3. We present TeDFA-δ, a bio. plaus. deep spiking RL model that leverages temporal integration and weak learning rules to outperform standard MLPs+BP for policy learning, highlighting the importance of neural dynamics over credit assignment for effective control:

2025.ccneuro.org/poster/?id=S...

2. We developed a bio. plaus. computational model of the dentate gyrus that shows how both impaired synaptic plasticity and increased neurogenesis—modulated by Cbln4-Neo1 complex—disrupt behavioral pattern separation:

2025.ccneuro.org/poster/?id=P...

1. We developed a RNN-based meta-RL framework that models schizophrenia-like decision-making deficits. We see a positive correlation between the number of dynamical attractor states and suboptimal behavior:

2025.ccneuro.org/poster/?id=4...

Interestingly, we found no significant difference in under and over-updating behavior in Schizophrenia patient data (Nassar et al. 2021). Instead, analyzing the behavior using the delta area metric showed a significant difference, suggesting the utility of model-guided human-behavior data analysis.

We used a fixed point finder algorithm and found that suboptimal agents (lower delta area value) exhibited smaller number of unstable fixed points compared to more optimal agents. The number of stable fixed points remained consistent across the delta area metric.

Besides the (1) reward discount factor, we explored (2) prediction error scaling, (3) probability of disrupting RNN dynamics, (4) rollout buffer length. Each hyperparameter differently influenced the suboptimal decision making behavior, which we termed as delta area.