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We compare the circuit and cellular mechanisms for homeostatic plasticity that have been discovered in rodent somatosensory (S1) and visual (V1) cortex. Both areas use similar mechanisms to restore mean firing rate after sensory deprivation. Two time scales of homeostasis are evident, with distinct mechanisms. Slow homeostasis occurs over several days, and is mediated by homeostatic synaptic scaling in excitatory networks and, in some cases, homeostatic adjustment of pyramidal cell intrinsic excitability. Fast homeostasis occurs within less than 1 day, and is mediated by rapid disinhibition, implemented by activity-dependent plasticity in parvalbumin interneuron circuits. These processes interact with Hebbian synaptic plasticity to maintain cortical firing rates during learned adjustments in sensory representations. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

Spiking activity of place cells in the hippocampus encodes the animal’s position as it moves through an environment. Within a cell’s place field, both the firing rate and the phase of spiking in the local theta oscillation contain spatial information. We propose a position–theta-phase (PTP) model that captures the simultaneous expression of the firing-rate code and theta-phase code in place cell spiking. This model parametrically characterizes place fields to compare across cells, time, and conditions; generates realistic place cell simulation data; and conceptualizes a framework for principled hypothesis testing to identify additional features of place cell activity. We use the PTP model to assess the effect of running speed in place cell data recorded from rats running on linear tracks. For the majority of place fields, we do not find evidence for speed modulation of the firing rate. For a small subset of place fields, we find firing rates significantly increase or decrease with speed. We use the PTP model to compare candidate mechanisms of speed modulation in significantly modulated fields and determine that speed acts as a gain control on the magnitude of firing rate. Our model provides a tool that connects rigorous analysis with a computational framework for understanding place cell activity.

A fundamental inequality governing the spike activity of peripheral neurons is derived and tested against auditory data. This inequality states that the steady-state firing rate must lie between the arithmetic and geometric means of the spontaneous and peak activities during adaptation. Implications towards the development of auditory mechanistic models are explored.

Regulation of firing rate homeostasis constitutes a fundamental property of central neural circuits. While intracellular Ca2+ has long been hypothesized to be a feedback control signal, the molecular machinery enabling a network-wide homeostatic response remains largely unknown. We show that deletion of insulin-like growth factor-1 receptor (IGF-1R) limits firing rate homeostasis in response to inactivity, without altering the distribution of baseline firing rates. The deficient firing rate homeostatic response was due to disruption of both postsynaptic and intrinsic plasticity. At the cellular level, we detected a fraction of IGF-1Rs in mitochondria, colocalized with the mitochondrial calcium uniporter complex (MCUc). IGF-1R deletion suppressed transcription of the MCUc members and burst-evoked mitochondrial Ca2+ (mitoCa2+) by weakening mitochondria-to-cytosol Ca2+ coupling. Overexpression of either mitochondriatargeted IGF-1R or MCUc in IGF-1R—deficient neurons was sufficient to rescue the deficits in burst-to-mitoCa2+ coupling and firing rate homeostasis. Our findings indicate that mitochondrial IGF-1R is a key regulator of the integrated homeostatic response by tuning the reliability of burst transfer by MCUc. Based on these results, we propose that MCUc acts as a homeostatic Ca2+ sensor. Faulty activation of MCUc may drive dysregulation of firing rate homeostasis in aging and in brain disorders associated with aberrant IGF-1R/MCUc signaling.

Spiking neural networks (SNNs) are well-suited to process asynchronous event-based data. Most of the existing SNNs use rate-coding schemes that focus on firing rate (FR), and so they generally ignore the spike timing in events. On the contrary, methods based on temporal coding, particularly time-to-first-spike (TTFS) coding, can be accurate and efficient but they are difficult to train. Currently, there is limited research on applying TTFS coding to real events, since traditional TTFS-based methods impose one-spike constraint, which is not realistic for event-based data. In this study, we present a novel decision-making strategy based on first-spike (FS) coding that encodes FS timings of the output neurons to investigate the role of the first-spike timing in classifying real-world event sequences with complex temporal structures. To achieve FS coding, we propose a novel surrogate gradient learning method for discrete spike trains. In the forward pass, output spikes are encoded into discrete times to generate FS times. In the backpropagation, we develop an error assignment method that propagates error from FS times to spikes through a Gaussian window, and then supervised learning for spikes is implemented through a surrogate gradient approach. Additional strategies are introduced to facilitate the training of FS timings, such as adding empty sequences and employing different parameters for different layers. We make a comprehensive comparison between FS and FR coding in the experiments. Our results show that FS coding achieves comparable accuracy to FR coding while leading to superior energy efficiency and distinct neuronal dynamics on data sequences with very rich temporal structures. Additionally, a longer time delay in the first spike leads to higher accuracy, indicating important information is encoded in the timing of the first spike.

Networks in the spinal cord, which are responsible for the generation of rhythmic movements, commonly known as central pattern generators (CPGs), have remained elusive for decades. Although it is well-known that many spinal neurons are rhythmically active, little attention has been given to the distribution of firing rates across the population. Here, we argue that firing rate distributions can provide an important clue to the organization of the CPGs. The data that can be gleaned from the sparse literature indicate a firing rate distribution, which is skewed toward zero with a long tail, akin to a normal distribution on a log-scale, i.e., a “log-normal” distribution. Importantly, such a shape is difficult to unite with the widespread assumption of modules composed of recurrently connected excitatory neurons. Spinal modules with recurrent excitation has the propensity to quickly escalate their firing rate and reach the maximum, hence equalizing the spiking activity across the population. The population distribution of firing rates hence would consist of a narrow peak near the maximum. This is incompatible with experiments, that show wide distributions and a peak close to zero. A way to resolve this puzzle is to include recurrent inhibition internally in each CPG modules. Hence, we investigate the impact of recurrent inhibition in a model and find that the firing rate distributions are closer to the experimentally observed. We therefore propose that recurrent inhibition is a crucial element in motor circuits, and suggest that future models of motor circuits should include recurrent inhibition as a mandatory element.

We examined sex‐specific changes to neuromuscular function in response to mental fatigue. Twenty‐five young, healthy adults (13 F, 12 M) performed a mentally fatiguing task and control condition for 30 min on two separate days. Neuromuscular function was assessed in the first dorsal interosseous before and after each condition. Reaction time decreased after the mentally fatiguing task (P < 0.001, η2 = 0.47). Males and females reported higher levels of subjective fatigue after the mentally fatiguing task (P < 0.02, η2 = 0.07). Motor unit firing rate increased over time at 10% maximal voluntary contraction (MVC; P < 0.04, η2 = 0.16), and decreased over time at 50% MVC (P < 0.01, η2 = 0.14); however, this was not unique to either sex. During a variable force contraction, error decreased in females over time and increased in males (P < 0.05, η2 = 0.13), although changes were not unique to mental fatigue. Physiological function of the neuromuscular system was not specifically affected by mental fatigue in males or females. What is the central question of this study? What is the effect of mental fatigue on neuromuscular behaviour in the first dorsal interosseus? What is the main finding and its importance? Mental fatigue does not have a significant effect on neuromuscular function in the first dorsal interosseus. This evidence supports previous evidence observed in the tibialis anterior that there is no significant influence on, nor sex‐specific changes to, neuromuscular function after a mentally fatiguing condition.

In order to advance the development of sensors fabricated with monofunctional sensation systems capable of a versatile response to tactile, thermal, gustatory, olfactory, and auditory sensations, mechanoreceptors fabricated as a single platform with an electric circuit require investigation. In addition, it is essential to resolve the complicated structure of the sensor. In order to realize the single platform, our proposed hybrid fluid (HF) rubber mechanoreceptors of free nerve endings, Merkel cells, Krause end bulbs, Meissner corpuscles, Ruffini endings, and Pacinian corpuscles mimicking the bio-inspired five senses are useful enough to facilitate the fabrication process for the resolution of the complicated structure. This study used electrochemical impedance spectroscopy (EIS) to elucidate the intrinsic structure of the single platform and the physical mechanisms of the firing rate such as slow adaption (SA) and fast adaption (FA), which were induced from the structure and involved the capacitance, inductance, reactance, etc. of the HF rubber mechanoreceptors. In addition, the relations among the firing rates of the various sensations were clarified. The adaption of the firing rate in the thermal sensation is the opposite of that in the tactile sensation. The firing rates in the gustation, olfaction, and auditory sensations at frequencies of less than 1 kHz have the same adaption as in the tactile sensation. The present findings are useful not only in the field of neurophysiology, to research the biochemical reactions of neurons and brain perceptions of stimuli, but also in the field of sensors, to advance salient developments in sensors mimicking bio-inspired sensations.

Firing rate models for describing the mean-field activities of neuronal ensembles can be used effectively to study network function and dynamics, including synchronization and rhythmicity of excitatory-inhibitory populations. However, traditional Wilson-Cowan-like models, even when extended to include an explicit dynamic synaptic activation variable, are found unable to capture some dynamics such as Interneuronal Network Gamma oscillations (ING). Use of an explicit delay is helpful in simulations at the expense of complicating mathematical analysis. We resolve this issue by introducing a dynamic variable, u, that acts as an effective delay in the negative feedback loop between firing rate (r) and synaptic gating of inhibition (s). In effect, u endows synaptic activation with second order dynamics. With linear stability analysis, numerical branch-tracking and simulations, we show that our r-u-s rate model captures some key qualitative features of spiking network models for ING. We also propose an alternative formulation, a v-u-s model, in which mean membrane potential v satisfies an averaged current-balance equation. Furthermore, we extend the framework to E-I networks. With our six-variable v-u-s model, we demonstrate in firing rate models the transition from Pyramidal-Interneuronal Network Gamma (PING) to ING by increasing the external drive to the inhibitory population without adjusting synaptic weights. Having PING and ING available in a single network, without invoking synaptic blockers, is plausible and natural for explaining the emergence and transition of two different types of gamma oscillations.

Approaches for validating motor unit firing times following surface electromyographic (EMG) signal decomposition with the precision decomposition III (PDIII) algorithm have not been agreed upon. Two approaches have been common: (1) “reconstruct-and-test” and (2) spike-triggered averaging (STA). We sought to compare motor unit results following the application of these approaches. Surface EMG signals were recorded from the vastus lateralis of 13 young males performing trapezoidal, isometric knee extensions at 50% and 80% of maximum voluntary contraction (MVC) force. The PDIII algorithm was used to quantify motor unit firing rates. Motor units were excluded using eight combinations of the reconstruct-and-test approach with accuracy thresholds of 0, 90, 91, and 92% with and without STA. The mean firing rate versus recruitment threshold relationship was minimally affected by STA. At 80% MVC, slopes acquired at the 0% accuracy threshold were significantly greater (i.e., less negative) than when 91% (p = .010) and 92% (p = .030) accuracy thresholds were applied. The application of STA has minimal influence on surface EMG signal decomposition results. Stringent reconstruct-and-test accuracy thresholds influence motor unit-derived relationships at high forces, perhaps explained through the increased presence of large motor unit action potentials. Investigators using the PDIII algorithm can expect negligible changes in motor unit-derived linear regression relationships with the application of secondary validation procedures.