Firing Rate Research Articles

Epilepsy-associated Kv1.1 channel subunits regulate intrinsic cardiac pacemaking in mice.

The heartbeat originates from spontaneous action potentials in specialized pacemaker cells within the sinoatrial node (SAN) of the right atrium. Voltage-gated potassium channels in SAN myocytes mediate outward K+ currents that regulate cardiac pacemaking by controlling action potential repolarization, influencing the time between heartbeats. Gene expression studies have identified transcripts for many types of voltage-gated potassium channels in the SAN, but most remain of unknown functional significance. One such gene is Kcna1, which encodes epilepsy-associated voltage-gated Kv1.1 K+ channel α-subunits that are important for regulating action potential firing in neurons and cardiomyocytes. Here, we investigated the functional contribution of Kv1.1 to cardiac pacemaking at the whole heart, SAN, and SAN myocyte levels by performing Langendorff-perfused isolated heart preparations, multielectrode array recordings, patch clamp electrophysiology, and immunocytochemistry using Kcna1 knockout (KO) and wild-type (WT) mice. Our results showed that either genetic or pharmacological ablation of Kv1.1 significantly decreased the SAN firing rate, primarily by impairing SAN myocyte action potential repolarization. Voltage-clamp electrophysiology and immunocytochemistry revealed that Kv1.1 exerts its effects despite contributing only a small outward K+ current component, which we term IKv1.1, and despite apparently being present in low abundance at the protein level in SAN myocytes. These findings establish Kv1.1 as the first identified member of the Kv1 channel family to play a role in sinoatrial function, thereby rendering it a potential candidate and therapeutic targeting of sinus node dysfunction. Furthermore, our results demonstrate that small currents generated via low-abundance channels can still have significant impacts on cardiac pacemaking.

Firing rate adaptation affords place cell theta sweeps, phase precession, and procession

Hippocampal place cells in freely moving rodents display both theta phase precession and procession, which is thought to play important roles in cognition, but the neural mechanism for producing theta phase shift remains largely unknown. Here, we show that firing rate adaptation within a continuous attractor neural network causes the neural activity bump to oscillate around the external input, resembling theta sweeps of decoded position during locomotion. These forward and backward sweeps naturally account for theta phase precession and procession of individual neurons, respectively. By tuning the adaptation strength, our model explains the difference between ‘bimodal cells’ showing interleaved phase precession and procession, and ‘unimodal cells’ in which phase precession predominates. Our model also explains the constant cycling of theta sweeps along different arms in a T-maze environment, the speed modulation of place cells’ firing frequency, and the continued phase shift after transient silencing of the hippocampus. We hope that this study will aid an understanding of the neural mechanism supporting theta phase coding in the brain.

Firing rate adaptation affords place cell theta sweeps, phase precession, and procession.

Hippocampal place cells in freely moving rodents display both theta phase precession and procession, which is thought to play important roles in cognition, but the neural mechanism for producing theta phase shift remains largely unknown. Here, we show that firing rate adaptation within a continuous attractor neural network causes the neural activity bump to oscillate around the external input, resembling theta sweeps of decoded position during locomotion. These forward and backward sweeps naturally account for theta phase precession and procession of individual neurons, respectively. By tuning the adaptation strength, our model explains the difference between 'bimodal cells' showing interleaved phase precession and procession, and 'unimodal cells' in which phase precession predominates. Our model also explains the constant cycling of theta sweeps along different arms in a T-maze environment, the speed modulation of place cells' firing frequency, and the continued phase shift after transient silencing of the hippocampus. We hope that this study will aid an understanding of the neural mechanism supporting theta phase coding in the brain.

Self-avoidance dominates the selection of hippocampal replay.

Spontaneous neural activity sequences are generated by the brain in the absence of external input 1-12 , yet how they are produced remains unknown. During immobility, hippocampal replay sequences depict spatial paths related to the animal's past experience or predicted future 13 . By recording from large ensembles of hippocampal place cells 14 in combination with optogenetic manipulation of cortical input in freely behaving rats, we show here that the selection of hippocampal replay is governed by a novel self-avoidance principle. Following movement cessation, replay of the animal's past path is strongly avoided, while replay of the future path predominates. Moreover, when the past and future paths overlap, early replays avoid both and depict entirely different trajectories. Further, replays avoid self-repetition, on a shorter timescale compared to the avoidance of previous behavioral trajectories. Eventually, several seconds into the stopping period, replay of the past trajectory dominates. This temporal organization contrasts with established and recent predictions 9,10,15,16 but is well-recapitulated by a symmetry-breaking attractor model of sequence generation in which individual neurons adapt their firing rates over time 26-35 . However, while the model is sufficient to produce avoidance of recently traversed or reactivated paths, it requires an additional excitatory input into recently activated cells to produce the later window of past-dominance. We performed optogenetic perturbations to demonstrate that this input is provided by medial entorhinal cortex, revealing its role in maintaining a memory of past experience that biases hippocampal replay. Together, these data provide specific evidence for how hippocampal replays are generated.

Electrophysiological activity pattern of mouse hippocampal CA1 and dentate gyrus under isoflurane anesthesia.

General anesthesia can impact a patient's memory and cognition by influencing hippocampal function. The CA1 and dentate gyrus (DG), serving as the primary efferent and gateway of the hippocampal trisynaptic circuit facilitating cognitive learning and memory functions, exhibit significant differences in cellular composition, molecular makeup, and responses to various stimuli. However, the effects of isoflurane-induced general anesthesia on CA1 and DG neuronal activity in mice are not well understood. In this study, utilizing electrophysiological recordings, we examined neuronal population dynamics and single-unit activity (SUA) of CA1 and DG in freely behaving mice during natural sleep and general anesthesia. Our findings reveal that isoflurane anesthesia shifts local field potential (LFP) to delta frequency and reduces the firing rate of SUA in both CA1 and DG, compared to wakefulness. Additionally, the firing rates of DG neurons are significantly lower than CA1 neurons during isoflurane anesthesia, and the recovery of theta power is slower in DG than in CA1 during the transition from anesthesia to wakefulness, indicating a stronger and more prolonged impact of isoflurane anesthesia on DG. This work presents a suitable approach for studying brain activities during general anesthesia and provides evidence for distinct effects of isoflurane anesthesia on hippocampal subregions.

Tropospheric ozone effect on olfactory perception and olfactory bulb dopaminergic interneuron excitability

Ozone (O3) forms in the Earth’s atmosphere, both naturally and by reactions of man-made air pollutants. Deleterious effects of O3 have been found in the respiratory system. Here, we examine whether O3 alters olfactory behavior and cellular properties in the olfactory system. For this purpose, mice were exposed to O3 at a concentration found in highly polluted city air [0.8 ppm], and the behavior elicited by social and non-social odors in habituation/dishabituation tests was assessed. In addition, the electrical responses of dopaminergic olfactory bulb (OB) neurons were also evaluated. O3 differentially compromises olfactory perception to odors: it reduces responses to social and non-social odors in Swiss Webster mice, while this effect was observed in C57BL/6 J mice only for some non-social odors. Additionally, O3 reduced the rate of spontaneous spike firing in periglomerular dopaminergic cells (PG-DA) of the OB. Because this effect could reflect changes in excitability and/or synaptic inputs, the ability of O3 to alter PG-DA spontaneous activity was also tested together with cell membrane resistance, membrane potential, rheobase and chronaxie. Taken together, our data suggest the ability of O3 to affect olfactory perception.

Pulsatile electrical stimulation creates predictable, correctable disruptions in neural firing

Electrical stimulation is a key tool in neuroscience, both in brain mapping studies and in many therapeutic applications such as cochlear, vestibular, and retinal neural implants. Due to safety considerations, stimulation is restricted to short biphasic pulses. Despite decades of research and development, neural implants lead to varying restoration of function in patients. In this study, we use computational modeling to provide an explanation for how pulsatile stimulation affects axonal channels and therefore leads to variability in restoration of neural responses. The phenomenological explanation is transformed into equations that predict induced firing rate as a function of pulse rate, pulse amplitude, and spontaneous firing rate. We show that these equations predict simulated responses to pulsatile stimulation with a variety of parameters as well as several features of experimentally recorded primate vestibular afferent responses to pulsatile stimulation. We then discuss the implications of these effects for improving clinical stimulation paradigms and electrical stimulation-based experiments.

A Wearable Dual-Mode Probe for Image-Guided Closed-Loop Ultrasound Neuromodulation.

Low-intensity focused ultrasound (FUS) is an emerging non-invasive and spatially/temporally precise method for modulating the firing rates and patterns of peripheral nerves. This paper describes an image-guided platform for chronic and patient-specific FUS neuromodulation. The system uses custom wearable probes containing separate ultrasound imaging and modulation transducer arrays realized using piezoelectric transducers assembled on a flexible printed circuit board (PCB). Dual-mode probes operating around 4 MHz (imaging) and 1.3 MHz (modulation) were fabricated and tested on tissue phantoms. The resulting B-mode images were analyzed using a template-matching algorithm to estimate the location of the target nerve and then direct the modulation beam toward the target. The ultrasound transmit voltage used to excite the modulation array was optimized in real-time by automatically regulating functional feedback signals (the average rates of emulated muscle twitches detected by an on-board motion sensor) through a proportional and integral (PI) controller, thus providing robustness to inter-subject variability and probe positioning errors. The proposed closed-loop neuromodulation paradigm was experimentally demonstrated in vitro using an active tissue phantom that integrates models of the posterior tibial nerve and nearby blood vessels together with embedded sensors and actuators.

Initial and corrective submovement encoding differences within primary motor cortex during precision reaching.

Precision reaching often requires corrective submovements to obtain the desired goal. Most studies of reaching have focused on single initial movements, and implied the cortical encoding model was the same for all submovements. However, corrective submovements may show different encoding patterns from the initial submovement with distinct patterns of activation across the population. Two rhesus macaques performed a precision center-out-task with small targets. Neural activity from single units in the primary motor cortex and associated behavioral data were recorded to evaluate movement characteristics. Neural population data and individual neuronal firing rates identified with a peak finding algorithm to identify peaks in hand speed were examined for encoding differences between initial and corrective submovements. Individual neurons were fitted with a regression model that included the reach vector, position, and speed to predict firing rate. For both initial and corrective submovements, the largest effect remained movement direction. We observed a large subset changed their preferred direction greater than 45° between initial and corrective submovements. Neuronal depth of modulation also showed considerable variation when adjusted for movement speed. By using principal component analysis, neural trajectories of initial and corrective submovements progressed through different neural subspaces. These findings all suggest that different neural encoding patterns exist for initial and corrective submovements within the cortex. We hypothesize that this variation in how neurons change to encode small, corrective submovements might allow for a larger portion of the neural space being used to encode a greater range of movements with varying amplitudes and levels of precision.NEW & NOTEWORTHY Neuronal recordings matched with kinematic behavior were collected in a precision center-out task that often required corrective movements. We reveal large differences in preferred direction and depth of modulation between initial and corrective submovements across the neural population. We then present a model of the neural population describing how these shifts in tuning create different subspaces for signaling initial and corrective movements likely to improve motor precision.

Synaptic reorganization of synchronized neuronal networks with synaptic weight and structural plasticity.

Abnormally strong neural synchronization may impair brain function, as observed in several brain disorders. We computationally study how neuronal dynamics, synaptic weights, and network structure co-emerge, in particular, during (de)synchronization processes and how they are affected by external perturbation. To investigate the impact of different types of plasticity mechanisms, we combine a network of excitatory integrate-and-fire neurons with different synaptic weight and/or structural plasticity mechanisms: (i) only spike-timing-dependent plasticity (STDP), (ii) only homeostatic structural plasticity (hSP), i.e., without weight-dependent pruning and without STDP, (iii) a combination of STDP and hSP, i.e., without weight-dependent pruning, and (iv) a combination of STDP and structural plasticity (SP) that includes hSP and weight-dependent pruning. To accommodate the diverse time scales of neuronal firing, STDP, and SP, we introduce a simple stochastic SP model, enabling detailed numerical analyses. With tools from network theory, we reveal that structural reorganization may remarkably enhance the network's level of synchrony. When weaker contacts are preferentially eliminated by weight-dependent pruning, synchrony is achieved with significantly sparser connections than in randomly structured networks in the STDP-only model. In particular, the strengthening of contacts from neurons with higher natural firing rates to those with lower rates and the weakening of contacts in the opposite direction, followed by selective removal of weak contacts, allows for strong synchrony with fewer connections. This activity-led network reorganization results in the emergence of degree-frequency, degree-degree correlations, and a mixture of degree assortativity. We compare the stimulation-induced desynchronization of synchronized states in the STDP-only model (i) with the desynchronization of models (iii) and (iv). The latter require stimuli of significantly higher intensity to achieve long-term desynchronization. These findings may inform future pre-clinical and clinical studies with invasive or non-invasive stimulus modalities aiming at inducing long-lasting relief of symptoms, e.g., in Parkinson's disease.

Neural Circuit Mechanisms of Sinisan formula for the Treatment of adolescent Depression: prefrontal cortex to dorsal raphe nucleus

Ethnopharmacological relevanceSinisan formula (SNSF), documented in the classic books Shanghan Lun, is known for its ability to regulate liver-qi and treat depression. However, its underlying mechanism, particularly its effects on dynamic real-time neuron activity and circuits remains to be fully elucidated. Aim of the studyThis study aimed to investigate the antidepressant effect of SNSF and its central nervous system mechanism on depression-like behaviors, focusing on the prefrontal cortex (PFC) to dorsal raphe nucleus (DRN) neural circuit in a stress-induced adolescent animal model. Materials and methodsSNSF comprised four herbs, the root of Bupleurum chinense DC., the root of Paeonia lactiflora Pall., the fruit of Citrus aurantium L., the rhizome of Glycyrrhiza uralensis Fisch., in equal propotions. The adolescent depression animal model was induced by maternal separation (MS) and chronic restraint stress (CRS). In-vivo multichannel physiological electrodes were implanted into the PFC on PND 28 and animals were recorded 5 times during PND 35–46. From PND 47, the behavioral tests were performed to evaluate the antidepressant efficacy of SNSF. Subsequently, brain tissue was collected for Western blot and immunofluorescence staining analysis. Retro virus was injected into the DRN to explore sources of projections received by serotonergic (5-HTergic) neurons. And the PFC-to-DRN circuit was activated or inhibited through chemogenetic techniques to investigate the effects of SNSF on depression-like behaviors. ResultsAdministration of SNSF for 18 days effectively alleviated depression-like behaviors in MS&CRS adolescent mice. The PFC emerged as the primary glutamatergic projection source of the DRN5−HT neurons. Following SNSF administration for 13/15/18 days, there was an increase in the firing rate of excitatory neurons and excitatory/inhibitory (E/I) ratio in the PFC. MS&CRS stress let to a reduction in the density of 5-HT+ and CaMKII + neurons in the DRN, accompanied by an increase in the density of GAD + neurons in the DRN, while SNSF administration reversed the alterations. Chemogenetic activation of the PFC-to-DRN circuit rescued the depression-like behaviors induced by MS&CRS, whereas suppression of this circuit attenuated the antidepressant effect of SNSF. ConclusionsSNSF significantly mitigated depression-like behaviors in MS&CRS mice. SNSF exerts its antidepressant effects by increasing the E/I ratio in the PFC and enhancing glutamatergic projections from the PFC to the DRN.

Incorporating slow NMDA-type receptors with nonlinear voltage-dependent magnesium block in a next generation neural mass model: derivation and dynamics

We derive a next generation neural mass model of a population of quadratic-integrate-and-fire neurons, with slow adaptation, and conductance-based AMPAR, GABAR and nonlinear NMDAR synapses. We show that the Lorentzian ansatz assumption can be satisfied by introducing a piece-wise polynomial approximation of the nonlinear voltage-dependent magnesium block of NMDAR current. We study the dynamics of the resulting system for two example cases of excitatory cortical neurons and inhibitory striatal neurons. Bifurcation diagrams are presented comparing the different dynamical regimes as compared to the case of linear NMDAR currents, along with sample comparison simulation time series demonstrating different possible oscillatory solutions. The omission of the nonlinearity of NMDAR currents results in a shift in the range (and possible disappearance) of the constant high firing rate regime, along with a modulation in the amplitude and frequency power spectrum of oscillations. Moreover, nonlinear NMDAR action is seen to be state-dependent and can have opposite effects depending on the type of neurons involved and the level of input firing rate received. The presented model can serve as a computationally efficient building block in whole brain network models for investigating the differential modulation of different types of synapses under neuromodulatory influence or receptor specific malfunction.

Dentate gyrus granule cells are a locus of pathology in Scn8a developmental encephalopathy

Gain-of-function mutations in SCN8A cause developmental and epileptic encephalopathy (DEE), a disorder characterized by early-onset refractory seizures, deficits in motor and intellectual functions, and increased risk of sudden unexpected death in epilepsy. Altered activity of neurons in the corticohippocampal circuit has been reported in mouse models of DEE. We examined the effect of chronic seizures on gene expression in the hippocampus by single-nucleus RNA sequencing in mice expressing the patient mutation SCN8A-p.Asn1768Asp (N1768D). One hundred and eighty four differentially expressed genes were identified in dentate gyrus granule cells, many more than in other cell types. Electrophysiological recording from dentate gyrus granule cells demonstrated an elevated firing rate. Targeted reduction of Scn8a expression in the dentate gyrus by viral delivery of an shRNA resulted in doubling of median survival time from 4 months to 8 months, whereas delivery of shRNA to the CA1 and CA3 regions did not result in lengthened survival. These data indicate that granule cells of the dentate gyrus are a specific locus of pathology in SCN8A-DEE.

Systemic pharmacological suppression of neural activity reverses learning impairment in a mouse model of Fragile X syndrome

The enhancement of associative synaptic plasticity often results in impaired rather than enhanced learning. Previously, we proposed that such learning impairments can result from saturation of the plasticity mechanism (Nguyen-Vu et al., 2017), or, more generally, from a history-dependent change in the threshold for plasticity. This hypothesis was based on experimental results from mice lacking two class I major histocompatibility molecules, MHCI H2-Kb and H2-Db (MHCI KbDb−/−), which have enhanced associative long-term depression at the parallel fiber-Purkinje cell synapses in the cerebellum (PF-Purkinje cell LTD). Here, we extend this work by testing predictions of the threshold metaplasticity hypothesis in a second mouse line with enhanced PF-Purkinje cell LTD, the Fmr1 knockout mouse model of Fragile X syndrome (FXS). Mice lacking Fmr1 gene expression in cerebellar Purkinje cells (L7-Fmr1 KO) were selectively impaired on two oculomotor learning tasks in which PF-Purkinje cell LTD has been implicated, with no impairment on LTD-independent oculomotor learning tasks. Consistent with the threshold metaplasticity hypothesis, behavioral pre-training designed to reverse LTD at the PF-Purkinje cell synapses eliminated the oculomotor learning deficit in the L7-Fmr1 KO mice, as previously reported in MHCI KbDb−/−mice. In addition, diazepam treatment to suppress neural activity and thereby limit the induction of associative LTD during the pre-training period also eliminated the learning deficits in L7-Fmr1 KO mice. These results support the hypothesis that cerebellar LTD-dependent learning is governed by an experience-dependent sliding threshold for plasticity. An increased threshold for LTD in response to elevated neural activity would tend to oppose firing rate stability, but could serve to stabilize synaptic weights and recently acquired memories. The metaplasticity perspective could inform the development of new clinical approaches for addressing learning impairments in autism and other disorders of the nervous system.

Systemic pharmacological suppression of neural activity reverses learning impairment in a mouse model of Fragile X syndrome.

The enhancement of associative synaptic plasticity often results in impaired rather than enhanced learning. Previously, we proposed that such learning impairments can result from saturation of the plasticity mechanism (Nguyen-Vu et al., 2017), or, more generally, from a history-dependent change in the threshold for plasticity. This hypothesis was based on experimental results from mice lacking two class I major histocompatibility molecules, MHCI H2-Kb and H2-Db (MHCI KbDb-/-), which have enhanced associative long-term depression at the parallel fiber-Purkinje cell synapses in the cerebellum (PF-Purkinje cell LTD). Here, we extend this work by testing predictions of the threshold metaplasticity hypothesis in a second mouse line with enhanced PF-Purkinje cell LTD, the Fmr1 knockout mouse model of Fragile X syndrome (FXS). Mice lacking Fmr1 gene expression in cerebellar Purkinje cells (L7-Fmr1 KO) were selectively impaired on two oculomotor learning tasks in which PF-Purkinje cell LTD has been implicated, with no impairment on LTD-independent oculomotor learning tasks. Consistent with the threshold metaplasticity hypothesis, behavioral pre-training designed to reverse LTD at the PF-Purkinje cell synapses eliminated the oculomotor learning deficit in the L7-Fmr1 KO mice, as previously reported in MHCI KbDb-/-mice. In addition, diazepam treatment to suppress neural activity and thereby limit the induction of associative LTD during the pre-training period also eliminated the learning deficits in L7-Fmr1 KO mice. These results support the hypothesis that cerebellar LTD-dependent learning is governed by an experience-dependent sliding threshold for plasticity. An increased threshold for LTD in response to elevated neural activity would tend to oppose firing rate stability, but could serve to stabilize synaptic weights and recently acquired memories. The metaplasticity perspective could inform the development of new clinical approaches for addressing learning impairments in autism and other disorders of the nervous system.

Impact of Unitary Synaptic Inhibition on Spike Timing in Ventral Tegmental Area Dopamine Neurons.

Midbrain dopamine neurons receive convergent synaptic input from multiple brain areas, which perturbs rhythmic pacemaking to produce the complex firing patterns observed in vivo. This study investigated the impact of single and multiple inhibitory inputs on ventral tegmental area (VTA) dopamine neuron firing in mice of both sexes using novel experimental measurements and modeling. We first measured unitary inhibitory postsynaptic currents produced by single axons using both minimal electrical stimulation and minimal optical stimulation of rostromedial tegmental nucleus and ventral pallidum afferents. We next determined the phase resetting curve, the reversal potential for GABAA receptor-mediated inhibitory postsynaptic currents (IPSCs), and the average interspike membrane potential trajectory during pacemaking. We combined these data in a phase oscillator model of a VTA dopamine neuron, simulating the effects of unitary inhibitory postsynaptic conductances (uIPSGs) on spike timing and rate. The effect of a uIPSG on spike timing was predicted to vary according to its timing within the interspike interval or phase. Simulations were performed to predict the pause duration resulting from the synchronous arrival of multiple uIPSGs and the changes in firing rate and regularity produced by asynchronous uIPSGs. The model data suggest that asynchronous inhibition is more effective than synchronous inhibition, because it tends to hold the neuron at membrane potentials well positive to the IPSC reversal potential. Our results indicate that small fluctuations in the inhibitory synaptic input arriving from the many afferents to each dopamine neuron are sufficient to produce highly variable firing patterns, including pauses that have been implicated in reinforcement.

The Integration of Top-down and Bottom-up Inputs to the Striatal Cholinergic Interneurons.

Cholinergic interneurons (ChIs) are important for learning and memory. They exhibit a multiphasic excitation-pause-rebound response to reward or sensory cues indicating a reward, believed to gate dopamine-dependent learning. Although ChIs receive extensive top-down inputs from the cortex and bottom-up inputs from the thalamus and midbrain, it is unclear which inputs are involved in the development of ChI multiphasic activity. We used a single-unit recording of putative ChIs (pChIs) in response to cortical and visual stimulation to investigate how top-down and bottom-up inputs regulate the firing pattern of ChIs. We demonstrated that cortical stimulation strongly regulates pChIs, with the maximum firing rate occurring at the peak of the inverted local field potential (iLFP), reflecting maximum cortical stimulation. Pauses in pChIs occurred during the descending phase of iLFP, indicating withdrawal of excitatory cortical input. Visual stimulation induced long pauses in pChIs, but it is unlikely that bottom- up inputs alone induce pauses in behaving animals. Also, the firing pattern of ChIs triggered by visual stimulation did not correlate with the iLFP as it did after cortical stimulation. Top-down and bottom-up inputs independently regulate the firing pattern of ChIs with similar efficacy but notably produce a well-defined pause in ChI firing. This study provides in vivo evidence that the multiphasic ChI response may require both top-down and bottom-up inputs. The findings suggest that the firing pattern of ChIs correlated to the iLFP might be a useful tool for estimating the degree of contribution of top-down and bottom-up inputs in regulating the firing activity of ChIs.

Hierarchical Spiking-Based Model for Efficient Image Classification With Enhanced Feature Extraction and Encoding.

Thanks to their event-driven nature, spiking neural networks (SNNs) are surmised to be great computation-efficient models. The spiking neurons encode beneficial temporal facts and possess excessive anti-noise properties. However, the high-quality encoding of spatio-temporal complexity and also its training optimization of SNNs are restricted by means of the contemporary problem, this article proposes a novel hierarchical event-driven visual device to explore how information transmits and signifies in the retina the usage of biologically manageable mechanisms. This cognitive model is an augmented spiking-based framework consisting of the function learning capacity of convolutional neural networks (CNNs) with the cognition capability of SNNs. Furthermore, this visual device is modeled in a biological realism way with unsupervised learning rules and advanced spike firing rate encoding methods. We train and test them on some image datasets (Modified National Institute of Standards and Technology (MNIST), Canadian Institute for Advanced Research (CIFAR)10, and its noisy versions) to show that our mannequin can process greater vital data than present cognitive models. This article also proposes a novel quantization approach to make the proposed spiking-based model more efficient for neuromorphic hardware implementation. The outcomes show this joint CNN-SNN model can reap excessive focus accuracy and get more effective generalization ability.

Influence of Blood Flow Restriction on Neuromuscular Function and Fatigue During Forearm Flexion in Men.

Montgomery, TR Jr, Olmos, A, Sears, KN, Succi, PJ, Hammer, SM, Bergstrom, HC, Hill, EC, Trevino, MA, and Dinyer-McNeely, TK. Influence of blood flow restriction on neuromuscular function and fatigue during forearm flexion in men. J Strength Cond Res 38(7): e349-e358, 2024-To determine the effects of blood flow restriction (BFR) on the mean firing rate (MFR) and motor unit action potential amplitude (MUAPAMP) vs. recruitment threshold (RT) relationships during fatiguing isometric elbow flexions. Ten men (24.5 ± 4.0 years) performed isometric trapezoidal contractions at 50% maximum voluntary contraction to task failure with or without BFR, on 2 separate days. For BFR, a cuff was inflated to 60% of the pressure required for full brachial artery occlusion at rest. During both visits, surface electromyography was recorded from the biceps brachii of the dominant limb and the signal was decomposed. A paired-samples t test was used to determine the number of repetitions completed between BFR and CON. ANOVAs (repetition [first, last] × condition [BFR, CON]) were used to determine differences in MFR vs. RT and MUAPAMP vs. RT relationships. Subjects completed more repetitions during CON (12 ± 4) than BFR (9 ± 2; p = 0.012). There was no significant interaction (p > 0.05) between the slopes and y-intercepts during the repetition × condition interaction for MUAPAMP vs. MFR. However, there was a main effect of repetition for the slopes of the MUAPAMP vs. RT (p = 0.041) but not the y-intercept (p = 0.964). Post hoc analysis (collapsed across condition) indicated that the slopes of the MUAPAMP vs. RT during the first repetition was less than the last repetition (first: 0.022 ± 0.003 mv/%MVC; last: 0.028 ± 0.004 mv/%MVC; p = 0.041). Blood flow restriction resulted in the same amount of higher threshold MU recruitment in approximately 75% of the repetitions. Furthermore, there was no change in MFR for either condition, even when taken to task failure. Thus, BFR training may create similar MU responses with less total work completed than training without BFR.

The Volitional Control of Individual Motor Units Is Constrained within Low-Dimensional Neural Manifolds by Common Inputs.

The implementation of low-dimensional movement control by the central nervous system has been debated for decades. In this study, we investigated the dimensionality of the control signals received by spinal motor neurons when controlling either the ankle or knee joint torque. We first identified the low-dimensional latent factors underlying motor unit activity during torque-matched isometric contractions in male participants. Subsequently, we evaluated the extent to which motor units could be independently controlled. To this aim, we used an online control paradigm in which participants received the corresponding motor unit firing rates as visual feedback. We identified two main latent factors, regardless of the muscle group (vastus lateralis-medialis and gastrocnemius lateralis-medialis). The motor units of the gastrocnemius lateralis could be controlled largely independently from those of the gastrocnemius medialis during ankle plantarflexion. This dissociation of motor unit activity imposed similar behavior to the motor units that were not displayed in the feedback. Conversely, it was not possible to dissociate the activity of the motor units between the vastus lateralis and medialis muscles during the knee extension tasks. These results demonstrate that the number of latent factors estimated from linear dimensionality reduction algorithms does not necessarily reflect the dimensionality of volitional control of motor units. Overall, individual motor units were never controlled independently of all others but rather belonged to synergistic groups. Together, these findings provide evidence for a low-dimensional control of motor units constrained by common inputs, with notable differences between muscle groups.