Dana Mastrovito

The paper presents a database of spiking activity in the mouse visual system during an image change detection task, revealing task-specific coding patterns across brain regions.

This study examines if communication subspaces develop with training in ANNs and finds increased alignment between activations and weights in trained models.

The paper shows that chaotic dynamics in recurrent neural networks separate learning regimes and correlate with a consciousness measure, suggesting a link between critical dynamics, learning, and consciousness.

The paper develops a neuronal model with complex dynamics and shows it can learn temporal tasks using gradient descent, exhibiting diversity and robustness.

The paper analyzes temporal dynamics of default mode network activity using Human Connectome Project data, showing that the network's activity changes over time during tasks, challenging the traditional view of it being task-negative.

This paper fills gaps in cell-class specific connectivity data using a statistical model and kernel learning method, producing a mesoscale mouse brain connectivity matrix.

This paper introduces a biologically plausible method for continual learning in ANNs that prevents catastrophic forgetting and promotes synaptic multi-modality.