Rosa Cossart

CICADA provides a unified framework for analyzing neurophysiological data in the NWB format, supporting both GUI-driven and programmatic workflows with modular analysis plugins.

The paper investigates the developmental emergence of internal and external hippocampal representations in mice, finding that internal sequences appear at postnatal day 24, following cue-based representations.

The paper investigates the role of GABAergic neurons in network inhibition during neonatal hippocampal development in vivo, finding that GABA's action is inhibitory, not excitatory, even in early postnatal stages.

The paper introduces Track2p, a cell-tracking method for longitudinal analysis of neuronal activity in developing mouse brains, revealing a developmental transition in activity statistics.

The paper shows that CA1 interneurons form a network promoting synchrony through disinhibition during awake immobility, while preserving endogenous cell assemblies.

The paper examines the relationship between cortical assembly activity and EEG microstate dynamics during different vigilance states, finding coordination during quiet wakefulness but not sleep.

This study investigates how extrinsic inputs control early postnatal CA1 hippocampal circuits in mice, showing differential contributions from entorhinal and thalamic pathways to synchronous activity.

The paper shows that in mouse hippocampus, CA1 pyramidal cells switch from being activated to inhibited by self-motion around postnatal day 7, linked to increased local GABAergic innervation.

The paper maps presynaptic inputs to PV and SST cortical interneurons across cortical areas, revealing region-specific developmental patterns and differential vulnerability to neurodevelopmental disorders.

The paper examines how the birthdate of CA1 pyramidal neurons correlates with their diversity in the ventral hippocampus, showing that early-born neurons have distinct features.

The paper identifies early-born GABA neurons in the hippocampus as lifelong hubs with unique functional properties.

DeepCINAC is a deep learning toolbox for inferring neuronal activity from calcium imaging data, achieving human-level performance and better F1 scores than existing methods.

The paper shows that hippocampal network activity represents a combination of space and time with tunable degrees across days, using calcium imaging and network modeling in mice.

The paper develops a spiking network model to identify two types of driver cells in developing neuronal networks and explains their roles in synchronization dynamics.