Search

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.

Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time–Frequency (T–F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T–F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T–F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain–machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered. Fil: Alegre Cortés, Javier. Universidad de Miguel Hernández; España Fil: Soto Sánchez, Cristina. Universidad de Alicante; España. Universidad de Miguel Hernández; España. Centro de Redes de Investigación Biomédica en Bioingeniería, Biomateriales y Nanomedicina; España Fil: Albarracin, Ana Lia. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; Argentina. 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 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; Argentina Fil: Val Calvo, Mikel. Universidad Politécnica de Cartagena; España Fil: Ferrandez, José M.. Universidad Politécnica de Cartagena; España Fil: Fernandez, Eduardo. Centro de Redes de Investigación Biomédica en Bioingeniería, Biomateriales y Nanomedicina; España. Universidad de Miguel Hernández; España

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.

Understanding the underlying properties of neuronal populations over single neurons is a longstanding goal for both basic and applied neurosciences, with a specifically suitable application in the field of neuroprosthesis development, aimed to restore the loss of function of a visual cortex as a result of an injury or disease. We study how the interstimulus interval (ISI) period of a repeated visual stimulus influences the overall activity of rat visual cortex neuronal populations. Our results suggest that certain (3, 5 s) interstimulus intervals do have an increased stimulus response compared to longer or shorter ISIs for a 500 ms grating drifting stimulus. Based on the preliminary results shown in this article, we claim the need of a better understanding of the biological dynamics of the visual cortex neuronal populations in order to properly design suitable brain-machine interfaces for visual neurorehabilitation intracortical neuroprosthetics.