Biology subjects

Bisi, A.

Publications and source records attributed to Bisi, A..

4 recordsLinked to original sources

bioRxiv2026

CICADA: A unified framework for NWB-based neurophysiological data analysis

Neurophysiology datasets are becoming increasingly complex, combining behavioral measurements with high-dimensional neuronal activity recordings coming from optical and/or electrophysiological measurements. The Neurodata Without Borders (NWB) standard has emerged in the community as the format of record. While standardized and widely used preprocessing tools generating NWB files have been developed, extensible frameworks for scientific analysis downstream of the NWB ecosystem are still under-represented. We present CICADA, a Python framework dedicated to analysis of neurophysiological data in the standardized NWB format. The toolbox is built as three hierarchically-organized packages: cicada-nwb (NWB access layer), cicada-analysis (plugin-based analysis engine and tool library), and cicada-gui (PyQt5 desktop application at the head of the pipeline). Beyond this architectural separation, CICADA is built around a central design principle: supporting a continuum from turnkey use to full modularity. Researchers can use the complete GUI-driven cicada-gui workflow without writing code, programmatically use existing analysis plugins from cicada-analysis, contribute to new analysis plugins, reuse utilities from cicada-tools, or build entirely custom pipelines on top of the cicada-nwb access layer alone. The same analysis plugin runs identically in interactive GUI and parameter-configured headless modes, enabling reproducible multi-session, multi-animal group analyses. We illustrate the versatility of CICADA with example analyses of behavioral, calcium imaging (two-photon and widefield) and extracellular electrophysiology datasets from rodent laboratories. CICADA is open source, actively maintained, and designed so that any laboratory can contribute at any level of the stack without modifying the core framework.

neuroscience↗

bioRxiv2026

Rapid cortical reorganization tracks goal-directed sensorimotor learning in real time

Sensorimotor associations are typically thought to require days of training to consolidate in sensory cortex, yet adaptive behavior can emerge within minutes. Here, we developed a barrel cortex-dependent whisker-based detection task in which mice learned to associate a novel tactile whisker stimulus with reward within a single behavioral session. Longitudinal two-photon calcium imaging of layer 2/3 barrel cortex neurons revealed that reward-driven learning rapidly reorganized the neuronal representation of the whisker deflection within a single session. Population decoding tracked this transition trial-by-trial during learning with neuronal trajectories mirroring behavior. Critically, neurons that gained stimulus responsiveness across training preferentially took part in spontaneous reactivation events during learning, suggesting that online reactivations could act as a potential upstream selection mechanism. Our results suggest that reward-based learning evokes rapid sensory cortical reorganization on the timescale of minutes, which could be mediated by a concurrent reactivation-based mechanism driving plasticity.

neuroscience↗

bioRxiv2025

Retrosplenial cortex enables context-dependent goal-directed sensorimotor transformation

The ability to dynamically adjust a behavioral response to a stimulus depending on context is of critical importance for animals. To investigate the neural basis supporting context-dependent sensory processing we developed a behavioral task in which mice changed their response to a single whisker deflection according to a continuously present contextual cue. Through unbiased optogenetic inactivation mapping, we found that neuronal activity in sensory and motor cortices contributed to task execution and, interestingly, we uncovered an unexpected role of retrosplenial cortex for contextual integration. Widefield calcium imaging revealed that retrosplenial cortex was the first dorsal cortical area to show context discrimination in response to whisker stimulation, followed by whisker motor cortex. Finally, we combined optogenetic inactivation with calcium imaging to define causal context-dependent changes in sensorimotor processing. Our cortex-wide mapping experiments thus begin to define key cortical nodes for context-dependent sensorimotor transformation and highlight an important contribution of retrosplenial cortex.

neuroscience↗

bioRxiv2023

Task-driven neural network models predict neural dynamics of proprioception

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network modeling approach to investigate the neural code of proprioceptive neurons in both cuneate nucleus (CN) and somatosensory cortex area 2 (S1). We simulated muscle spindle signals through musculoskeletal modeling and generated a large-scale, naturalistic movement repertoire to train thousands of neural network models on 16 behavioral tasks, each reflecting a hypothesis about the neural computations of the ascending proprioceptive pathway. We found that the networks internal representations developed through task-optimization generalize from synthetic data to predict single-trial neural activity in CN and S1 of primates performing center-out reaching. Task-driven models outperform linear encoding models and data-driven models. Behavioral tasks, which aim to predict the limb position and velocity were the best to predict the neural activity in both areas. Architectures that are better at solving the tasks are also better at predicting the neural data. Last, since task-optimization develops representations that better predict neural activity during active but not passively generated movements, we hypothesize that neural activity in CN and S1 is top-down modulated during goal-directed movements.

neuroscience↗