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Transcranial alternating current stimulation (tACS) represents a promising non-invasive treatment for an increasingly wide range of neurological and neuropsychiatric disorders. The ability to use periodically oscillating electric fields to non-invasively engage neural dynamics opens up the possibility of recruiting synaptic plasticity and to modulate brain function. However, despite consistent reports about tACS clinical effectiveness, strong state-dependence combined with the ubiquitous heterogeneity of cortical networks collectively results in high outcome variability. Introducing variations in intrinsic neuronal timescales, we explored how such heterogeneity influences stimulation-induced change in synaptic connectivity. We examined how spike timing dependent plasticity, at the level of cells, intra- and inter-laminar cortical networks, can be selectively and preferentially engaged by periodic stimulation. Using leaky integrate-and-fire neuron models, we analyzed cortical circuits comprised of multiple cell-types, alongside superficial multi-layered networks expressing distinct layer-specific timescales. Our results show that mismatch in neuronal timescales within and/or between cells—and the resulting variability in excitability, temporal integration properties and frequency tuning—enables selective and directional control on synaptic connectivity by tACS. Our work provides new vistas on how to recruit neural heterogeneity to guide brain plasticity using non-invasive stimulation paradigms.

Brain networks exhibit very variable and dynamical functional connectivity and flexible configurations of information exchange despite their overall fixed structure. Brain oscillations are hypothesized to underlie time-dependent functional connectivity by periodically changing the excitability of neural populations. In this paper, we investigate the role of the connection delay and the detuning between the natural frequencies of neural populations in the transmission of signals. Based on numerical simulations and analytical arguments, we show that the amount of information transfer between two oscillating neural populations could be determined by their connection delay and the mismatch in their oscillation frequencies. Our results highlight the role of the collective phase response curve of the oscillating neural populations for the efficacy of signal transmission and the quality of the information transfer in brain networks.

The emergence of flexible information channels in brain networks is a fundamental question in neuroscience. Understanding the mechanisms of dynamic routing of information would have far-reaching implications in a number of disciplines ranging from biology and medicine to information technologies and engineering. In this work, we show that the presence of a node firing at a higher frequency in a network with local connections, leads to reliable transmission of signals and establishes a preferential direction of information flow. Thus, by raising the firing rate a low degree node can behave as a functional hub, spreading its activity patterns polysynaptically in the network. Therefore, in an otherwise homogeneous and undirected network, firing rate is a tunable parameter that introduces directionality and enhances the reliability of signal transmission. The intrinsic firing rate across neuronal populations may thus determine preferred routes for signal transmission that can be easily controlled by changing the firing rate in specific nodes. We show that the results are generic and the same mechanism works in the networks with more complex topology. •The presence of high frequency nuclei facilitates the transmission of signals in neuronal networks with local interactions.•Local signals that impact neuronal nodes oscillating at a higher frequency can be reliably transmitted through the network.•In a homogeneous network, where all nodes have almost the same frequency, signals hardly propagate.•When multiple signals impact the network, only those impinging on the high frequency nodes propagate through the whole network.•The results hold for more complex networks and in particular for anatomic networks such as CoCoMac connectome.

Periodic brain stimulation (PBS) techniques, either intracranial or non-invasive, electrical or magnetic, represent promising neuromodulatory tools for the treatment of neurological and neuropsychiatric disorders. Through the modulation of endogenous oscillations, PBS may engage synaptic plasticity, hopefully leading to persistent lasting effects. However, stabilizing such effects represents an important challenge: the interaction between induced electromagnetic fields and neural circuits may yield highly variable responses due to heterogeneous neuronal and synaptic biophysical properties, limiting PBS clinical potential. In this study, we explored the conditions on which PBS leads to amplified post-stimulation oscillatory power, persisting once stimulation has been turned off. We specifically examined the effects of heterogeneity in neuron time scales on post-stimulation dynamics in a population of balanced leaky-integrated and fire (LIF) neurons that exhibit synchronous-irregular spiking activity. Our analysis reveals that such heterogeneity enables PBS to engage synaptic plasticity, amplifying post-stimulation power. Our results show that such post-stimulation aftereffects result from selective frequency- and cell-type-specific synaptic modifications. We evaluated the relative importance of stimulation-induced plasticity amongst and between excitatory and inhibitory populations. Our results indicate that heterogeneity in neurons' time scales and synaptic plasticity are both essential for stimulation to support post-stimulation aftereffects, notably to amplify the power of endogenous rhythms.Competing Interest StatementThe authors have declared no competing interest.

Non-invasive brain stimulation techniques are widely used for manipulating the behaviour of neuronal circuits and the excitability of the neurons therein. While the usage of these techniques is widely studied at the meso- and macroscopic scales, less is known about the specificity of such approaches at the level of individual cells. Here we use models based on the morphologies of real pyramidal and parvalbumin neurons from mouse primary visual cortex created by the Allen Institute for Brain Science to explore the variability and evoked response susceptibility of different morphologies to uniform electric fields. We devised a range of metrics quantifying various aspects of cellular morphology, ranging from whole cell attributes to net compartment length, branching, diameter to orientation. In supporting layer- and cell-type specific responses, none of these physical traits passed statistical significance tests. While electric fields can modulate somatic, dendritic and axonal compartments reliably and subtype-specific responses could be observed, the specificity of such stimuli was blurred by the variability in cellular morphology. These null results suggest that morphology alone may not account for the reported subtype specificity of brain stimulation paradigms, and question the extent to which such techniques may be used to probe and control neural circuitry. Over the last several decades there has been increased interest in the efficacy of non-invasive brain stimulation, particularly in determining the limits of specificity of such techniques. Despite this growing area of research, much remains unknown about the interactions of non-invasive techniques with neurons at the single-cell level, notably the importance of morphology to these interactions. We make use of detailed single-neuron models and simulate them in a uniform electric field and demonstrate that the high variability in neuron morphologies may limit how specifically single neurons can be targeted non-invasively. We confirmed this for neuron morphology characteristics at macro- and meso- scales and at varied orientations. Our work suggests that previously reported subtype specificities in non-invasive frameworks are not accounted for by considering only morphological factors.

A comparative study of a qutrit (three-level atomic system) coupled to a classical field in a typical Markovian reservoir (free space) and in a photonic band-gap (PBG) crystal is carried out. The aim of the study is to assess the collective impact of structured environment and classical control of the system on the dynamics of quantum coherence, non-Markovianity, and estimation of parameters which are initially encoded in the atomic state. We show that the constructive interplay of PBG material as a medium and classical driving field as a part of system results in a significant enhancement of all the quantum traits of interest, compared to the case when the driven qutrit is in a Markovian environment. Our results supply insights for preserving and enhancing quantum features in qutrit systems which are promising alternative candidates to be used in quantum processors instead of qubits.

Transcranial alternating current stimulation (tACS) represents a promising non-invasive treatment for an increasingly wide range of neurological and neuropsychiatric disorders. The ability to use periodically oscillating electric fields to non-invasively engage neural dynamics opens up the possibility of recruiting synaptic plasticity and to modulate brain function. However, despite consistent reports about tACS clinical effectiveness, strong state-dependence combined with the ubiquitous heterogeneity of cortical networks collectively results in high outcome variability. Introducing variations in intrinsic neuronal timescales, we explored how such heterogeneity influences stimulation-induced change in synaptic connectivity. We examined how spike timing dependent plasticity, at the level of cells, intra- and inter-laminar cortical networks, can be selectively and preferentially engaged by periodic stimulation. Using computational simulations informed by human experimental data, we analyzed cortical circuits comprised of multiple cell-types, alongside superficial multi-layered networks expressing distinct layer-specific timescales. Our results show that mismatch in neuronal timescales within and/or between cells - and the resulting variability in excitability, temporal integration properties and frequency tuning - enables selective and directional control on synaptic connectivity by tACS. Our work provides new vistas on how to recruit neural heterogeneity to guide brain plasticity using non-invasive stimulation paradigms. Brain stimulation techniques, such as transcranial alternating current stimulation (tACS), are increasingly used to treat mental health disorders and to probe brain function. Despite promising results, it remains unclear how these non-invasive interventions impact both the dynamics and connectivity of neural circuits. We developed an interdisciplinary framework showing that heterogeneity in neuronal timescales, and its consequences on cellular excitability and temporal integration properties of cortical neurons, may lead to selective and directional control on synaptic modifications by tACS. Differences in neuron responses resulting from timescale mismatch establishes phase- and frequency-specific tuning relationships which may be recruited by periodic stimuli to guide synaptic plasticity. We confirmed this using both intra - and inter-laminar cortical circuit models comprised of multiple cell-types and informed by experimental data. Our work showcases how heterogeneity might be used to guide synaptic plasticity using non-invasive stimulation paradigms.

Brain networks exhibit very variable and dynamical functional connectivity and flexible configurations of information exchange despite their overall fixed structure (connectome). Brain oscillations are hypothesized to underlie time-dependent functional connectivity by periodically changing the excitability of neural populations. In this paper, we investigate the role that the connection delay and the frequency detuning between different neural populations play in the transmission of signals. Based on numerical simulations and analytical arguments, we show that the amount of information transfer between two oscillating neural populations can be determined solely by their connection delay and the mismatch in their oscillation frequencies. Our results highlight the role of the collective phase response curve of the oscillating neural populations for the efficacy of signal transmission and the quality of the information transfer in brain networks. Collective dynamics in brain networks is characterized by a coordinated activity of their constituent neurons that lead to brain oscillations. Many evidences highlight the role that brain oscillations play in signal transmission, the control of the effective communication between brain areas and the integration of information processed by different specialized regions. Oscillations periodically modulate the excitability of neurons and determine the response those areas receiving the signals. Based on the communication trough coherence (CTC) theory, the adjustment of the phase difference between local oscillations of connected areas can specify the timing of exchanged signals and therefore, the efficacy of the communication channels. An important factor is the delay in the transmission of signals from one region to another that affects the phase difference and timing, and consequently the impact of the signals. Despite this delay plays an essential role in CTC theory, its role has been mostly overlooked in previous studies. In this manuscript, we concentrate on the role that the connection delay and the oscillation frequency of the populations play in the signal transmission, and consequently in the effective connectivity, between two brain areas. Through extensive numerical simulations, as well as analytical results with reduced models, we show that these parameters have two essential impacts on the effective connectivity of the neural networks: First, that the populations advancing in phase to others do not necessarily play the role of the information source; and second, that the amount and direction of information transfer dependents on the oscillation frequency of the populations.

We propose and numerically study two permanent magnetic micro-structures for creating Ioffe-Pritchard microtraps. A bias magnetic field is used to vary the depth, trap frequencies and the minimum of each microtrap. After the Bose-Einstein condensation achievement, the bias magnetic field can be slowly removed to increase the trap barrier heights for more efficiently holding the Bose-Einstein condensates. Even without the external magnetic field, it is possible to hold ultracold atoms in the microtraps. These microtraps may also be useful for single atom experiments for quantum information processing due to their very high confinement.