Dynamics Of Neuronal Networks Research Articles

Cranial Window for Acute and Chronic Optical Access to Record Neuronal Network Dynamics in the Olfactory Bulb.

Cranial window implant is the preparation of choice for acute and chronic optical access to a given brain area. The cranial window provides a stable preparation, which can last for months. This window allows to follow the activity of distinct population of neurons expressing genetically encoded fluorescent reporters of activity in awake, behaving, head-fixed animals. The optical access can also be exploited for acute imaging, in anesthetized animals. Here we provide a detailed protocol for acute and chronic cranial window implantations in the olfactory bulb. We also provide the procedure to perform injections of adeno-associated viruses expressing genetically encoded fluorescent sensors in the same area.

The implication of a diversity of non-neuronal cells in disorders affecting brain networks.

In the central nervous system (CNS) neurons are classically considered the functional unit of the brain. Analysis of the physical connections and co-activation of neurons, referred to as structural and functional connectivity, respectively, is a metric used to understand their interplay at a higher level. A myriad of glial cell types throughout the brain composed of microglia, astrocytes and oligodendrocytes are key players in the maintenance and regulation of neuronal network dynamics. Microglia are the central immune cells of the CNS, able to affect neuronal populations in number and connectivity, allowing for maturation and plasticity of the CNS. Microglia and astrocytes are part of the neurovascular unit, and together they are essential to protect and supply nutrients to the CNS. Oligodendrocytes are known for their canonical role in axonal myelination, but also contribute, with microglia and astrocytes, to CNS energy metabolism. Glial cells can achieve this variety of roles because of their heterogeneous populations comprised of different states. The neuroglial relationship can be compromised in various manners in case of pathologies affecting development and plasticity of the CNS, but also consciousness and mood. This review covers structural and functional connectivity alterations in schizophrenia, major depressive disorder, and disorder of consciousness, as well as their correlation with vascular connectivity. These networks are further explored at the cellular scale by integrating the role of glial cell diversity across the CNS to explain how these networks are affected in pathology.

Long-term morphological and functional dynamics of human stem cell-derived neuronal networks on high-density micro-electrode arrays.

Comprehensive electrophysiological characterizations of human induced pluripotent stem cell (hiPSC)-derived neuronal networks are essential to determine to what extent these in vitro models recapitulate the functional features of in vivo neuronal circuits. High-density micro-electrode arrays (HD-MEAs) offer non-invasive recording with the best spatial and temporal resolution possible to date. For 3 months, we tracked the morphology and activity features of developing networks derived from a transgenic hiPSC line in which neurogenesis is inducible by neurogenic transcription factor overexpression. Our morphological data revealed large-scale structural changes from homogeneously distributed neurons in the first month to the formation of neuronal clusters over time. This led to a constant shift in position of neuronal cells and clusters on HD-MEAs and corresponding changes in spatial distribution of the network activity maps. Network activity appeared as scarce action potentials (APs), evolved as local bursts with longer duration and changed to network-wide synchronized bursts with higher frequencies but shorter duration over time, resembling the emerging burst features found in the developing human brain. Instantaneous firing rate data indicated that the fraction of fast spiking neurons (150–600 Hz) increases sharply after 63 days post induction (dpi). Inhibition of glutamatergic synapses erased burst features from network activity profiles and confirmed the presence of mature excitatory neurotransmission. The application of GABAergic receptor antagonists profoundly changed the bursting profile of the network at 120 dpi. This indicated a GABAergic switch from excitatory to inhibitory neurotransmission during circuit development and maturation. Our results suggested that an emerging GABAergic system at older culture ages is involved in regulating spontaneous network bursts. In conclusion, our data showed that long-term and continuous microscopy and electrophysiology readouts are crucial for a meaningful characterization of morphological and functional maturation in stem cell-derived human networks. Most importantly, assessing the level and duration of functional maturation is key to subject these human neuronal circuits on HD-MEAs for basic and biomedical applications.

Astrocytes Learn to Detect and Signal Deviations From Critical Brain Dynamics.

Astrocytes are nonneuronal brain cells that were recently shown to actively communicate with neurons and are implicated in memory, learning, and regulation of cognitive states. Interestingly, these information processing functions are also closely linked to the brain's ability to self-organize at a critical phase transition. Investigating the mechanistic link between astrocytes and critical brain dynamics remains beyond the reach of cellular experiments, but it becomes increasingly approachable through computational studies. We developed a biologically plausible computational model of astrocytes to analyze how astrocyte calcium waves can respond to changes in underlying network dynamics. Our results suggest that astrocytes detect synaptic activity and signal directional changes in neuronal network dynamics using the frequency of their calcium waves. We show that this function may be facilitated by receptor scaling plasticity by enabling astrocytes to learn the approximate information content of input synaptic activity. This resulted in a computationally simple, information-theoretic model, which we demonstrate replicating the signaling functionality of the biophysical astrocyte model with receptor scaling. Our findings provide several experimentally testable hypotheses that offer insight into the regulatory role of astrocytes in brain information processing.

Analysis of dynamical robustness of multilayer neuronal networks with inter-layer ephaptic coupling at different scales

In nervous system, a non-synaptic communication between neurons, known as the ephaptic coupling, emerges depending on electromagnetic induction which is induced by extracellular electric fields. In this paper, a multilayer neuronal network is constructed by adopting ephaptic coupling between layers, and dynamical robustness of multilayer neuronal networks with inter-layer ephaptic coupling is analyzed at different scales. The macroscopic oscillation of the whole network occupies the middle ground between mesoscopic oscillation of intermediate layer and that of the top (or bottom) layer. Strong inter-layer ephaptic coupling is capable of suppressing the resonance like phenomena of oscillation at mesoscale and macroscale. At weak electrical coupling, dynamical robustness of intermediate layer is stronger than that of top and bottom layers, and is even stronger than that of the whole network. While at strong electrical coupling, dynamical robustness of each layer is comparable to that of the whole network. The inter-layer ephaptic coupling could enhance dynamical robustness of each layer and the entire multilayer neuronal network, which is in stark contrast to electrical coupling with the tendency to spoil the dynamical robustness. Dynamical robustness of the whole network is always weaker than that of intermediate layer, but is stronger than that of top and bottom layers with increasing inter-layer ephaptic coupling. The firing modes of neurons before the critical ratio is analyzed at microscale. The ratio of inactive neurons switches the firing patterns of the active neuron among different firing patterns in multilayer neuronal network. The dynamics of multilayer neuronal network with inter-layer ephaptic coupling is verified in the analog circuit built on Multisim. This study provides new clues to understand mechanism of collective phenomenon in realistic neuronal systems.

Reconstruction of sparse recurrent connectivity and inputs from the nonlinear dynamics of neuronal networks.

Reconstructing the recurrent structural connectivity of neuronal networks is a challenge crucial to address in characterizing neuronal computations. While directly measuring the detailed connectivity structure is generally prohibitive for large networks, we develop a novel framework for reverse-engineering large-scale recurrent network connectivity matrices from neuronal dynamics by utilizing the widespread sparsity of neuronal connections. We derive a linear input-output mapping that underlies the irregular dynamics of a model network composed of both excitatory and inhibitory integrate-and-fire neurons with pulse coupling, thereby relating network inputs to evoked neuronal activity. Using this embedded mapping and experimentally feasible measurements of the firing rate as well as voltage dynamics in response to a relatively small ensemble of random input stimuli, we efficiently reconstruct the recurrent network connectivity via compressive sensing techniques. Through analogous analysis, we then recover high dimensional natural stimuli from evoked neuronal network dynamics over a short time horizon. This work provides a generalizable methodology for rapidly recovering sparse neuronal network data and underlines the natural role of sparsity in facilitating the efficient encoding of network data in neuronal dynamics.

Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition.

The varied cognitive abilities and rich adaptive behaviors enabled by the animal nervous system are often described in terms of information processing. This framing raises the issue of how biological neural circuits actually process information, and some of the most fundamental outstanding questions in neuroscience center on understanding the mechanisms of neural information processing. Classical information theory has long been understood to be a natural framework within which information processing can be understood, and recent advances in the field of multivariate information theory offer new insights into the structure of computation in complex systems. In this review, we provide an introduction to the conceptual and practical issues associated with using multivariate information theory to analyze information processing in neural circuits, as well as discussing recent empirical work in this vein. Specifically, we provide an accessible introduction to the partial information decomposition (PID) framework. PID reveals redundant, unique, and synergistic modes by which neurons integrate information from multiple sources. We focus particularly on the synergistic mode, which quantifies the “higher-order” information carried in the patterns of multiple inputs and is not reducible to input from any single source. Recent work in a variety of model systems has revealed that synergistic dynamics are ubiquitous in neural circuitry and show reliable structure–function relationships, emerging disproportionately in neuronal rich clubs, downstream of recurrent connectivity, and in the convergence of correlated activity. We draw on the existing literature on higher-order information dynamics in neuronal networks to illustrate the insights that have been gained by taking an information decomposition perspective on neural activity. Finally, we briefly discuss future promising directions for information decomposition approaches to neuroscience, such as work on behaving animals, multi-target generalizations of PID, and time-resolved local analyses.

Understanding Primary Blast Injury: High Frequency Pressure Acutely Disrupts Neuronal Network Dynamics in Cerebral Organoids.

Blast exposure represents a common occupational risk capable of generating mild to severe traumatic brain injuries (TBI). During blast exposure, a pressure shockwave passes through the skull and exposes brain tissue to complex pressure waveforms. The primary neurophysiological response to blast-induced pressure waveforms remains poorly understood. Here, we use a computer-controlled table-top pressure chamber to expose human stem cell-derived cerebral organoids to varied frequency of pressure waves and characterize the neurophysiological response. Pressure waves that reach a maximum amplitude of 250 kPa were used to model a less severe TBI and 350 kPa for a more severe blast TBI event. With each amplitude, a frequency range of 500 Hz, 3000 Hz, and 5000 Hz was tested. Following the 250 kPa overpressure a multi-electrode array recorded organoid neural activity. We observed an acute suppression neuronal activity in single unit events, population events, and network oscillations that recovered within 24 h. Additionally, we observed a network desynchronization after exposure higher frequency waveforms. Conversely, organoids exposed to higher amplitude pressure (350k Pa) displayed drastic neurophysiological differences that failed to recover within 24 h. Further, lower amplitude "blast" (250 kPa) did not induce cellular damage whereas the higher amplitude "blast" (350 kPa) generated greater apoptosis throughout each organoid. Our data indicate that specific features of pressure waves found intracranially during blast TBI have varied effects on neurophysiological activity that can occur even without cellular damage.

Model design for networks of heterogeneous Hodgkin–Huxley neurons

We present a novel modular, scalable and adaptable modelling framework to accurately model neuronal networks composed of neurons with different dynamic properties and distinct firing patterns based on a control-inspired feedback structure. We consider three important classes of neurons: inhibitory Fast spiking neurons, excitatory regular spiking with adaptations neurons, and excitatory intrinsic bursting neurons. We also take into consideration two basic means of neuronal interconnection: electrical and chemical synapses. By separating the neuronal dynamics from the network dynamics, we have developed a fully flexible feedback structure that can be further augmented to incorporate additional types of neurons and/or synapses. We use an augmented version of the Hodgkin–Huxley model to describe the individual neuron dynamics and graph theory to define the network structure. We provide simulation results for small fundamental neuron motifs as well as bigger neuronal networks and we verify the accuracy, flexibility and scalability of the proposed method. Therefore, we provide the basis for a comprehensive modelling framework that is able to imitate the dynamics of individual neurons and neuronal networks and is able to replicate basic normal brain function. The structure of the proposed framework is ideal for applications of control and optimization methods both for modelling the effect of pharmacological substances as well as for modelling diseased neuron and network conditions.

Functional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making.

The notion that a neuron transmits the same set of neurotransmitters at all of its post-synaptic connections, typically known as Dale's law, is well supported throughout the majority of the brain and is assumed in almost all theoretical studies investigating the mechanisms for computation in neuronal networks. Dale's law has numerous functional implications in fundamental sensory processing and decision-making tasks, and it plays a key role in the current understanding of the structure-function relationship in the brain. However, since exceptions to Dale's law have been discovered for certain neurons and because other biological systems with complex network structure incorporate individual units that send both positive and negative feedback signals, we investigate the functional implications of network model dynamics that violate Dale's law by allowing each neuron to send out both excitatory and inhibitory signals to its neighbors. We show how balanced network dynamics, in which large excitatory and inhibitory inputs are dynamically adjusted such that input fluctuations produce irregular firing events, are theoretically preserved for a single population of neurons violating Dale's law. We further leverage this single-population network model in the context of two competing pools of neurons to demonstrate that effective decision-making dynamics are also produced, agreeing with experimental observations from honeybee dynamics in selecting a food source and artificial neural networks trained in optimal selection. Through direct comparison with the classical two-population balanced neuronal network, we argue that the one-population network demonstrates more robust balanced activity for systems with less computational units, such as honeybee colonies, whereas the two-population network exhibits a more rapid response to temporal variations in network inputs, as required by the brain. We expect this study will shed light on the role of neurons violating Dale's law found in experiment as well as shared design principles across biological systems that perform complex computations.

Self-organization collective dynamics of heterogeneous neurons with memristive and plastic chemical synapses

Hybrid synapses widely exist in the brain neural system, but how memristive and plastic chemical synapses cooperatively modulate the collective dynamics of neurons remains largely unknown. Here, we constructed self-organized networks with two heterogeneous FitzHugh–Nagumo (FHN) neurons coupled with memristive and chemical synapses, wherein the chemical synapse is modulated by the spike-timing-dependent plasticity (STDP) rule. Additionally, three kinds of network models involving excitatory–excitatory ([Formula: see text]–[Formula: see text] neurons, high excitatory–inhibitory (high [Formula: see text]–[Formula: see text]) neurons and low excitatory–inhibitory (low [Formula: see text]–[Formula: see text]) neurons were constructed. The modulation of memristive synapses on the structure and dynamics of self-organized neuronal networks is greatly dependent on model selection. Stronger coupling of memristive synapses induces consistently more stable network structure and enhanced network synchronization in the [Formula: see text]–[Formula: see text] and high [Formula: see text]–[Formula: see text] models but has complex effects on the low [Formula: see text]–[Formula: see text] neuronal network. In contrast, increasing the closing rate of memristive synapses has little effect on the [Formula: see text]–[Formula: see text] and high [Formula: see text]–[Formula: see text] networks but can accelerate the self-organization process and result in more complex firing patterns and weaker synchronization in the low [Formula: see text]–[Formula: see text] network. These results provide further understanding of the mechanism of the self-organized neuronal network dynamics underlying hybrid synapses and neuronal excitation.

Dynamics of a two-layer neuronal network with asymmetry in coupling.

Investigating the effect of changes in neuronal connectivity on the brain's behavior is of interest in neuroscience studies. Complex network theory is one of the most capable tools to study the effects of these changes on collective brain behavior. By using complex networks, the neural structure, function, and dynamics can be analyzed. In this context, various frameworks can be used to mimic neural networks, among which multi-layer networks are a proper one. Compared to single-layer models, multi-layer networks can provide a more realistic model of the brain due to their high complexity and dimensionality. This paper examines the effect of changes in asymmetry coupling on the behaviors of a multi-layer neuronal network. To this aim, a two-layer network is considered as a minimum model of left and right cerebral hemispheres communicated with the corpus callosum. The chaotic model of Hindmarsh-Rose is taken as the dynamics of the nodes. Only two neurons of each layer connect two layers of the network. In this model, it is assumed that the layers have different coupling strengths, so the effect of each coupling change on network behavior can be analyzed. As a result, the projection of the nodes is plotted for several coupling strengths to investigate how the asymmetry coupling influences the network behaviors. It is observed that although no coexisting attractor is present in the Hindmarsh-Rose model, an asymmetry in couplings causes the emergence of different attractors. The bifurcation diagrams of one node of each layer are presented to show the variation of the dynamics due to coupling changes. For further analysis, the network synchronization is investigated by computing intra-layer and inter-layer errors. Calculating these errors shows that the network can be synchronized only for large enough symmetric coupling.

Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight Resolution.

The representation of the natural-density, heterogeneous connectivity of neuronal network models at relevant spatial scales remains a challenge for Computational Neuroscience and Neuromorphic Computing. In particular, the memory demands imposed by the vast number of synapses in brain-scale network simulations constitute a major obstacle. Limiting the number resolution of synaptic weights appears to be a natural strategy to reduce memory and compute load. In this study, we investigate the effects of a limited synaptic-weight resolution on the dynamics of recurrent spiking neuronal networks resembling local cortical circuits and develop strategies for minimizing deviations from the dynamics of networks with high-resolution synaptic weights. We mimic the effect of a limited synaptic weight resolution by replacing normally distributed synaptic weights with weights drawn from a discrete distribution, and compare the resulting statistics characterizing firing rates, spike-train irregularity, and correlation coefficients with the reference solution. We show that a naive discretization of synaptic weights generally leads to a distortion of the spike-train statistics. If the weights are discretized such that the mean and the variance of the total synaptic input currents are preserved, the firing statistics remain unaffected for the types of networks considered in this study. For networks with sufficiently heterogeneous in-degrees, the firing statistics can be preserved even if all synaptic weights are replaced by the mean of the weight distribution. We conclude that even for simple networks with non-plastic neurons and synapses, a discretization of synaptic weights can lead to substantial deviations in the firing statistics unless the discretization is performed with care and guided by a rigorous validation process. For the network model used in this study, the synaptic weights can be replaced by low-resolution weights without affecting its macroscopic dynamical characteristics, thereby saving substantial amounts of memory.

STXBP1 Syndrome Is Characterized by Inhibition-Dominated Dynamics of Resting-State EEG.

STXBP1 syndrome is a rare neurodevelopmental disorder caused by heterozygous variants in the STXBP1 gene and is characterized by psychomotor delay, early-onset developmental delay, and epileptic encephalopathy. Pathogenic STXBP1 variants are thought to alter excitation-inhibition (E/I) balance at the synaptic level, which could impact neuronal network dynamics; however, this has not been investigated yet. Here, we present the first EEG study of patients with STXBP1 syndrome to quantify the impact of the synaptic E/I dysregulation on ongoing brain activity. We used high-frequency-resolution analyses of classical and recently developed methods known to be sensitive to E/I balance. EEG was recorded during eyes-open rest in children with STXBP1 syndrome (n = 14) and age-matched typically developing children (n = 50). Brain-wide abnormalities were observed in each of the four resting-state measures assessed here: (i) slowing of activity and increased low-frequency power in the range 1.75–4.63 Hz, (ii) increased long-range temporal correlations in the 11–18 Hz range, (iii) a decrease of our recently introduced measure of functional E/I ratio in a similar frequency range (12–24 Hz), and (iv) a larger exponent of the 1/f-like aperiodic component of the power spectrum. Overall, these findings indicate that large-scale brain activity in STXBP1 syndrome exhibits inhibition-dominated dynamics, which may be compensatory to counteract local circuitry imbalances expected to shift E/I balance toward excitation, as observed in preclinical models. We argue that quantitative EEG investigations in STXBP1 and other neurodevelopmental disorders are a crucial step to understand large-scale functional consequences of synaptic E/I perturbations.

Shed CNTNAP2 ectodomain is detectable in CSF and regulates Ca2+ homeostasis and network synchrony via PMCA2/ATP2B2

Although many neuronal membrane proteins undergo proteolytic cleavage, little is known about the biological significance of neuronal ectodomain shedding (ES). Here, we show that the neuronal sheddome is detectable in human cerebrospinal fluid (hCSF) and is enriched in neurodevelopmental disorder (NDD) risk factors. Among shed synaptic proteins is the ectodomain of CNTNAP2 (CNTNAP2-ecto), a prominent NDD risk factor. CNTNAP2 undergoes activity-dependent ES via MMP9 (matrix metalloprotease 9), and CNTNAP2-ecto levels are reduced in the hCSF of individuals with autism spectrum disorder. Using mass spectrometry, we identified the plasma membrane Ca2+ ATPase (PMCA) extrusion pumps as novel CNTNAP2-ecto binding partners. CNTNAP2-ecto enhances the activity of PMCA2 and regulates neuronal network dynamics in a PMCA2-dependent manner. Our data underscore the promise of sheddome analysis in discovering neurobiological mechanisms, provide insight into the biology of ES and its relationship with the CSF, and reveal a mechanism of regulation of Ca2+ homeostasis and neuronal network synchrony by a shed ectodomain.

Emergence and fragmentation of the alpha-band driven by neuronal network dynamics.

Rhythmic neuronal network activity underlies brain oscillations. To investigate how connected neuronal networks contribute to the emergence of the α-band and to the regulation of Up and Down states, we study a model based on synaptic short-term depression-facilitation with afterhyperpolarization (AHP). We found that the α-band is generated by the network behavior near the attractor of the Up-state. Coupling inhibitory and excitatory networks by reciprocal connections leads to the emergence of a stable α-band during the Up states, as reflected in the spectrogram. To better characterize the emergence and stability of thalamocortical oscillations containing α and δ rhythms during anesthesia, we model the interaction of two excitatory networks with one inhibitory network, showing that this minimal topology underlies the generation of a persistent α-band in the neuronal voltage characterized by dominant Up over Down states. Finally, we show that the emergence of the α-band appears when external inputs are suppressed, while fragmentation occurs at small synaptic noise or with increasing inhibitory inputs. To conclude, α-oscillations could result from the synaptic dynamics of interacting excitatory neuronal networks with and without AHP, a principle that could apply to other rhythms.

Aberrant Patterns of Sensory-Evoked Activity in the Olfactory Bulb of LRRK2 Knockout Mice.

The LRRK2 gene is the major genetic determinant of familiar Parkinson’s disease (PD). Leucine-rich repeat kinase 2 (LRRK2) is a multidomain protein involved in several intracellular signaling pathways. A wealth of evidence indicates that LRRK2 is enriched at the presynaptic compartment where it regulates vesicle trafficking and neurotransmitter release. However, whether the role of LRRK2 affects neuronal networks dynamic at systems level remains unknown. Addressing this question is critical to unravel the impact of LRRK2 on brain function. Here, combining behavioral tests, electrophysiological recordings, and functional imaging, we investigated neuronal network dynamics, in vivo, in the olfactory bulb of mice carrying a null mutation in LRRK2 gene (LRRK2 knockout, LRRK2 KO, mice). We found that LRRK2 KO mice exhibit olfactory behavioral deficits. At the circuit level, the lack of LRRK2 expression results in altered gamma rhythms and odorant-evoked activity with significant impairments, while the spontaneous activity exhibited limited alterations. Overall, our data in the olfactory bulb suggest that the multifaced role of LRRK2 has a strong impact at system level when the network is engaged in active sensory processing.

Marine and Anthropogenic Bromopyrroles Alter Cellular Ca2+ Dynamics of Murine Cortical Neuronal Networks by Targeting the Ryanodine Receptor and Sarco/Endoplasmic Reticulum Ca2+-ATPase.

Bromopyrroles (BrPyr) are synthesized naturally by marine sponge symbionts and produced anthropogenically as byproducts of wastewater treatment. BrPyr interact with ryanodine receptors (RYRs) and sarco/endoplasmic reticulum (SR/ER) Ca2+-ATPase (SERCA). Influences of BrPyr on the neuronal network activity remain uncharted. BrPyr analogues with differing spectra of RYR/SERCA activities were tested using RYR-null or RYR1-expressing HEK293 and murine cortical neuronal/glial cocultures (NGCs) loaded with Fluo-4 to elucidate their mechanisms altering Ca2+ dynamics. The NGC electrical spike activity (ESA) was measured from NGCs plated on multielectrode arrays. Nanomolar tetrabromopyrrole (TBP, 1) potentiated caffeine-triggered Ca2+ release independent of extracellular [Ca2+] in RYR1-HEK293, whereas higher concentrations produce slow and sustained rise in cytoplasmic [Ca2+] independent of RYR1 expression. TBP, 2,3,5-tribromopyrrole (2), pyrrole (3), 2,3,4-tribromopyrrole (4), and ethyl 4-bromopyrrole-2-carboxylate (5) added acutely to NGC showed differential potency; rank order TBP (IC50 ≈ 220 nM) > 2 ≫ 5, whereas 3 and 4 were inactive at 10 μM. TBP >2 μM elicited sustained elevation of cytoplasmic [Ca2+] and loss of neuronal viability. TBP did not alter network ESA. BrPyr from marine and anthropogenic sources are ecological signaling molecules and emerging anthropogenic pollutants of concern to environmental and human health that potently alter ER Ca2+ dynamics and warrant further investigation in vivo.

Closed-loop control of neural spike rate of cultured neurons using a thermoplasmonics-based photothermal neural stimulation

Objective. Photothermal neural stimulation has been developed in a variety of interfaces as an alternative technology that can perturb neural activity. The demonstrations of these techniques have heavily relied on open-loop stimulation or complete suppression of neural activity. To extend the controllability of photothermal neural stimulation, combining it with a closed-loop system is required. In this work, we investigated whether photothermal suppression mechanism can be used in a closed-loop system to reliably modulate neural spike rate to non-zero setpoints. Approach. To incorporate the photothermal inhibition mechanism into the neural feedback system, we combined a thermoplasmonic stimulation platform based on gold nanorods (GNRs) and near-infrared illuminations (808 nm, spot size: 2 mm or 200 μm in diameter) with a proportional-integral (PI) controller. The closed-loop feedback control system was implemented to track predetermined target spike rates of hippocampal neuronal networks cultured on GNR-coated microelectrode arrays. Main results. The closed-loop system for neural spike rate control was successfully implemented using a PI controller and the thermoplasmonic neural suppression platform. Compared to the open-loop control, the target-channel spike rates were precisely modulated to remain constant or change in a sinusoidal form in the range below baseline spike rates. The spike rate response behaviors were affected by the choice of the controller gain. We also demonstrated that the functional connectivity of a synchronized bursting network could be altered by controlling the spike rate of one of the participating channels. Significance. The thermoplasmonic feedback controller proved that it can precisely modulate neural spike rate of neural activity in vitro. This technology can be used for studying neuronal network dynamics and might provide insights in developing new neuromodulation techniques in clinical applications.

Bringing Anatomical Information into Neuronal Network Models.

For constructing neuronal network models computational neuroscientists have access to wide-ranging anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined. Finding and interpreting the most relevant data, estimating missing values, and combining the data and estimates from various sources into a coherent whole is a daunting task. With this chapter we aim to provide guidance to modelers by describing the main types of anatomical data that may be useful for informing neuronal network models. We further discuss aspects of the underlying experimental techniques relevant to the interpretation of the data, list particularly comprehensive data sets, and describe methods for filling in the gaps in the experimental data. Such methods of "predictive connectomics" estimate connectivity where the data are lacking based on statistical relationships with known quantities. Exploiting organizational principles that link the plethora of data in a unifying framework can be useful for informing computational models. Besides overarching principles, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on the mammalian cerebral cortex. Given the still existing need for modelers to navigate a complex data landscape full of holes and stumbling blocks, it is vital that the field of neuroanatomy is moving toward increasingly systematic data collection, representation, and publication.