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Barrel cortex integrates contra- and ipsilateral whiskers’ inputs. While contralateral inputs depend on the thalamocortical innervation, ipsilateral ones are thought to rely on callosal axons. These are more abundant in the barrel cortex region bordering with S2 and containing the row A-whiskers representation, the row lying nearest to the facial midline. Here we ask what role this callosal axonal arrangement plays in ipsilateral tactile signalling. We found that novel object exploration with ipsilateral whiskers confines c-Fos expression within the highly callosal subregion. Targeting this area within vivopatch-clamp recordings revealed neurons with uniquely strong ipsilateral responses dependent on the corpus callosum, as assessed by tetrodotoxin silencing and by optogenetic activation of the contralateral hemisphere. Still, in this area, stimulation of contra- or ipsilateral row A-whiskers evoked an indistinguishable response in some neurons, mostly located in layers 5/6, indicating their involvement in the midline representation of the whiskers’ sensory space.

Synchrony of neural activity among cortical areas arises from functional coupling between those areas. Such a strong synchrony characterises the two mouse barrel fields (BFs) when the animal is deeply anaesthetised or asleep. In these conditions, neurons in the two hemispheres depolarise (up-state) and hyperpolarise to their resting potential (down-state) in a remarkably coordinated fashion. Callosal glutamatergic axons provide a means to functionally couple supra- and infragranular neurons of the two BFs. However, little is known about their relationship with the BF grid-like architecture: Are they able to influence the activity of barrel and/or septal neurons? Are there specific barrels more sensitive to the contralateral activity? To respond to these questions, we localised and counted the BF cells positive to c-Fos (c-Fos +) resulting from a contralateral whiskers deprivation when mice were free to explore a novel environment. In layer 4, we found a greater number of c-Fos + cells in septa compared to barrels, which mainly localised in the posterior and lateral aspects of the sensory-deprived BF. To learn more about such interhemispheric recruitment, we studied the propagation of slow-oscillatory activity in anaesthetised mice. We performed whole-cell patch-clamp in the ipsilateral BF while recording LFPs in the contralateral BF. In the BF lateral region, neurons showed faster oscillatory cycles, shorter up-state duration and faster down-to-up transitions compared to neurons recorded in BF regions with a sparser c-Fos signal, suggesting the reception of extra inputs in the former. We thus propose that the lateral BF is a critical sub-region for BFs activity-coupling.

Although the visual cortex receives information at multiple temporal patterns, much of the research in the field has focused only on intervals shorter than 1 second. Consequently, there is almost no information on what happens at longer temporal intervals. We have tried to address this question recording neuronal populations of the primary visual cortex during visual stimulation with repetitive grating stimuli and intervals ranging from 1 to 7 seconds. Our results showed that firing rate and response stability were dependent of interval duration. In addition, there were collective oscillations with different properties in response to changes in intervals duration. These results suggest that visual cortex could encode visual information at several time scales using oscillations at multiple frequencies.

Background: Linear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to real-world stimulation are nonlinear under some conditions, and many neuronal components demonstrate strong nonlinear behavior. In spite of this, temporal and frequency dynamics of neural populations to sensory stimulation have been usually analyzed with linear approaches. New method: In this paper, we propose the use of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm, plus the Hilbert transform as a suitable tool for analyzing population oscillatory dynamics in a multi-dimensional space with instantaneous frequency (IF) resolution. Results: The proposed approach was able to extract oscillatory information of neurophysiological data of deep vibrissal nerve and visual cortex multiunit recordings that were not evidenced using linear approaches with fixed bases such as the Fourier analysis. Comparison with existing methods: Texture discrimination analysis performance was increased when Noise-Assisted Multivariate Empirical Mode plus Hilbert transform was implemented, compared to linear techniques. Cortical oscillatory population activity was analyzed with precise time-frequency resolution. Similarly, NA-MEMD provided increased time-frequency resolution of cortical oscillatory population activity. Conclusions: Noise-Assisted Multivariate Empirical Mode Decomposition plus Hilbert transform is an improved method to analyze neuronal population oscillatory dynamics overcoming linear and stationary assumptions of classical methods. Fil: Alegre Cortes, Javier. Universidad de Miguel Hernández; España Fil: Soto Sanchez, Cristina. Universidad de Miguel Hernández; España Fil: Pizá, Alvaro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina Fil: Albarracin, Ana Lia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina Fil: Farfan, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina Fil: Felice, Carmelo Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina Fil: Fernandez, Eduardo. Universidad de Miguel Hernández; España