Publications by authors named "A Buccino"

Perineuronal nets (PNNs) are a condensed form of extracellular matrix primarily found around parvalbumin-expressing (PV+) interneurons. The postnatal maturation of PV+ neurons is accompanied with the formation of PNNs and reduced plasticity. Alterations in PNN and PV+ neuron function have been described for mental disorders such as schizophrenia and autism.

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Intracellular electrophysiology, a vital and versatile technique in cellular neuroscience, is typically conducted using the patch-clamp method. Despite its effectiveness, this method poses challenges due to its complexity and low throughput. The pursuit of multi-channel parallel neural intracellular recording has been a long-standing goal, yet achieving reliable and consistent scaling has been elusive because of several technological barriers.

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In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model.

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A growing consensus that the brain is a mechanosensitive organ is driving the need for tools that mechanically stimulate and simultaneously record the electrophysiological response of neurons within neuronal networks. Here we introduce a synchronized combination of atomic force microscopy, high-density microelectrode array and fluorescence microscopy to monitor neuronal networks and to mechanically characterize and stimulate individual neurons at piconewton force sensitivity and nanometre precision while monitoring their electrophysiological activity at subcellular spatial and millisecond temporal resolution. No correlation is found between mechanical stiffness and electrophysiological activity of neuronal compartments.

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Scientific progress depends on reliable and reproducible results. Progress can also be accelerated when data are shared and re-analyzed to address new questions. Current approaches to storing and analyzing neural data typically involve bespoke formats and software that make replication, as well as the subsequent reuse of data, difficult if not impossible.

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