Biology subjects

Francioni, V.

Publications and source records attributed to Francioni, V..

3 recordsLinked to original sources

bioRxiv2025

CLOOSE: An open-source platform for optical closed-loop experiments

Brain-Computer Interfaces (BCI) have catalyzed advancements in both clinical applications and basic neuroscience research. However, technical barriers such as steep learning curves and complex synchronization requirements often impede their widespread adoption. In response to the increasing demand for optical BCI experiments and to the technical challenges associated with their implementation, we introduce CLOOSE (Closed-loop Optical Open-Source Experiments), a versatile platform designed to standardize, simplify, and accelerate closed-loop experiments with functional imaging data. CLOOSE interfaces easily with any frame-based imaging system via TCP/IP, allowing for real-time data streaming and distributed computation. Benchmark tests validate CLOOSEs real-time accuracy in image registration, signal processing, and analysis at imaging frequencies [≥]1 kHz, making it the first optical BCI compatible with voltage indicators. Throughout the paper we showcase CLOOSEs versatility in supporting several neurofeedback paradigms: from single neuron to population dynamics, multiplane imaging, and online (and offline) z-tracking. CLOOSEs functionality is easily accessible to users with minimal coding experience through an intuitive graphical interface for experiment setup and real-time performance monitoring. By significantly lowering the barrier to performing technically demanding closed-loop experiments, CLOOSE advances the streamlining, standardization, and democratization of online image analysis and neurofeedback BCI paradigms, opening new avenues for precise manipulation and real-time readout of neuronal activity in health and disease.

neuroscience↗

bioRxiv2023

Vectorized instructive signals in cortical dendrites during a brain-computerinterface task

Vectorization of teaching signals is a key element of virtually all modern machine learning algorithms, including backpropagation, target propagation and reinforcement learning. Vectorization allows a scalable and computationally efficient solution to the credit assignment problem by tailoring instructive signals to individual neurons. Recent theoretical models have suggested that neural circuits could implement single-phase vectorized learning at the cellular level by processing feedforward and feedback information streams in separate dendritic compartments1-5. This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. We leveraged a neurofeedback brain-computer interface (BCI) task with an experimenter-defined reward function to test for vectorized instructive signals in dendrites. We trained mice to modulate the activity of two spatially intermingled populations (4 or 5 neurons each) of layer 5 pyramidal neurons in the retrosplenial cortex to rotate a visual grating towards a target orientation while we recorded GCaMP activity from somas and corresponding distal apical dendrites. We observed that the relative magnitudes of somatic versus dendritic signals could be predicted using the activity of the surrounding network and contained information about task-related variables that could serve as instructive signals, including reward and error. The signs of these putative teaching signals both depended on the causal role of individual neurons in the task and predicted changes in overall activity over the course of learning. Furthermore, targeted optogenetic perturbation of these signals disrupted learning. These results provide the first biological evidence of a vectorized instructive signal in the brain, implemented via semi-independent computation in cortical dendrites, unveiling a potential mechanism for solving credit assignment in the brain.

neuroscience↗

bioRxiv2019

High somato-dendritic coupling of V1 layer 5 neurons independent of behavioural state and visual stimulation

Active dendrites impact sensory processing and behaviour. However, it remains unclear how active dendritic integration relates to somatic output in vivo. We imaged semi-simultaneously GCaMP6s signals in the soma, trunk and distal tuft dendrites of layer 5 pyramidal neurons in the awake mouse primary visual cortex. We found that apical tuft signals were dominated by widespread, highly correlated calcium transients throughout the tuft. While these signals were highly coupled to trunk and somatic transients, the frequency of calcium transients was found to decrease in a distance-dependent manner from soma to tuft. Ex vivo recordings suggest that low-frequency back-propagating action potentials underlie the distance-dependent loss of signals, while coupled somato-dendritic signals can be triggered by high-frequency somatic bursts or strong apical tuft depolarization. Visual stimulation and locomotion increased neuronal activity without affecting somato-dendritic coupling. High, asymmetric somato-dendritic coupling is therefore a widespread feature of layer 5 neurons activity in vivo.

neuroscience↗