporting a role in cognitive flexibility.