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Panniello, M.

Publications and source records attributed to Panniello, M..

3 recordsLinked to original sources

Stimulus information guides the emergence of behavior related signals in primary somatosensory cortex during learning

Cortical neurons in primary sensory cortex carry not only sensory but also behavior-related information. However, it remains unclear how these types of information emerge and are integrated with one another over learning and what the relative contribution of activity in individual cells versus neuronal populations is in this process. Current evidence supports two opposing views of learning-related changes: 1) sensory information increases in primary cortex or 2) sensory information remains stable in primary cortex but its readout efficiency in association cortices increases. Here, we investigate these questions in primary sensory cortex during learning of a sensory task. Over the course of weeks, we imaged neuronal activity at different depths within layers 2 and 3 of the mouse vibrissal primary somatosensory cortex (vS1) before, during, and after training on a whisker-based object-localization task. We leveraged information theoretical analysis to quantify stimulus and behavior-related information in vS1 and estimate how much neural activity encoding sensory information is used to inform perceptual choices as sensory learning progresses. We also quantified the extent to which these types of information are supported by an individual neuron or population code. We found that, while sensory information rises progressively from the start of training, choice information is only present in the final stages of learning and is increasingly supported by a population code. Moreover, we demonstrate that not only the increase in available information, but also a more efficient readout of such information in primary sensory cortex mediate sensory learning. Together, our results highlight the importance of primary cortical neurons in perceptual learning.

neuroscience↗

pyControl: Open source, Python based, hardware and software for controlling behavioural neuroscience experiments.

Laboratory behavioural tasks are an essential research tool. As questions asked of behaviour and brain activity become more sophisticated, the ability to specify and run richly structured tasks becomes more important. An increasing focus on reproducibility also necessitates accurate communication of task logic to other researchers. To these ends we developed pyControl, a system of open source hardware and software for controlling behavioural experiments comprising; a simple yet flexible Python-based syntax for specifying tasks as extended state machines, hardware modules for building behavioural setups, and a graphical user interface designed for efficiently running high throughput experiments on many setups in parallel, all with extensive online documentation. These tools make it quicker, easier and cheaper to implement rich behavioural tasks at scale. As important, pyControl facilitates communication and reproducibility of behavioural experiments through a highly readable task definition syntax and self-documenting features. ResourcesDocumentation: https://pycontrol.readthedocs.io Repositories: https://github.com/pyControl User support: https://groups.google.com/g/pycontrol

neuroscience↗

Complexity of frequency receptive fields predicts tonotopic variability across species

Primary cortical areas contain maps of sensory features, including sound frequency in primary auditory cortex (A1). Two-photon calcium imaging in mice has confirmed the presence of these large-scale maps, while uncovering an unexpected local variability in the stimulus preferences of individual neurons in A1 and other primary regions. Here we show that fractured tonotopy is not unique to rodents. Using two-photon imaging, we found that local variance in frequency preferences is equivalent in ferrets and mice. Much of this heterogeneity was due to neurons with complex frequency tuning, which are less spatially organized than those tuned to a single frequency. Finally, we show that microelectrode recordings may describe a smoother tonotopic arrangement due to a bias towards neurons with simple frequency tuning. These results show that local variability in the tonotopic map is not restricted to rodents and help explain inconsistencies in cortical topography across species and recording techniques.

neuroscience↗