Large-scale neural networks can be described in the spatial continuous limit by neural field equations. For large-scale brain networks, the connectivity is typically translationally variant and imposes a large computational burden upon simulations. To reduce this burden, we take a semiquantitative approach and study the dynamics of neural fields described by a delayed integrodifferential equation. We decompose the connectivity into spatially variant and invariant contributions, which typically comprise the short- and long-range fiber systems, respectively. The neural fields are mapped on the two-dimensional spherical surface, which is choice consistent with routine mappings of cortical surfaces. Then, we perform mathematically a mode decomposition of the neural field equation into spherical harmonic basis functions. A spatial truncation of the leading orders at low wave number is consistent with the spatially coherent pattern formation of large-scale patterns observed in simulations and empirical brain imaging data and leads to a low-dimensional representation of the dynamics of the neural fields, bearing promise for an acceleration of the numerical simulations by orders of magnitude.

The thalamus is a key element of sensory transmission in the brain, as it gates and selects sensory streams through a modulation of its internal activity. A preponderant role in these functions is played by its internal activity in the alpha range ([8–14] Hz), but the mechanism underlying this process is not completely understood. In particular, how do thalamocortical connections convey stimulus driven information selectively over the back-ground of thalamic internally generated activity? Here we investigate this issue with a spiking network model of feedforward connectivity between thalamus and primary sensory cortex reproducing the local field potential of both areas. We found that in a feedforward network, thalamic oscillations in the alpha range do not entrain cortical activity for two reasons: (i) alpha range oscillations are weaker in neurons projecting to the cortex, (ii) the gamma resonance dynamics of cortical networks hampers oscillations over the 10–20 Hz range thus weakening alpha range oscillations. This latter mechanism depends on the balance of the strength of thalamocortical connections toward excitatory and inhibitory neurons in the cortex. Our results highlight the relevance of corticothalamic feedback to sustain alpha range oscillations and pave the way toward an integrated understanding of the sensory streams traveling between the periphery and the cortex.

Masticatory dysfunction is a risk factor for Alzheimer 's disease (AD), likely due to the trigeminal influences on the Locus Coeruleus (LC), a structure implicated in neurodegenerative processes and whose activity level is reflected by pupil size. Chewing activity acutely stimulates cognitive performance by increasing LC activation during task, as reflected by an increased pupil dilatation (mydriasis). The presence of a trigeminal sensorimotor imbalance is associated to an asymmetry in LC activity (reflected by an asymmetry in pupil size) and is detrimental for performance. These observations prompt for developing testing procedure, in normal elders and in patients affected by AD, aimed to: 1) study the presence of trigeminal asymmetries and to assess the possible contribution of occlusal correction in improving the subject's condition and in preventing/delaying the AD development; 2) verify the stimulating effect of chewing on cognitive performance and task-related mydriasis as an estimate of the LC excitability for determining the subject condition and its susceptibility to undergo AD development. Procedures appropriate for achieving these goals are proposed.

We measure the I–V curve of a p–n diode on a range of currents where the well known Shockley model exhibits its shortcoming. We show that the behavior of the I–V curve, on the whole range of currents, can be captured by a modified four parameter Shockley model; the parameters of the model are obtained by a numerical procedure, consisting of an iterative rapidly converging nonlinear fitting algorithm. The fit returns sound estimates of the parameters for I–V curves, as function of temperature. The method is validated on a well known 1N4148 diode, but it can also be applied to any other devices described by single-diode models such as solar cells. We also show that the knowledge of the temperature dependence of the parameters can be used to obtain a quantitative estimate of other physical properties of the system, such as the energy gap of junction materials.

Recent research studies in brain neural networks are highlighting the involvement of glial cells, in particular astrocytes, in synaptic modulation, memory formation, and neural synchronization, a role that has often been overlooked. Thus, theoretical models have begun incorporating astrocytes to better understand their functional impact. Additionally, the structural organization of neuron-neuron, astrocyte-neuron and astrocyte-astrocyte connections plays a crucial role in network dynamics. Starting from a recently published astrocyte-neuron network model with neuron-neuron random connectivity, we provide an extensive evaluation of this same model, focusing on astrocytic dynamics, neuron-astrocyte connectivity, and spatial distribution of inhibitory neurons. We propose refinements to the model with the aim of improving the biological plausibility of the above described characteristics of the model. To assess the interplay between astrocytes and network topology, we compare four configurations: neural networks with and without astrocytes, each under random and hub-driven connectivity. Simulations are conducted using the Brian2 simulator, providing insights into how astrocytes and structural heterogeneity jointly influence neural dynamics. Our findings contribute to a deeper understanding of neuron-glia interactions and the impact of network topology on astrocyte-neuron network dynamics. In particular, while finding an expected decrease of neural firing activity due to astrocyte calcium dynamics, we also found that hub-driven topology trigger a much higher firing rate with respect to the random topology, even having this last one a much higher number of neuron-neuron connections.