Neuronal Information Processing Research Articles

Vibrational resonance in a neuron-astrocyte coupled model.

Recent findings have revealed that not only neurons but also astrocytes, a special type of glial cells, are major players of neuronal information processing. It is now widely accepted that they contribute to the regulation of their microenvironment by cross-talking with neurons via gliotransmitters. In this context, we here study the phenomenon of vibrational resonance in neurons by considering their interaction with astrocytes. Our analysis of a neuron-astrocyte pair reveals that intracellular dynamics of astrocytes can induce a double vibrational resonance effect in the weak signal detection performance of a neuron, exhibiting two distinct wells centred at different high-frequency driving amplitudes. We also identify the underlying mechanism of this behaviour, showing that the interaction of widely separated time scales of neurons, astrocytes and driving signals is the key factor for the emergence and control of double vibrational resonance. This article is part of the theme issue 'Vibrational and stochastic resonance in driven nonlinear systems (part 2)'.

Measurement of intact quantal packet of transmitters released from single nerve terminal by loose-patch amperometry

Neuronal information is majorly encoded chemically at synapses and the elementary unit of synaptic transmission is the contents of neurotransmitter released from single vesicle. However, the contents of quantal neurotransmitter have never been precisely estimated at synapses, which largely prevent our understanding the nature of quantal neurotransmitter release and its impact on neuronal information processing. In order to break through the technical bottleneck of precisely counting quantal neurotransmitter molecules, we developed a new approach in combination of electrophysiology and electrochemistry to measure intact quantal content of single vesicles. An etched submicro-carbon fiber electrode for electrochemical detection was designed to be enclosed in an electrophysiologically used glass pipette. The glass pipette allowed the electrochemical electrode to access the release site, and amperometric recordings were made within the enclosed space at the electrophysiological loose-patch mode. Our study showed that the intact quantal release could be successfully detected at the dopaminergic varicosities by this loose-patch amperometric measurement in real time with negligible leakage.

SPIKING ACTIVITY OF LIFH NEURON MODEL WITH VARIABLE INPUT STIMULUS

Neuronal information processing occurs in term of spikes. A neuron can emits various kinds of spiking patterns based on the applied input stimulus. In this article, we study the spiking pattern of LIFH neuron model in the presence of four different kinds of applied input stimulus, namely, constant input stimulus, uniformly distributed input stimulus, Gaussian distributed input stimulus and stochastic input stimulus. Here, we notice the tonic and semi-tonic spiking pattern for Gaussian distributed input stimulus and stochastic input stimulus.

A Circadian Clock in the Retina Regulates Rod-Cone Gap Junction Coupling and Neuronal Light Responses via Activation of Adenosine A2A Receptors.

Adenosine, a major neuromodulator in the central nervous system (CNS), is involved in a variety of regulatory functions such as the sleep/wake cycle. Because exogenous adenosine displays dark- and night-mimicking effects in the vertebrate retina, we tested the hypothesis that a circadian (24 h) clock in the retina uses adenosine to control neuronal light responses and information processing. Using a variety of techniques in the intact goldfish retina including measurements of adenosine overflow and content, tracer labeling, and electrical recording of the light responses of cone photoreceptor cells and cone horizontal cells (cHCs), which are post-synaptic to cones, we demonstrate that a circadian clock in the retina itself—but not activation of melatonin or dopamine receptors—controls extracellular and intracellular adenosine levels so that they are highest during the subjective night. Moreover, the results show that the clock increases extracellular adenosine at night by enhancing adenosine content so that inward adenosine transport ceases. Also, we report that circadian clock control of endogenous cone adenosine A2A receptor activation increases rod-cone gap junction coupling and rod input to cones and cHCs at night. These results demonstrate that adenosine and A2A receptor activity are controlled by a circadian clock in the retina, and are used by the clock to modulate rod-cone electrical synapses and the sensitivity of cones and cHCs to very dim light stimuli. Moreover, the adenosine system represents a separate circadian-controlled pathway in the retina that is independent of the melatonin/dopamine pathway but which nevertheless acts in concert to enhance the day/night difference in rod-cone coupling.

Firing patterns of the CA1 pyramidal neuron with geometric singular perturbation: a model study

An investigation of CA1 pyramidal model is an important issue for applications, which is intimately related to the composition of ions in the extracellular environment and external stimulation. In this paper, it is demonstrated that the effects of different electrophysiological parameters such as muscarinic-sensitive potassium current activation variable and sustained sodium current inactivation variable on the firing sequence of model by numerical simulations. Furthermore, the paper also discusses that the temperature affects the firing of the CA1 model from direct current (DC) and alternating current (AC) stimuli. It is found that the model exhibits excellent spiking and bursting patterns, even chaotic patterns occur. Meanwhile, generalized mixed oscillations emerge in the model. Additionally, the firing modes are depicted by providing the response curve (RC), inter-spike interval curve (ISI), phase diagram curve (PDC) and the number of spikes per burst curve (NC). Mathematically, the paper elaborates the results which are presented to obtain two lower dimensional subsystems, which govern the fast and slow dynamics for giving insight into the dynamic behaviors of the full 5D system based on the geometric singular perturbation theory (GSPT). Particularly, we analyse the phase diagrams of the CA1 model to understand the properties better. The present results may contribute to further understand the information processing of the CA1 pyramidal neurons.

Motor beta cortical oscillations are related with the gait kinematics of youth with cerebral palsy.

ObjectiveIt is widely believed that the perinatal brain injuries seen in youth with cerebral palsy (CP) impact neuronal processing of sensory information and the production of leg motor actions during gait. However, very limited efforts have been made to evaluate the connection between neural activity within sensorimotor networks and the altered spatiotemportal gait biomechanics seen in youth with CP. The objective of this investigation was to use magnetoencephalographic (MEG) brain imaging and biomechanical analysis to probe this connection.MethodsWe examined the cortical beta oscillations serving motor control of the legs in a cohort of youth with CP (N = 20; Age = 15.5 ± 3 years; GMFCS levels I‐III) and healthy controls (N = 15; Age = 14.1 ± 3 years) using MEG brain imaging and a goal‐directed isometric knee target‐matching task. Outside the scanner, a digital mat was used to quantify the spatiotemporal gait biomechanics.ResultsOur MEG imaging results revealed that the participants with CP exhibited stronger sensorimotor beta oscillations during the motor planning and execution stages compared to the controls. Interestingly, we also found that those with the strongest sensorimotor beta oscillations during motor execution also tended to walk slower and have a reduced cadence.InterpretationThese results fuel the impression that the beta sensorimotor cortical oscillations that underlie leg musculature control may play a central role in the altered mobility seen in youth with CP.

Developing a photonic hardware platform for brain-inspired computing based on 5 × 5 VCSEL arrays

Brain-inspired computing concepts like artificial neural networks have become promising alternatives to classical von Neumann computer architectures. Photonic neural networks target the realizations of neurons, network connections and potentially learning in photonic substrates. Here, we report the development of a nanophotonic hardware platform of fast and energy-efficient photonic neurons via arrays of high-quality vertical cavity surface emitting lasers (VCSELs). The developed 5 × 5 VCSEL arrays provide high optical injection locking efficiency through homogeneous fabrication combined with individual control over the laser wavelengths. Injection locking is crucial for the reliable processing of information in VCSEL-based photonic neurons, and we demonstrate the suitability of the VCSEL arrays by injection locking measurements and current-induced spectral fine-tuning. We find that our investigated array can readily be tuned to the required spectral homogeneity, and as such show that VCSEL arrays based on our technology can act as highly energy efficient and ultra-fast photonic neurons for next generation photonic neural networks. Combined with fully parallel photonic networks our substrates are promising for ultra-fast operation reaching 10 s of GHz bandwidths, and we show that a single non-linear transformation based on our lasers will consume only about 100 fJ per VCSEL, which is highly competitive, compared to other platforms.

Miniature Fluorescence Microscopy for Imaging Brain Activity in Freely-Behaving Animals.

An ultimate goal of neuroscience is to decipher the principles underlying neuronal information processing at the molecular, cellular, circuit, and system levels. The advent of miniature fluorescence microscopy has furthered the quest by visualizing brain activities and structural dynamics in animals engaged in self-determined behaviors. In this brief review, we summarize recent advances in miniature fluorescence microscopy for neuroscience, focusing mostly on two mainstream solutions - miniature single-photon microscopy, and miniature two-photon microscopy. We discuss their technical advantages and limitations as well as unmet challenges for future improvement. Examples of preliminary applications are also presented to reflect on a new trend of brain imaging in experimental paradigms involving body movements, long and complex protocols, and even disease progression and aging.

Translation information processing is regulated by protein kinase C-dependent mechanism in Purkinje cells in murine posterior vermis

The cerebellar posterior vermis generates an estimation of our motion (translation) and orientation (tilt) in space using cues originating from semicircular canals and otolith organs. Theoretical work has laid out the basic computations necessary for this signal transformation, but details on the cellular loci and mechanisms responsible are lacking. Using a multicomponent modeling approach, we show that canal and otolith information are spatially and temporally matched in mouse posterior vermis Purkinje cells and that Purkinje cell responses combine translation and tilt information. Purkinje cell-specific inhibition of protein kinase C decreased and phase-shifted the translation component of Purkinje cell responses, but did not affect the tilt component. Our findings suggest that translation and tilt signals reach Purkinje cells via separate information pathways and that protein kinase C-dependent mechanisms regulate translation information processing in cerebellar cortex output neurons.

Lactate Attenuates Synaptic Transmission and Affects Brain Rhythms Featuring High Energy Expenditure.

SummaryLactate shuttled from blood, astrocytes, and/or oligodendrocytes may serve as the major glucose alternative in brain energy metabolism. However, its effectiveness in fueling neuronal information processing underlying complex cortex functions like perception and memory is unclear. We show that sole lactate disturbs electrical gamma and theta-gamma oscillations in hippocampal networks by either attenuation or neural bursts. Bursting is suppressed by elevating the glucose fraction in substrate supply. By contrast, lactate does not affect electrical sharp wave-ripple activity featuring lower energy use. Lactate increases the oxygen consumption during the network states, reflecting enhanced oxidative ATP synthesis in mitochondria. Finally, lactate attenuates synaptic transmission in excitatory pyramidal cells and fast-spiking, inhibitory interneurons by reduced neurotransmitter release from presynaptic terminals, whereas action potential generation in the axon is regular. In conclusion, sole lactate is less effective and potentially harmful during gamma-band rhythms by omitting obligatory ATP delivery through fast glycolysis at the synapse.

Measurement of excitation-inhibition ratio in autism spectrum disorder using critical brain dynamics

Balance between excitation (E) and inhibition (I) is a key principle for neuronal network organization and information processing. Consistent with this notion, excitation-inhibition imbalances are considered a pathophysiological mechanism in many brain disorders including autism spectrum disorder (ASD). However, methods to measure E/I ratios in human brain networks are lacking. Here, we present a method to quantify a functional E/I ratio (fE/I) from neuronal oscillations, and validate it in healthy subjects and children with ASD. We define structural E/I ratio in an in silico neuronal network, investigate how it relates to power and long-range temporal correlations (LRTC) of the network’s activity, and use these relationships to design the fE/I algorithm. Application of this algorithm to the EEGs of healthy adults showed that fE/I is balanced at the population level and is decreased through GABAergic enforcement. In children with ASD, we observed larger fE/I variability and stronger LRTC compared to typically developing children (TDC). Interestingly, visual grading for EEG abnormalities that are thought to reflect E/I imbalances revealed elevated fE/I and LRTC in ASD children with normal EEG compared to TDC or ASD with abnormal EEG. We speculate that our approach will help understand physiological heterogeneity also in other brain disorders.

MiRNAs-dependent regulation of synapse formation and function.

A synapse is a fundamental signaling component that facilitates neuronal connectivity and information processing in the brain. Dynamic changes in the number, size, and functionality of synapse are induced by extensive signaling networks and structural proteins, which are stimulated on various neuronal activities. Changes in the expression level of synaptic proteins depend upon the physiological and pathological conditions at transcriptional, post-transcriptional, and post-translational levels. MicroRNAs (miRNAs) have not only emerged as pivotal gene expression regulators in neurons, but also in diverse cell types. miRNAs are evolutionarily conserved small non-coding RNAs that modulate mRNA stability and protein synthesis by interacting with 3'-untranslated region (3'-UTR) of mRNAs. Often, miRNA expression is limited to specific neuronal compartments such as axons, dendrites, and cell body to locally regulate protein synthesis in response to various stimuli. Increasing evidences suggest that miRNAs are involved in the regulation of neuronal proliferation, differentiation, migration, development, and many other processes. This article reviews recent findings on the role of miRNAs in synapse formation and function. Many studies have elucidated the role of miRNAs in diverse neuronal physiological and pathological processes. A better understanding of the mechanisms involved in miRNA functioning at the synapse will be beneficial in formulating novel therapeutic strategies.

A Model for the Study of the Increase in Stimulus and Change Point Detection with Small and Variable Spiking Delays.

Precise timing of spikes between different neurons has been found to convey reliable information beyond the spike count. In contrast, the role of small and variable spiking delays, as reported, for example, in the visual cortex, remains largely unclear. This issue becomes particularly important considering the high speed of neuronal information processing, which is assumed to be based on only a few milliseconds within each processing step. We investigate the role of small and variable spiking delays with a parsimonious stochastic spiking model that is strongly motivated by experimental observations. The model contains only two parameters for the response of a neuron to one stimulus, describing directly the rate and the delay, or phase. Within the theoretical model, we specifically investigate two quantities, the probability of correct stimulus detection and the probability of correct change point detection, as a function of these parameters and within short periods of time. Optimal combinations of the two parameters across stimuli are derived that maximize these probabilities and enable comparison of pure rate, pure phase, and combined codes. In particular, the gain in correct detection probability when adding small and variable spiking delays to pure rate coding increases with the number of stimuli. More interesting, small and variable spiking delays can considerably improve the process of detecting changes in the stimulus, while also decreasing the probability of false alarms and thus increasing robustness and speed of change point detection. The results are compared to empirical spike train recordings of neurons in the visual cortex reported earlier in response to a number of visual stimuli. The results suggest that near-optimal combinations of rate and phase parameters may be implemented in the brain and that adding phase information could particularly increase the quality of change point detection in cases of highly similar stimuli.

Lifespan associated global patterns of coherent neural communication

Healthy ageing is accompanied by changes to spontaneous electromagnetic oscillations. At the macroscopic scale, previous studies have quantified the basic features, e.g., power and frequencies in rhythms of interest from the perspective of attention, perception, learning and memory. On the other hand, signatures and modes of neural communication have recently been argued to be identifiable from global measures applied on neuro-electromagnetic data such as global coherence that quantifies the degree of togetherness of distributed neural oscillations and metastability that parametrizes the transient dynamics of the network switching between successive stable states. Here, we demonstrate that global coherence and metastability can be informative measures to track healthy ageing dynamics over lifespan and together with the traditional spectral measures provides an attractive explanation of neuronal information processing. Finding normative patterns of brain rhythms in resting state MEG would naturally pave the way for tracking task relevant metrics that could crucially determine cognitive flexibility and performance. While previously reported observations of a reduction in peak alpha frequency and increased beta power in older adults are reflective of changes at individual sensors (during rest and task), global coherence and metastability pinpoint the underlying coordination dynamics over multiple brain areas across the entire lifespan. In addition to replication of the previous observations in a substantially larger lifespan cohort than what was previously reported, we also demonstrate, for the first time to the best of our knowledge, age related changes in coherence and metastability in signals over time scales of neuronal processing. Furthermore, we observed a marked frequency dependence in changes in global coordination dynamics, which, coupled with the long-held view of specific frequency bands subserving different aspects of cognition, hints at differential functional processing roles for slower and faster brain dynamics.

Serotonin improves behavioral contrast sensitivity of freely moving rats.

Serotonin (5-HT) is a neuromodulator secreted from serotonergic neurons located in the pons and upper brain stem in a behavioral context-dependent manner. The serotonergic axon terminals innervate almost the whole brain, causing modulatory actions on various brain functions including vision. Our previous study demonstrated the visual responses of neurons in the primary visual cortex (V1) of anesthetized monkeys were modulated by the activation of 5-HT receptors depending on the response magnitude, in which 5-HT2A receptor-selective agonists enhanced weak visual responses but not strong responses. This observation suggests that the activation of serotonergic receptors modulates neuronal visual information processing to improve the behavioral detectability of a stimulus. However, it remains unknown if 5-HT improves visual detectability at the behavioral level. To investigate this point, visual detectability was measured as contrast sensitivity (CS) in freely moving rats using a two-alternative forced-choice visual detection task (2AFC-VDT) combined with the staircase method. The grating contrast was decreased or increased step by step after a correct choice (hit) or incorrect choice (miss), respectively. CS was evaluated as an inverse value of the visual contrast threshold. The effect of the intraperitoneal administration of fluoxetine (FLX, 5 mg/kg), a selective serotonin reuptake inhibitor, on CS was tested. The CS of rats was significantly higher in FLX than control conditions, and the drug effect showed specificity for the spatial frequency (SF) of a grating stimulus, in which CS improvement was observed at optimal SF but not non-optimal high SF. Thus, we conclude that endogenously-secreted serotonin in the brain improves visual detectability, which may be mediated by vision-related neurons acquiring SF information of the visual stimulus.

The noise cancelation effects caused by spike-frequency adaptation in single neurons

Spike-frequency adaptation, which reduces the firing rate of a neuron during a constant stimulus, is a prominent property observed in many neurons. In this work, we studied the effects of the $$I_{\mathrm{AHP}}$$ spiking adaptation on the information transmission and efficiency of the Morris–Lecar (ML) neuron model in the spike-timing coding scheme. We showed that this kind of adaptation caused non-trivial spiking dynamics when the input rate was high. Under the stimulation of high-rate inputs, although $$I_{\mathrm{AHP}}$$ adaptation neurons could not outperform non-adaptation neurons in terms of information rate, $$I_{\mathrm{AHP}}$$ adaptation neurons yielded higher coding efficiency than non-adaptation neurons. We also found that the noise could enlarge the range of input rates that the adaptation takes effect to enhance coding efficiency. Increasing the calcium-activated $$\hbox {K}^{+}$$ current could also extend the range of input rates in which adaptation takes effect. Therefore, we argue that the $$I_{\mathrm{AHP}}$$ adaptation mechanism may play a role of adaptive noise cancelation mechanism in neuronal information processing, i.e., it maintains information transmission when neurons receive low-rate inputs but substantially enhancing coding efficiency for high-rate inputs and highly noise environments by suppressing the spike train variability caused by the noise.

Erratum to: Volume Electron Microscopy Study of the Relationship Between Synapses and Astrocytes in the Developing Rat Somatosensory Cortex.

In recent years, numerous studies have shown that astrocytes play an important role in neuronal processing of information. One of the most interesting findings is the existence of bidirectional interactions between neurons and astrocytes at synapses, which has given rise to the concept of from a functional point of view. We used focused ion beam milling and scanning electron microscopy (FIB/SEM) to examine in 3D the relationship of synapses with astrocytes that were previously labeled by intracellular injections in the rat somatosensory cortex. We observed that a large number of synapses (32%) had no contact with astrocytic processes. The remaining synapses (68%) were in contact with astrocytic processes, either at the level of the synaptic cleft (44%) or with the pre- and/or post-synaptic elements (24%). Regarding synaptic morphology, larger synapses with more complex shapes were most frequently found within the population that had the synaptic cleft in contact with astrocytic processes. Furthermore, we observed that although synapses were randomly distributed in space, synapses that were free of astrocytic processes tended to form clusters. Overall, at least in the developing rat neocortex, the concept of tripartite synapse only seems to be applicable to a subset of synapses.

Spontaneous and stimulus-induced coherent states of critically balanced neuronal networks

How the information microscopically processed by individual neurons is integrated and used in organizing the behavior of an animal is a central question in neuroscience. The coherence of neuronal dynamics over different scales has been suggested as a clue to the mechanisms underlying this integration. Balanced excitation and inhibition may amplify microscopic fluctuations to a macroscopic level, thus providing a mechanism for generating coherent multiscale dynamics. Previous theories of brain dynamics, however, were restricted to cases in which inhibition dominated excitation and suppressed fluctuations in the macroscopic population activity. In the present study, we investigate the dynamics of neuronal networks at a critical point between excitation-dominant and inhibition-dominant states. In these networks, the microscopic fluctuations are amplified by the strong excitation and inhibition to drive the macroscopic dynamics, while the macroscopic dynamics determine the statistics of the microscopic fluctuations. Developing a novel type of mean-field theory applicable to this class of interscale interactions, we show that the amplification mechanism generates spontaneous, irregular macroscopic rhythms similar to those observed in the brain. Through the same mechanism, microscopic inputs to a small number of neurons effectively entrain the dynamics of the whole network. These network dynamics undergo a probabilistic transition to a coherent state, as the magnitude of either the balanced excitation and inhibition or the external inputs is increased. Our mean-field theory successfully predicts the behavior of this model. Furthermore, we numerically demonstrate that the coherent dynamics can be used for state-dependent read-out of information from the network. These results show a novel form of neuronal information processing that connects neuronal dynamics on different scales.

Synaptic Plasticity Depends on the Fine-Scale Input Pattern in Thin Dendrites of CA1 Pyramidal Neurons

Coordinated long-term plasticity of nearby excitatory synaptic inputs has been proposed to shape experience-related neuronal information processing. To elucidate the induction rules leading to spatially structured forms of synaptic potentiation in dendrites, we explored plasticity of glutamate uncaging-evoked excitatory input patterns with various spatial distributions in perisomatic dendrites of CA1 pyramidal neurons in slices from adult male rats. We show that (1) the cooperativity rules governing the induction of synaptic LTP depend on dendritic location; (2) LTP of input patterns that are subthreshold or suprathreshold to evoke local dendritic spikes (d-spikes) requires different spatial organization; and (3) input patterns evoking d-spikes can strengthen nearby, nonsynchronous synapses by local heterosynaptic plasticity crosstalk mediated by NMDAR-dependent MEK/ERK signaling. These results suggest that multiple mechanisms can trigger spatially organized synaptic plasticity on various spatial and temporal scales, enriching the ability of neurons to use synaptic clustering for information processing.SIGNIFICANCE STATEMENT A fundamental question in neuroscience is how neuronal feature selectivity is established via the combination of dendritic processing of synaptic input patterns with long-term synaptic plasticity. As these processes have been mostly studied separately, the relationship between the rules of integration and rules of plasticity remained elusive. Here we explore how the fine-grained spatial pattern and the form of voltage integration determine plasticity of different excitatory synaptic input patterns in perisomatic dendrites of CA1 pyramidal cells. We demonstrate that the plasticity rules depend highly on three factors: (1) the location of the input within the dendritic branch (proximal vs distal), (2) the strength of the input pattern (subthreshold or suprathreshold for dendritic spikes), and (3) the stimulation of neighboring synapses.

Volume Electron Microscopy Study of the Relationship Between Synapses and Astrocytes in the Developing Rat Somatosensory Cortex.

In recent years, numerous studies have shown that astrocytes play an important role in neuronal processing of information. One of the most interesting findings is the existence of bidirectional interactions between neurons and astrocytes at synapses, which has given rise to the concept of “tripartite synapses” from a functional point of view. We used focused ion beam milling and scanning electron microscopy (FIB/SEM) to examine in 3D the relationship of synapses with astrocytes that were previously labeled by intracellular injections in the rat somatosensory cortex. We observed that a large number of synapses (32%) had no contact with astrocytic processes. The remaining synapses (68%) were in contact with astrocytic processes, either at the level of the synaptic cleft (44%) or with the pre- and/or post-synaptic elements (24%). Regarding synaptic morphology, larger synapses with more complex shapes were most frequently found within the population that had the synaptic cleft in contact with astrocytic processes. Furthermore, we observed that although synapses were randomly distributed in space, synapses that were free of astrocytic processes tended to form clusters. Overall, at least in the developing rat neocortex, the concept of tripartite synapse only seems to be applicable to a subset of synapses.