Changes of the biophysical properties of voltage-gated Na+ currents during maturation of the sodium-taste cells in rat fungiform papillae.

Taste cells are a heterogeneous population of sensory receptors that undergo continuous turnover. Different chemo-sensitive cell lines rely on action potentials to release the neurotransmitter onto nerve endings. The electrical excitability is due to the presence of a tetrodotoxin-sensitive, voltage-gated sodium current (INa ) similar to that found in neurons. Since the biophysical properties of neuronal INa change during development, we wondered whether the same also occurred in taste cells. Here, we used the patch-clamp recording technique to study INa in salt-sensing cells (sodium cells) of rat fungiform papillae. We identified these cells by exploiting the known blocking effect of amiloride on ENaC, the sodium (salt) receptor. Based on the amplitude of INa , which is known to increase during development, we subdivided sodium cells into two groups: cells with small sodium current (SSC cells; INa  < 1 nA) and cells with large sodium current (LSC cells; INa  > 1 nA). We found that: the voltage dependence of activation and inactivation significantly differed between these subsets; a slowly inactivating sodium current was more prominent in LSC cells; membrane capacitance in SSC cells was larger than in LSC cells. mRNA expression analysis of the α-subunits of voltage-gated sodium channels in fungiform taste buds supported the functional data. Lucifer Yellow labelling of recorded cells revealed that our electrophysiological criterion for distinguishing two broad groups of taste cells was in good agreement with morphological observations for cell maturity. Thus, all these findings are consistent with developmental changes in the voltage-dependent properties of sodium-taste cells. KEY POINTS: Taste cells are sensory receptors that undergo continuous turnover while they detect food chemicals and communicate with afferent nerve fibres. The voltage-gated sodium current (INa ) is a key ion current for generating action potentials in fully differentiated and chemo-sensitive taste cells, which use electrical signalling to release neurotransmitters. Here we show that, during the maturation of rat taste cells involved in salt detection (sodium cells), the biophysical properties of INa , such as voltage dependence of activation and inactivation, change significantly. Our results help reveal how taste cells gain electrical excitability during turnover, a property critical to their operation as chemical detectors that relay sensory information to nerve fibres.

Experimental determination of shift-less aberration bases for sensorless adaptive optics in nonlinear microscopy.

Adaptive optics can improve the performance of optical systems and devices by correcting phase aberrations. While in most applications wavefront sensing is employed to drive the adaptive optics correction, some microscopy methods may require sensorless optimization of the wavefront. In these cases, the correction is performed by describing the aberration as a linear combination of a base of influence functions, optimizing an image quality metric as a function of the coefficients. The influence functions base is generally chosen to either efficiently represent the adaptive device used or to describe generic wavefronts in an orthogonal fashion. A rarely discussed problem is that most correction bases have elements which introduce, together with a correction of the aberration, a shift of the imaging field of view in three dimensions. While simple methods to solve the problem are available for linear microscopy methods, nonlinear microscopy techniques such as multiphoton or second harmonic generation microscopy require non-trivial base determination. In this paper, we discuss the problem, and we present a method for calibrating a shift-less base on a spatial light modulator for two-photon microscopy.

Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders.

The investigation of synaptic functions remains one of the most fascinating challenges in the field of neuroscience and a large number of experimental methods have been tuned to dissect the mechanisms taking part in the neurotransmission process. Furthermore, the understanding of the insights of neurological disorders originating from alterations in neurotransmission often requires the development of (i) animal models of pathologies, (ii) invasive tools and (iii) targeted pharmacological approaches. In the last decades, additional tools to explore neurological diseases have been provided to the scientific community. A wide range of computational models in fact have been developed to explore the alterations of the mechanisms involved in neurotransmission following the emergence of neurological pathologies. Here, we review some of the advancements in the development of computational methods employed to investigate neuronal circuits with a particular focus on the application to the most diffuse neurological disorders.

Inhibitory Plasticity: From Molecules to Computation and Beyond.

Synaptic plasticity is the cellular and molecular counterpart of learning and memory and, since its first discovery, the analysis of the mechanisms underlying long-term changes of synaptic strength has been almost exclusively focused on excitatory connections. Conversely, inhibition was considered as a fixed controller of circuit excitability. Only recently, inhibitory networks were shown to be finely regulated by a wide number of mechanisms residing in their synaptic connections. Here, we review recent findings on the forms of inhibitory plasticity (IP) that have been discovered and characterized in different brain areas. In particular, we focus our attention on the molecular pathways involved in the induction and expression mechanisms leading to changes in synaptic efficacy, and we discuss, from the computational perspective, how IP can contribute to the emergence of functional properties of brain circuits.

Heterosynaptic GABAergic plasticity bidirectionally driven by the activity of pre- and postsynaptic NMDA receptors.

Dynamic changes of the strength of inhibitory synapses play a crucial role in processing neural information and in balancing network activity. Here, we report that the efficacy of GABAergic connections between Golgi cells and granule cells in the cerebellum is persistently altered by the activity of glutamatergic synapses. This form of plasticity is heterosynaptic and is expressed as an increase (long-term potentiation, LTPGABA) or a decrease (long-term depression, LTDGABA) of neurotransmitter release. LTPGABA is induced by postsynaptic NMDA receptor activation, leading to calcium increase and retrograde diffusion of nitric oxide, whereas LTDGABA depends on presynaptic NMDA receptor opening. The sign of plasticity is determined by the activation state of target granule and Golgi cells during the induction processes. By controlling the timing of spikes emitted by granule cells, this form of bidirectional plasticity provides a dynamic control of the granular layer encoding capacity.

Long-Term Spatiotemporal Reconfiguration of Neuronal Activity Revealed by Voltage-Sensitive Dye Imaging in the Cerebellar Granular Layer.

Understanding the spatiotemporal organization of long-term synaptic plasticity in neuronal networks demands techniques capable of monitoring changes in synaptic responsiveness over extended multineuronal structures. Among these techniques, voltage-sensitive dye imaging (VSD imaging) is of particular interest due to its good spatial resolution. However, improvements of the technique are needed in order to overcome limits imposed by its low signal-to-noise ratio. Here, we show that VSD imaging can detect long-term potentiation (LTP) and long-term depression (LTD) in acute cerebellar slices. Combined VSD imaging and patch-clamp recordings revealed that the most excited regions were predominantly associated with granule cells (GrCs) generating EPSP-spike complexes, while poorly responding regions were associated with GrCs generating EPSPs only. The correspondence with cellular changes occurring during LTP and LTD was highlighted by a vector representation obtained by combining amplitude with time-to-peak of VSD signals. This showed that LTP occurred in the most excited regions lying in the core of activated areas and increased the number of EPSP-spike complexes, while LTD occurred in the less excited regions lying in the surround. VSD imaging appears to be an efficient tool for investigating how synaptic plasticity contributes to the reorganization of multineuronal activity in neuronal circuits.

Full-scale scaffold model of the human hippocampus CA1 area.

The increasing availability of quantitative data on the human brain is opening new avenues to study neural function and dysfunction, thus bringing us closer and closer to the implementation of digital twin applications for personalized medicine. Here we provide a resource to the neuroscience community: a computational method to generate full-scale scaffold model of human brain regions starting from microscopy images. We have benchmarked the method to reconstruct the CA1 region of a right human hippocampus, which accounts for about half of the entire right hippocampal formation. Together with 3D soma positioning we provide a connectivity matrix generated using a morpho-anatomical connection strategy based on axonal and dendritic probability density functions accounting for morphological properties of hippocampal neurons. The data and algorithms are supplied in a ready-to-use format, suited to implement computational models at different scales and detail.

A Hybrid CMOS-Memristor Spiking Neural Network Supporting Multiple Learning Rules.

Artificial intelligence (AI) is changing the way computing is performed to cope with real-world, ill-defined tasks for which traditional algorithms fail. AI requires significant memory access, thus running into the von Neumann bottleneck when implemented in standard computing platforms. In this respect, low-latency energy-efficient in-memory computing can be achieved by exploiting emerging memristive devices, given their ability to emulate synaptic plasticity, which provides a path to design large-scale brain-inspired spiking neural networks (SNNs). Several plasticity rules have been described in the brain and their coexistence in the same network largely expands the computational capabilities of a given circuit. In this work, starting from the electrical characterization and modeling of the memristor device, we propose a neuro-synaptic architecture that co-integrates in a unique platform with a single type of synaptic device to implement two distinct learning rules, namely, the spike-timing-dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM). This architecture, by exploiting the aforementioned learning rules, successfully addressed two different tasks of unsupervised learning.

Long-Term Synaptic Plasticity Tunes the Gain of Information Channels through the Cerebellum Granular Layer.

A central hypothesis on brain functioning is that long-term potentiation (LTP) and depression (LTD) regulate the signals transfer function by modifying the efficacy of synaptic transmission. In the cerebellum, granule cells have been shown to control the gain of signals transmitted through the mossy fiber pathway by exploiting synaptic inhibition in the glomeruli. However, the way LTP and LTD control signal transformation at the single-cell level in the space, time and frequency domains remains unclear. Here, the impact of LTP and LTD on incoming activity patterns was analyzed by combining patch-clamp recordings in acute cerebellar slices and mathematical modeling. LTP reduced the delay, increased the gain and broadened the frequency bandwidth of mossy fiber burst transmission, while LTD caused opposite changes. These properties, by exploiting NMDA subthreshold integration, emerged from microscopic changes in spike generation in individual granule cells such that LTP anticipated the emission of spikes and increased their number and precision, while LTD sorted the opposite effects. Thus, akin with the expansion recoding process theoretically attributed to the cerebellum granular layer, LTP and LTD could implement selective filtering lines channeling information toward the molecular and Purkinje cell layers for further processing.

Emergence of associative learning in a neuromorphic inference network.

Objective. In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes-by modelling the activity of functional neural networks at a mesoscopic scale-the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored.Approach.We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller.Main results. Persistent changes of synaptic strength-that mirrored neurophysiological observations-emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures.Significance. These findings show that: (a) an ensemble of free energy minimizing neurons-organized in a biological plausible architecture-can recapitulate functional self-organization observed in nature, such as associative plasticity, and (b) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence.

A realistic morpho-anatomical connection strategy for modelling full-scale point-neuron microcircuits.

The modeling of extended microcircuits is emerging as an effective tool to simulate the neurophysiological correlates of brain activity and to investigate brain dysfunctions. However, for specific networks, a realistic modeling approach based on the combination of available physiological, morphological and anatomical data is still an open issue. One of the main problems in the generation of realistic networks lies in the strategy adopted to build network connectivity. Here we propose a method to implement a neuronal network at single cell resolution by using the geometrical probability volumes associated with pre- and postsynaptic neurites. This allows us to build a network with plausible connectivity properties without the explicit use of computationally intensive touch detection algorithms using full 3D neuron reconstructions. The method has been benchmarked for the mouse hippocampus CA1 area, and the results show that this approach is able to generate full-scale brain networks at single cell resolution that are in good agreement with experimental findings. This geometric reconstruction of axonal and dendritic occupancy, by effectively reflecting morphological and anatomical constraints, could be integrated into structured simulators generating entire circuits of different brain areas facilitating the simulation of different brain regions with realistic models.

Real Time Generation of Three Dimensional Patterns for Multiphoton Stimulation.

The advent of optogenetics has revolutionized experimental research in the field of Neuroscience and the possibility to selectively stimulate neurons in 3D volumes has opened new routes in the understanding of brain dynamics and functions. The combination of multiphoton excitation and optogenetic methods allows to identify and excite specific neuronal targets by means of the generation of cloud of excitation points. The most widely employed approach to produce the points cloud is through a spatial light modulation (SLM) which works with a refresh rate of tens of Hz. However, the computational time requested to calculate 3D patterns ranges between a few seconds and a few minutes, strongly limiting the overall performance of the system. The maximum speed of SLM can in fact be employed either with high quality patterns embedded into pre-calculated sequences or with low quality patterns for real time update. Here, we propose the implementation of a recently developed compressed sensing Gerchberg-Saxton algorithm on a consumer graphical processor unit allowing the generation of high quality patterns at video rate. This, would in turn dramatically reduce dead times in the experimental sessions, and could enable applications previously impossible, such as the control of neuronal network activity driven by the feedback from single neurons functional signals detected through calcium or voltage imaging or the real time compensation of motion artifacts.

Modeling Early Phases of COVID-19 Pandemic in Northern Italy and Its Implication for Outbreak Diffusion.

The COVID-19 pandemic has sparked an intense debate about the hidden factors underlying the dynamics of the outbreak. Several computational models have been proposed to inform effective social and healthcare strategies. Crucially, the predictive validity of these models often depends upon incorporating behavioral and social responses to infection. Among these tools, the analytic framework known as "dynamic causal modeling" (DCM) has been applied to the COVID-19 pandemic, shedding new light on the factors underlying the dynamics of the outbreak. We have applied DCM to data from northern Italian regions, the first areas in Europe to contend with the outbreak, and analyzed the predictive validity of the model and also its suitability in highlighting the hidden factors governing the pandemic diffusion. By taking into account data from the beginning of the pandemic, the model could faithfully predict the dynamics of outbreak diffusion varying from region to region. The DCM appears to be a reliable tool to investigate the mechanisms governing the spread of the SARS-CoV-2 to identify the containment and control strategies that could efficiently be used to counteract further waves of infection.

The effects of the general anesthetic sevoflurane on neurotransmission: an experimental and computational study.

The brain functions can be reversibly modulated by the action of general anesthetics. Despite a wide number of pharmacological studies, an extensive analysis of the cellular determinants of anesthesia at the microcircuits level is still missing. Here, by combining patch-clamp recordings and mathematical modeling, we examined the impact of sevoflurane, a general anesthetic widely employed in the clinical practice, on neuronal communication. The cerebellar microcircuit was used as a benchmark to analyze the action mechanisms of sevoflurane while a biologically realistic mathematical model was employed to explore at fine grain the molecular targets of anesthetic analyzing its impact on neuronal activity. The sevoflurane altered neurotransmission by strongly increasing GABAergic inhibition while decreasing glutamatergic NMDA activity. These changes caused a notable reduction of spike discharge in cerebellar granule cells (GrCs) following repetitive activation by excitatory mossy fibers (mfs). Unexpectedly, sevoflurane altered GrCs intrinsic excitability promoting action potential generation. Computational modelling revealed that this effect was triggered by an acceleration of persistent sodium current kinetics and by an increase in voltage dependent potassium current conductance. The overall effect was a reduced variability of GrCs responses elicited by mfs supporting the idea that sevoflurane shapes neuronal communication without silencing neural circuits.

Combinatorial responses controlled by synaptic inhibition in the cerebellum granular layer.

The granular layer of cerebellum has been long hypothesized to perform combinatorial operations on incoming signals. Although this assumption is at the basis of main computational theories of cerebellum, it has never been assessed experimentally. Here, by applying high-resolution voltage-sensitive dye imaging techniques, we show that simultaneous activation of two partially overlapping mossy fiber bundles (either with single pulses or high-frequency bursts) can cause combined excitation and combined inhibition, which are compatible with the concepts of coincidence detection and spatial pattern separation predicted by theory. Combined excitation appeared as an area in which the combination of two inputs is greater than the arithmetic sum of the individual inputs and was enhanced by gamma-aminobutyric acid type A (GABA(A)) receptor blockers. Combined inhibition was manifest as an area where two inputs combined resulted in a reduction to less than half of the activity evoked from either one of the two inputs alone and was prevented by GABA(A) receptor blockers. The combinatorial responses occupied small granular layer regions (approximately 30 microm diameter), with combined inhibition being interspersed among extended areas of combined excitation. Moreover, the combinatorial effects lasted for tens of milliseconds and combined inhibition occurred only after termination of the stimuli. These combinatorial operations, if engaged by natural input patterns in vivo, may be important to influence incoming impulses organizing spatiotemporal spike sequences to be relayed to Purkinje cells.

Cellular-resolution mapping uncovers spatial adaptive filtering at the rat cerebellum input stage.

Long-term synaptic plasticity is thought to provide the substrate for adaptive computation in brain circuits but very little is known about its spatiotemporal organization. Here, we combined multi-spot two-photon laser microscopy in rat cerebellar slices with realistic modeling to map the distribution of plasticity in multi-neuronal units of the cerebellar granular layer. The units, composed by ~300 neurons activated by ~50 mossy fiber glomeruli, showed long-term potentiation concentrated in the core and long-term depression in the periphery. This plasticity was effectively accounted for by an NMDA receptor and calcium-dependent induction rule and was regulated by the inhibitory Golgi cell loops. Long-term synaptic plasticity created effective spatial filters tuning the time-delay and gain of spike retransmission at the cerebellum input stage and provided a plausible basis for the spatiotemporal recoding of input spike patterns anticipated by the motor learning theory.

The spatial organization of long-term synaptic plasticity at the input stage of cerebellum.

The spatial organization of long-term synaptic plasticity [long-term potentiation (LTP) and long-term depression (LTD)] is supposed to play a critical role for distributed signal processing in neuronal networks, but its nature remains undetermined in most central circuits. By using multielectrode array recordings, we have reconstructed activation maps of the granular layer in cerebellar slices. LTP and LTD induced by theta-burst stimulation appeared in patches organized in such a way that, on average, LTP was surrounded by LTD. The sign of long-term synaptic plasticity in a given granular layer region was directly correlated with excitation and inversely correlated with inhibition: the most active areas tended to generate LTP, whereas the least active areas tended to generate LTD. Plasticity was almost entirely prevented by application of the NMDA receptor blocker, APV. This suggests that synaptic inhibition, through a control of membrane depolarization, effectively regulates NMDA channel unblock, postsynaptic calcium entry, and the induction of bidirectional synaptic plasticity at the mossy fiber-granule cell relay (Gall et al., 2005). By this mechanism, LTP and LTD could regulate the geometry and contrast of network computations, preprocessing the mossy fiber input to be conveyed to Purkinje cells and molecular layer interneurons.

Optical recording of electrical activity in intact neuronal networks with random access second-harmonic generation microscopy.

One of the main challenges in understanding the central nervous system is to measure the network dynamics of neuronal assemblies, while preserving the computational role of individual neurons. However, this is not possible with current techniques. In this work, we combined the advantages of second-harmonic generation (SHG) with a random access (RA) excitation scheme to realize a new microscope (RASH) capable of optically recording fast membrane potential events occurring in a wide-field of view. The RASH microscope, in combination with bulk loading of tissue with FM4-64 dye, was used to simultaneously record electrical activity from clusters of Purkinje cells in acute cerebellar slices. Complex spikes, both synchronous and asynchronous, were optically recorded simultaneously across a given population of neurons. Spontaneous electrical activity was also monitored simultaneously in pairs of neurons, where action potentials were recorded without averaging across trials. These results show the strength of this technique in describing the temporal dynamics of neuronal assemblies, opening promising perspectives in understanding the computations of neuronal networks.

Use of a 3D Floating Sphere Culture System to Maintain the Neural Crest-Related Properties of Human Dental Pulp Stem Cells.

Human dental pulp is considered an interesting source of adult stem cells, due to the low-invasive isolation procedures, high content of stem cells and its peculiar embryological origin from neural crest. Based on our previous findings, a dental pulp stem cells sub-population, enriched for the expression of STRO-1, c-Kit, and CD34, showed a higher neural commitment. However, their biological properties were compromised when cells were cultured in adherent standard conditions. The aim of this study was to evaluate the ability of three dimensional floating spheres to preserve embryological and biological properties of this sub-population. In addition, the expression of the inwardly rectifying potassium channel Kir4.1, Fas and FasL was investigated in 3D-sphere derived hDPSCs. Our data showed that 3D sphere-derived hDPSCs maintained their fibroblast-like morphology, preserved stemness markers expression and proliferative capability. The expression of neural crest markers and Kir4.1 was observed in undifferentiated hDPSCs, furthermore this culture system also preserved hDPSCs differentiation potential. The expression of Fas and FasL was observed in undifferentiated hDPSCs derived from sphere culture and, noteworthy, FasL was maintained even after the neurogenic commitment was reached, with a significantly higher expression compared to osteogenic and myogenic commitments. These data demonstrate that 3D sphere culture provides a favorable micro-environment for neural crest-derived hDPSCs to preserve their biological properties.