Axonal noise as a source of synaptic variability

From neural noise to co-adaptability: Rethinking the multifaceted architecture of motor variability

In the last decade, the source and the functional meaning of motor variability have attracted considerable attention in behavioral and brain sciences. This construct classically combined different levels of description, variable internal robustness or coherence, and multifaceted operational meanings. We provide here a comprehensive review of the literature with the primary aim of building a precise lexicon that goes beyond the generic and monolithic use of motor variability. In the pars destruens of the work, we model three domains of motor variability related to peculiar computational elements that influence fluctuations in motor outputs. Each domain is in turn characterized by multiple sub-domains. We begin with the domains of noise and differentiation. However, the main contribution of our model concerns the domain of adaptability, which refers to variation within the same exact motor representation. In particular, we use the terms learning and (social)fitting to specify the portions of motor variability that depend on our propensity to learn and on our largely constitutive propensity to be influenced by external factors. A particular focus is on motor variability in the context of the sub-domain named co-adaptability. Further groundbreaking challenges arise in the modeling of motor variability. Therefore, in a separate pars construens, we attempt to characterize these challenges, addressing both theoretical and experimental aspects as well as potential clinical implications for neurorehabilitation. All in all, our work suggests that motor variability is neither simply detrimental nor beneficial, and that studying its fluctuations can provide meaningful insights for future research.

Functional imaging of conduction dynamics in cortical and spinal axons

Mammalian axons are specialized for transmitting action potentials to targets within the central and peripheral nervous system. A growing body of evidence suggests that, besides signal conduction, axons play essential roles in neural information processing, and their malfunctions are common hallmarks of neurodegenerative diseases. The technologies available to study axonal function and structure integrally limit the comprehension of axon neurobiology. High-density microelectrode arrays (HD-MEAs) allow for accessing axonal action potentials at high spatiotemporal resolution, but provide no insights on axonal morphology. Here, we demonstrate a method for electrical visualization of axonal morphologies based on extracellular action potentials recorded from cortical and motor neurons using HD-MEAs. The method enabled us to reconstruct up to 5-cm-long axonal arbors and directly monitor axonal conduction across thousands of recording sites. We reconstructed 1.86 m of cortical and spinal axons in total and found specific features in their structure and function.

The computational power of the human brain

At the end of the 20th century, analog systems in computer science have been widely replaced by digital systems due to their higher computing power. Nevertheless, the question keeps being intriguing until now: is the brain analog or digital? Initially, the latter has been favored, considering it as a Turing machine that works like a digital computer. However, more recently, digital and analog processes have been combined to implant human behavior in robots, endowing them with artificial intelligence (AI). Therefore, we think it is timely to compare mathematical models with the biology of computation in the brain. To this end, digital and analog processes clearly identified in cellular and molecular interactions in the Central Nervous System are highlighted. But above that, we try to pinpoint reasons distinguishing in silico computation from salient features of biological computation. First, genuinely analog information processing has been observed in electrical synapses and through gap junctions, the latter both in neurons and astrocytes. Apparently opposed to that, neuronal action potentials (APs) or spikes represent clearly digital events, like the yes/no or 1/0 of a Turing machine. However, spikes are rarely uniform, but can vary in amplitude and widths, which has significant, differential effects on transmitter release at the presynaptic terminal, where notwithstanding the quantal (vesicular) release itself is digital. Conversely, at the dendritic site of the postsynaptic neuron, there are numerous analog events of computation. Moreover, synaptic transmission of information is not only neuronal, but heavily influenced by astrocytes tightly ensheathing the majority of synapses in brain (tripartite synapse). At least at this point, LTP and LTD modifying synaptic plasticity and believed to induce short and long-term memory processes including consolidation (equivalent to RAM and ROM in electronic devices) have to be discussed. The present knowledge of how the brain stores and retrieves memories includes a variety of options (e.g., neuronal network oscillations, engram cells, astrocytic syncytium). Also epigenetic features play crucial roles in memory formation and its consolidation, which necessarily guides to molecular events like gene transcription and translation. In conclusion, brain computation is not only digital or analog, or a combination of both, but encompasses features in parallel, and of higher orders of complexity.

Advances in the study of axon-associated vesicles

The central nervous system is the most important and difficult to study system in the human body and is known for its complex functions, components, and mechanisms. Neurons are the basic cellular units realizing neural functions. In neurons, vesicles are one of the critical pathways for intracellular material transport, linking information exchanges inside and outside cells. The axon is a vital part of neuron since electrical and molecular signals must be conducted through axons. Here, we describe and explore the formation, trafficking, and sorting of cellular vesicles within axons, as well as related-diseases and practical implications. Furthermore, with deepening of understanding and the development of new approaches, accumulating evidence proves that besides signal transmission between synapses, the material exchange and vesicular transmission between axons and extracellular environment are involved in physiological processes, and consequently to neural pathology. Recent studies have also paid attention to axonal vesicles and their physiological roles and pathological effects on axons themselves. Therefore, this review mainly focuses on these two key nodes to explain the role of intracellular vesicles and extracellular vesicles migrated from cells on axons and neurons, providing innovative strategy for future researches.

Analog Transmission of Action Potential Fine Structure in Spiral Ganglion Axons

Action potential waveforms generated at the axon initial segment (AIS) are specialized between and within neuronal classes. But is the fine structure of each electrical event retained when transmitted along myelinated axons or is it rapidly and uniformly transmitted to be modified again at the axon terminal? To address this issue action potential axonal transmission was evaluated in a class of primary sensory afferents that possess numerous types of voltage-gated ion channels underlying a complex repertoire of endogenous firing patterns. In addition to their signature intrinsic electrophysiological heterogeneity, spiral ganglion neurons are uniquely designed. The bipolar, myelinated somata of type I neurons are located within the conduction pathway, requiring that action potentials generated at the first heminode must be conducted through their electrically excitable membrane. We utilized this unusual axonal-like morphology to serve as a window into action potential transmission to compare locally-evoked action potential profiles to those generated peripherally at their glutamatergic synaptic connections with hair cell receptors. These comparisons showed that the distinctively-shaped somatic action potentials were highly correlated with the nodally-generated, invading ones for each neuron. This result indicates that the fine structure of the action potential waveform is maintained axonally, thus supporting the concept that analog signaling is incorporated into each digitally-transmitted action potential in the specialized primary auditory afferents.

Mechanism behind the neuronal ephaptic coupling during synchronizing by specific brain-triggered wave as neuronal motor toolkit

Probable mechanism behind the neuronal ephaptic coupling is investigated based on the introduction of "Brain"-triggered potential excitation signal smartly with a specific very low frequency (VLF) waves as a neuronal motor toolkit. Detection of this electric motor toolkit is attributed to in-vitro precise analyses of a neural network of snail, along to the disconnected snail's neuronal network as a control. This is achieved via rapid (real-time) electrical signals acquisition by blind patch-clamp method during micro-electrode implanting in the neurons at the gigaseal conditions by the surgery operations. This process is based on its waveform (potential excitation signal) detection by mathematical curve fitting process. The characterized waveform of this electrical signal is "Saw Tooth" that is smartly stimulated, alternatively, by the brain during triggering the action potential's (AP's) hyperpolarization zone at a certain time interval at the several µs levels. Triggering the neuron cells results in (1) observing a positive shift (10.0%, depending on the intensity of the triggering wave), and (2) major promotion in the electrical current from sub nano (n) to micro (µ) amper (nA, µA) levels. Direct tracing the time domain (i.e., electrical signal vs. time) and estimation of the frequency domain (diagram of electrical response vs. the applied electrical frequencies) by the "Discrete Fast Fourier Transform" algorithm approve the presence of bilateral and reversible electrical currents between axon and dendrite. This mechanism therefore opens a novel view about the neuronal motor toolkit mechanism, versus the general knowledge about the unilateral electrical current flow from axon to dendrite operations in as neural network. The reliability of this mechanism is evaluated via (1) sequential modulation and demodulation of the snail's neuron network by a simulation electrical functions and sequentially evaluation of the neuronal current sensitivity between pA and nA (during the promotion of the signal-to-noise ratio, via averaging of 30 ± 1 (n = 15) and recycling the electrical cycles before any neuronal response); and (2) operation of the process on the differentiated stem cells. The interstice behavior is attributed to the effective role of Ca2+ channels (besides Na+ and K+ ionic pumping), during hyper/hypo calcium processes, evidenced by inductively coupled plasma as the selected analytical method. This phenomenon is also modeled during proposing quadrupole well potential levels in the neuron systems. This mechanism therefore points to the microprocessor behavior of neuron networks. Stimulation of the neuronal system based on this mechanism, not only controls the sensitivity of neuron electrical stimulation, but also would open a light window for more efficient operating the neuronal connectivity during providing interruptions by phenomena such as neurolysis as well as an efficient treatment of neuron-based disorders.

Acute Effects of Two Different Species of Amyloid-β on Oscillatory Activity and Synaptic Plasticity in the Commissural CA3-CA1 Circuit of the Hippocampus

Recent evidence indicates that soluble amyloid-β (Aβ) species induce imbalances in excitatory and inhibitory transmission, resulting in neural network functional impairment and cognitive deficits during early stages of Alzheimer's disease (AD). To evaluate the in vivo effects of two soluble Aβ species (Aβ_25-35 and Aβ_1-40) on commissural CA3-to-CA1 (cCA3-to-CA1) synaptic transmission and plasticity, and CA1 oscillatory activity, we used acute intrahippocampal microinjections in adult anaesthetized male Wistar rats. Soluble Aβ microinjection increased cCA3-to-CA1 synaptic variability without significant changes in synaptic efficiency. High-frequency CA3 stimulation was rendered inefficient by soluble Aβ intrahippocampal injection to induce long-term potentiation and to enhance synaptic variability in CA1, contrasting with what was observed in vehicle-injected subjects. Although soluble Aβ microinjection significantly increased the relative power of γ-band and ripple oscillations and significantly shifted the average vector of θ-to-γ phase-amplitude coupling (PAC) in CA1, it prevented θ-to-γ PAC shift induced by high-frequency CA3 stimulation, opposite to what was observed in vehicle-injected animals. These results provide further evidence that soluble Aβ species induce synaptic dysfunction causing abnormal synaptic variability, impaired long-term plasticity, and deviant oscillatory activity, leading to network activity derailment in the hippocampus.

Swallowing Pressure Variability as a Function of Pharyngeal Region, Bolus Volume, Age, and Sex

OBJECTIVES

Within-individual movement variability occurs in most motor domains. However, it is unknown how pharyngeal swallowing pressure varies in healthy individuals. We hypothesized that: 1) variability would differ among pharyngeal regions; 2) variability would decrease with increased bolus volume; 3) variability would increase with age; and 4) there would be no sex differences.

STUDY DESIGN

Case series.

METHODS

We used pharyngeal high-resolution manometry to measure swallowing pressure in the following regions: velopharynx, tongue base, hypopharynx, and upper esophageal sphincter. Data were collected from 97 healthy adults (41 male) aged 21 to 89 years during thin liquid swallows: 2 mL, 10 mL, and participant-selected comfortable volume. Pressure variability was measured using coefficient of variation. Repeated measures analysis of variance was used to assess impacts of region, bolus volume, age, and sex on pressure variability.

RESULTS

There was a significant region × volume interaction (P < .001) and significant main effect of age (P = .005). Pressures in the hypopharynx region were more variable than all other regions (P ≤ .028), and pressures in the tongue base region were less variable than all other regions (P ≤ .002) except at 2 mL volumes (P = .065). Swallowing pressure variability was significantly different in the velopharynx and upper esophageal sphincter regions, with comfortable volume and 2 mL swallows having greater variability than 10 mL swallows (P ≤ .026). Pressure variability significantly increased with increasing age (P = .002). There were no effects of sex on pressure variability (P ≥ .15).

CONCLUSION

Pharyngeal swallowing pressure variability differs according pharyngeal region, volume, and age but not sex. Abnormal swallowing pressure variability may reflect deviations in motor control in persons with swallowing impairment, and results from this study can be used as normative data for future investigations evaluating swallowing pressure generation.

LEVEL OF EVIDENCE

4 Laryngoscope, 131:E52-E58, 2021.

Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in the Neocortex

Previous studies based on the 'Quantal Model' for synaptic transmission suggest that neurotransmitter release is mediated by a single release site at individual synaptic contacts in the neocortex. However, recent studies seem to contradict this hypothesis and indicate that multi-vesicular release (MVR) could better explain the synaptic response variability observed in vitro. In this study we present a novel method to estimate the number of release sites per synapse, also known as the size of the readily releasable pool (N_RRP), from paired whole-cell recordings of connections between layer 5 thick tufted pyramidal cell (L5_TTPC) in the juvenile rat somatosensory cortex. Our approach extends the work of Loebel et al. (2009) by leveraging a recently published data-driven biophysical model of neocortical tissue. Using this approach, we estimated N_RRP to be between two to three for synaptic connections between L5_TTPCs. To constrain N_RRP values for other connections in the microcircuit, we developed and validated a generalization approach using published data on the coefficient of variation (CV) of the amplitudes of post-synaptic potentials (PSPs) from literature and comparing them against in silico experiments. Our study predicts that transmitter release at synaptic connections in the neocortex could be mediated by MVR and provides a data-driven approach to constrain the MVR model parameters in the microcircuit.

Axonal Computations

Axons functionally link the somato-dendritic compartment to synaptic terminals. Structurally and functionally diverse, they accomplish a central role in determining the delays and reliability with which neuronal ensembles communicate. By combining their active and passive biophysical properties, they ensure a plethora of physiological computations. In this review, we revisit the biophysics of generation and propagation of electrical signals in the axon and their dynamics. We further place the computational abilities of axons in the context of intracellular and intercellular coupling. We discuss how, by means of sophisticated biophysical mechanisms, axons expand the repertoire of axonal computation, and thereby, of neural computation.

Past and Future of Analog-Digital Modulation of Synaptic Transmission

Action potentials (APs) are generally produced in response to complex summation of excitatory and inhibitory synaptic inputs. While it is usually considered as a digital event, both the amplitude and width of the AP are significantly impacted by the context of its emission. In particular, the analog variations in subthreshold membrane potential determine the spike waveform and subsequently affect synaptic strength, leading to the so-called analog-digital modulation of synaptic transmission. We review here the numerous evidence suggesting context-dependent modulation of spike waveform, the discovery analog-digital modulation of synaptic transmission in invertebrates and its recent validation in mammals. We discuss the potential roles of analog-digital transmission in the physiology of neural networks.

Effects of brain-derived neurotrophic factor and noise on transitions in temporal lobe epilepsy in a hippocampal network

Brain-derived neurotrophic factor (BDNF) has recently been implicated in the modulation of receptor activation leading to dynamic state transitions in temporal lobe epilepsy (TLE). In addition, the crucial role of neuronal noise in these transitions has been studied in electrophysiological experiments. However, the precise role of these factors during seizure generation in TLE is not known. Building on a previously proposed model of an epileptogenic hippocampal network, we included the actions of BDNF-regulated receptors and intrinsic noise. We found that the effects of both BDNF and noise can increase the activation of N-methyl-D-aspartate receptors leading to excessive C a 2 + flux, which induces abnormal fast spiking and bursting. Our results indicate that the combined effects have a strong influence on the seizure-generating network, resulting in higher firing frequency and amplitude. As correlations between firing increase, the synchronization of the entire network increases, a marker of the ictogenic transitions from normal to seizures-like dynamics. Our work on the effects of BDNF dynamics in a noisy environment might lead to an improved model-based understanding of the pathological mechanisms in TLE.

Methods for measuring swallowing pressure variability using high-resolution manometry

Any movement performed repeatedly will be executed with inter-trial variability. Oropharyngeal swallowing is a complex sensorimotor action, and swallow-to-swallow variability can have consequences that impact swallowing safety. Our aim was to determine an appropriate method to measure swallowing pressure waveform variability. An ideal variability metric must be sensitive to known deviations in waveform amplitude, duration, and overall shape, without being biased by waveforms that have both positive and sub-atmospheric pressure profiles. Through systematic analysis of model waveforms, we found a coefficient of variability (CV) parameter on waveforms adjusted such that the overall mean was 0 to be best suited for swallowing pressure variability analysis. We then investigated pharyngeal swallowing pressure variability using high-resolution manometry data from healthy individuals to assess impacts of waveform alignment, pharyngeal region, and number of swallows investigated. The alignment that resulted in the lowest overall swallowing pressure variability was when the superior-most sensor in the upper esophageal sphincter reached half its maximum pressure. Pressures in the tongue base region of the pharynx were least variable and pressures in the hypopharynx region were most variable. Sets of 3 - 10 consecutive swallows had no overall difference in variability, but sets of 2 swallows resulted in significantly less variability than the other dataset sizes. This study identified variability in swallowing pressure waveform shape throughout the pharynx in healthy adults; we discuss implications for swallowing motor control.

From two to three dimensions: The importance of the third dimension for evaluating the limits to neuronal miniaturization in insects

Most studies dealing with the limits to miniaturization in insect brains have until now relied on information based on data collected in two dimensions: either histological sections imaged by light microscopy, or electron micrographs of single ultrathin sections imaged by transmission electron microscopy (TEM). To test the validity of transferring information gained from two-dimensional images to the third dimension, we examined a 3D image stack from serial-section TEM (ssTEM) of the optic neuropiles of the miniature parasitic wasp Trichogramma brassicae (Bezdenko, 1968). We reinvestigated the proposed lower limit of 2 µm for the diameters of neuronal somata and found average volumes of 6.5 μm³ for lamina cells and 3.8 μm³ for medulla cells. We likewise found a limiting factor for the volume of nuclei, which averages 41.9% and 49.2% of the cell body volume, respectively, but that in turn the compactness of heterochromatin was not a limiting factor in the minimal volume of the nuclei. Finally, we also found a minimum axon diameter of 98 nm that could nevertheless accommodate axoplasmic mitochondria. Incorporating the third dimension thus proves critically important in avoiding volumetric misinterpretations of these values. We discuss the limitations of analyzing the effects of miniaturization from profile data of neurons and demonstrate that miniaturization within the nervous system can lie beyond previously described limits and in some cases is already present in the optic lobe neurons of T. brassicae.

Tracking individual action potentials throughout mammalian axonal arbors

Axons are neuronal processes specialized for conduction of action potentials (APs). The timing and temporal precision of APs when they reach each of the synapses are fundamentally important for information processing in the brain. Due to small diameters of axons, direct recording of single AP transmission is challenging. Consequently, most knowledge about axonal conductance derives from modeling studies or indirect measurements. We demonstrate a method to noninvasively and directly record individual APs propagating along millimeter-length axonal arbors in cortical cultures with hundreds of microelectrodes at microsecond temporal resolution. We find that cortical axons conduct single APs with high temporal precision (~100 µs arrival time jitter per mm length) and reliability: in more than 8,000,000 recorded APs, we did not observe any conduction or branch-point failures. Upon high-frequency stimulation at 100 Hz, successive became slower, and their arrival time precision decreased by 20% and 12% for the 100th AP, respectively.

Weak electric fields detectability in a noisy neural network

We investigate the detectability of weak electric field in a noisy neural network based on Izhikevich neuron model systematically. The neural network is composed of excitatory and inhibitory neurons with similar ratio as that in the mammalian neocortex, and the axonal conduction delays between neurons are also considered. It is found that the noise intensity can modulate the detectability of weak electric field. Stochastic resonance (SR) phenomenon induced by white noise is observed when the weak electric field is added to the network. It is interesting that SR almost disappeared when the connections between neurons are cancelled, suggesting the amplification effects of the neural coupling on the synchronization of neuronal spiking. Furthermore, the network parameters, such as the connection probability, the synaptic coupling strength, the scale of neuron population and the neuron heterogeneity, can also affect the detectability of the weak electric field. Finally, the model sensitivity is studied in detail, and results show that the neural network model has an optimal region for the detectability of weak electric field signal.

Differential frequency-dependent antidromic resonance of the Schaffer collaterals and mossy fibers

To better understand information transfer along the hippocampal pathways and its plasticity, here we studied the antidromic responses of the dentate gyrus (DG) and CA3 to activation of the mossy fibers and Schaffer collaterals, respectively, in hippocampal slices from naïve and epileptic rats. We applied trains of 600 electrical stimuli at functionally meaningful frequencies (θ, β/γ and γ). The responses of the DG to θ frequency trains underwent rapid potentiation that lasted about 400 stimuli, after which they progressively returned to control value. At β/γ and γ frequencies, however, the initial potentiation was followed by a strong frequency-dependent depression within the first 50 stimuli. In kindled animals, the initial potentiation was stronger than in control preparations and the resonant phase at θ frequency lasted longer. In contrast, CA3 responses were exponentially depressed at all frequencies, but depression was significantly less intense at θ frequency in epileptic preparations. Failure of fibers to fire action potentials could account for some of the aforementioned characteristics, but waveforms of the intracellular action potentials also changed as the field responses did, i.e., half-duration and time-to-peak increased in both structures along the stimulation trains. Noteworthy, block of glutamate and GABA ionotropic receptors prevented resonance and reduced the depression of antidromic responses to β/γ and γ stimulation recorded in the DG, but not in CA3. We show that the different behavior in the information transfer along these pathways depends on the frequency at which action potentials are generated, excitability history and anatomical features, including myelination and tortuosity. In addition, the mossy fibers are endowed with ionotropic receptors and terminal active properties conferring them their sui generis non-passive antidromic responses.

Volterra dendritic stimulus processors and biophysical spike generators with intrinsic noise sources

We consider a class of neural circuit models with internal noise sources arising in sensory systems. The basic neuron model in these circuits consists of a dendritic stimulus processor (DSP) cascaded with a biophysical spike generator (BSG). The dendritic stimulus processor is modeled as a set of nonlinear operators that are assumed to have a Volterra series representation. Biophysical point neuron models, such as the Hodgkin-Huxley neuron, are used to model the spike generator. We address the question of how intrinsic noise sources affect the precision in encoding and decoding of sensory stimuli and the functional identification of its sensory circuits. We investigate two intrinsic noise sources arising (i) in the active dendritic trees underlying the DSPs, and (ii) in the ion channels of the BSGs. Noise in dendritic stimulus processing arises from a combined effect of variability in synaptic transmission and dendritic interactions. Channel noise arises in the BSGs due to the fluctuation of the number of the active ion channels. Using a stochastic differential equations formalism we show that encoding with a neuron model consisting of a nonlinear DSP cascaded with a BSG with intrinsic noise sources can be treated as generalized sampling with noisy measurements. For single-input multi-output neural circuit models with feedforward, feedback and cross-feedback DSPs cascaded with BSGs we theoretically analyze the effect of noise sources on stimulus decoding. Building on a key duality property, the effect of noise parameters on the precision of the functional identification of the complete neural circuit with DSP/BSG neuron models is given. We demonstrate through extensive simulations the effects of noise on encoding stimuli with circuits that include neuron models that are akin to those commonly seen in sensory systems, e.g., complex cells in V1.