Instantaneous Firing Rate Research Articles

Experience-dependent regulation of dopaminergic signaling in the somatosensory cortex

Dopamine critically influences reward processing, sensory perception, and motor control. Yet, the modulation of dopaminergic signaling by sensory experiences is not fully delineated. Here, by manipulating sensory experience using bilateral single-row whisker deprivation, we demonstrated that gene transcription in the dopaminergic signaling pathway (DSP) undergoes experience-dependent plasticity in both granular and supragranular layers of the primary somatosensory (barrel) cortex (S1). Sensory experience and deprivation compete for the regulation of DSP transcription across neighboring cortical columns, and sensory deprivation-induced changes in DSP are topographically constrained. These changes in DSP extend beyond cortical map plasticity and influence neuronal information processing. Pharmacological regulation of D2 receptors, a key component of DSP, revealed that D2 receptor activation suppresses excitatory neuronal excitability, hyperpolarizes the action potential threshold, and reduces the instantaneous firing rate. These findings suggest that the dopaminergic drive originating from midbrain dopaminergic neurons, targeting the sensory cortex, is subject to experience-dependent regulation and might create a regulatory feedback loop for modulating sensory processing. Finally, using topological gene network analysis and mutual information, we identify the molecular hubs of experience-dependent plasticity of DSP. These findings provide new insights into the mechanisms by which sensory experience shapes dopaminergic signaling in the brain and might help unravel the sensory deficits observed after dopamine depletion.

Fluctuation-response relations for integrate-and-fire models with an absolute refractory period.

We study the problem of relating the spontaneous fluctuations of a stochastic integrate-and-fire (IF) model to the response of the instantaneous firing rate to time-dependent stimulation if the IF model is endowed with a non-vanishing refractory period and a finite (stereotypical) spike shape. This seemingly harmless addition to the model is shown to complicate the analysis put forward by Lindner Phys. Rev. Lett. (2022), i.e., the incorporation of the reset into the model equation, the Rice-like averaging of the stochastic differential equation, and the application of the Furutsu-Novikov theorem. We derive a still exact (although more complicated) fluctuation-response relation (FRR) for an IF model with refractory state and a white Gaussian background noise. We also briefly discuss an approximation for the case of a colored Gaussian noise and conclude with a summary and outlook on open problems.

Attenuation of muscle spindle firing with artificially increased series compliance during stretch of relaxed muscle.

Muscle spindles relay vital mechanosensory information for movement and posture, but muscle spindle feedback is coupled to skeletal motion by a compliant tendon. Little is known about the effects of tendon compliance on muscle spindle feedback during movement, and the complex firing of muscle spindles makes these effects difficult to predict. Our goal was to investigate changes in muscle spindle firing using added series elastic elements (SEEs) to mimic a more compliant tendon, and to characterize the accompanying changes in firing with respect to muscle-tendon unit (MTU) and muscle fascicle displacements (recorded via sonomicrometry). Sinusoidal, ramp-and-hold and triangular stretches were analysed to examine potential changes in muscle spindle instantaneous firing rates (IFRs) in locomotor- and perturbation-like stretches as well as serial history dependence. Added SEEs effectively reduced overall MTU stiffness and generally reduced muscle spindle firing rates, but the effect differed across stretch types. During sinusoidal stretches, peak and mean firing rates were not reduced and IFR was best-correlated with fascicle velocity. During ramp stretches, SEEs reduced the initial burst, dynamic and static responses of the spindle. Notably, IFR was negatively related to fascicle displacement during the hold phase. During triangular stretches, SEEs reduced the mean IFR during the first and second stretches, affecting the serial history dependence of mean IFR. Overall, these results demonstrate that tendon compliance may attenuate muscle spindle feedback during movement, but these changes cannot be fully explained by reduced muscle fascicle length or velocity, or MTU force.

Intrafusal cross-bridge dynamics shape history-dependent muscle spindle responses to stretch.

Computational models can be critical to linking complex properties of muscle spindle organs to the sensory information that they encode during behaviours such as postural sway and locomotion where few muscle spindle recordings exist. Here, we augment a biophysical muscle spindle model to predict the muscle spindle sensory signal. Muscle spindles comprise several intrafusal muscle fibres with varied myosin expression and are innervated by sensory neurons that fire during muscle stretch. We demonstrate how cross-bridge dynamics from thick and thin filament interactions affect the sensory receptor potential at the spike initiating region. Equivalent to the Ia afferent's instantaneous firing rate, the receptor potential is modelled as a linear sum of the force and rate change of force (yank) of a dynamic bag1 fibre and the force of a static bag2/chain fibre. We show the importance of inter-filament interactions in (i) generating large changes in force at stretch onset that drive initial bursts and (ii) faster recovery of bag fibre force and receptor potential following a shortening. We show how myosin attachment and detachment rates qualitatively alter the receptor potential. Finally, we show the effect of faster recovery of receptor potential on cyclic stretch-shorten cycles. Specifically, the model predicts history-dependence in muscle spindle receptor potentials as a function of inter-stretch interval (ISI), pre-stretch amplitude and the amplitude of sinusoidal stretches. This model provides a computational platform for predicting muscle spindle response in behaviourally relevant stretches and can link myosin expression seen in healthy and diseased intrafusal muscle fibres to muscle spindle function.

Modeling Instantaneous Firing Rate of Deep Brain Stimulation Target Neuronal Ensembles in the Basal Ganglia and Thalamus

ObjectiveDeep brain stimulation (DBS) is an effective treatment for movement disorders, including Parkinson disease and essential tremor. However, the underlying mechanisms of DBS remain elusive. Despite the capability of existing models in interpreting experimental data qualitatively, there are very few unified computational models that quantitatively capture the dynamics of the neuronal activity of varying stimulated nuclei—including subthalamic nucleus (STN), substantia nigra pars reticulata (SNr), and ventral intermediate nucleus (Vim)—across different DBS frequencies. Materials and MethodsBoth synthetic and experimental data were used in the model fitting; the synthetic data were generated by an established spiking neuron model that was reported in our previous work, and the experimental data were provided using single-unit microelectrode recordings (MERs) during DBS (microelectrode stimulation). Based on these data, we developed a novel mathematical model to represent the firing rate of neurons receiving DBS, including neurons in STN, SNr, and Vim—across different DBS frequencies. In our model, the DBS pulses were filtered through a synapse model and a nonlinear transfer function to formulate the firing rate variability. For each DBS-targeted nucleus, we fitted a single set of optimal model parameters consistent across varying DBS frequencies. ResultsOur model accurately reproduced the firing rates observed and calculated from both synthetic and experimental data. The optimal model parameters were consistent across different DBS frequencies. ConclusionsThe result of our model fitting was in agreement with experimental single-unit MER data during DBS. Reproducing neuronal firing rates of different nuclei of the basal ganglia and thalamus during DBS can be helpful to further understand the mechanisms of DBS and to potentially optimize stimulation parameters based on their actual effects on neuronal activity.

Assessment of motor unit firing by high-density surface electromyography detects motor neuronal hyperexcitable state in amyotrophic lateral sclerosis.

In amyotrophic lateral sclerosis (ALS), the impact of motor neuron dysfunction on the motor unit (MU) firing pattern remains to be elucidated. The aim of this study was to clarify the characteristics of the MU firing rate and its association with clinical factors in ALS patients using high-density surface electromyography (HDSEMG) and MU decomposition analysis. Nineteen ALS patients and 20 controls prospectively underwent HDSEMG recording of the vastus lateralis muscle during ramp-up (30% of maximum voluntary contraction) and sustained (10% of maximal voluntary contraction for 60 seconds) contractions on performing isometric knee extension. After decomposition analysis, instantaneous firing rates (IFRs) of individually identified MUs were calculated. Comparison of IFRs and clinical variables between ALS patients and controls and analysis of the correlation between individual mean IFR and clinical variables in ALS patients were performed. The number of identified MUs was lower in ALS patients than in controls (P = .017). Mean IFRs of MUs (i.e., mean MU firing rates) were higher in ALS patients than in controls at some force levels on ramp-up contraction (P < .05) and at 50 to 60 seconds during sustained contraction (9.1 [ALS] vs 8.3 [controls] pulses per second; P = .036). There was no correlation between the clinical parameters and mean IFR of each patient. ALS patients had a higher MU firing rate during muscle contraction at a low force level. Noninvasive assessment of the MU firing rate by HDSEMG can detect a motor neuronal hyperexcitable state in ALS patients.

Effects of short-term plasticity in UP-DOWN cortical dynamics

Neuronal dynamics are strongly influenced by short-term plasticity (STP), that is, changes in synaptic efficacy that occur on a short (from milliseconds to seconds) time scale. Depending on the brain areas considered, STP can be dominated by short-term depression (STD), short-term facilitation (STF), or both mechanisms can coexist simultaneously. These two plasticity mechanisms modulate particular patterns of electrophysiological activity characterized by alternating UP and DOWN states. In this work, we develop a network model made up of excitatory and inhibitory multi-compartment neurons endowed with both mechanisms (STD and STF), spatially arranged to emulate the connectivity circuitry observed experimentally in the visual cortex. Our results reveal that both depression and facilitation can be involved in the switching process between different activity patterns, from an alternation of UP and DOWN states (for relatively low levels of depression and high levels of facilitation) to an asynchronous firing regime (for relatively high levels of depression and low levels of facilitation). For STD and STF, we identify the critical levels of depression and facilitation that push the network into the different regimes. Furthermore, we also find that these critical levels separate different growth rates of the mean synaptic conductances of the whole network with respect to the depression levels. This latter data is paramount to understanding how excitation and inhibition are organized to generate different brain activity regimes. Finally, after observing the changes in the trajectories of excitatory and inhibitory instantaneous firing rates near these critical boundaries, we identify dynamic patterns that shed light on the type of bifurcations that should arise in a rate model for this complex network.

The steady state and response to a periodic stimulation of the firing rate for a theta neuron with correlated noise

The stochastic activity of neurons is caused by various sources of correlated fluctuations and can be described in terms of simplified, yet biophysically grounded, integrate-and-fire models. One paradigmatic model is the quadratic integrate-and-fire model and its equivalent phase description by the theta neuron. Here we study the theta neuron model driven by a correlated Ornstein-Uhlenbeck noise and by periodic stimuli. We apply the matrix-continued-fraction method to the associated Fokker-Planck equation to develop an efficient numerical scheme to determine the stationary firing rate as well as the stimulus-induced modulation of the instantaneous firing rate. For the stationary case, we identify the conditions under which the firing rate decreases or increases by the effect of the colored noise and compare our results to existing analytical approximations for limit cases. For an additional periodic signal we demonstrate how the linear and nonlinear response terms can be computed and report resonant behavior for some of them. We extend the method to the case of two periodic signals, generally with incommensurable frequencies, and present a particular case for which a strong mixed response to both signals is observed, i.e. where the response to the sum of signals differs significantly from the sum of responses to the single signals. We provide Python code for our computational method: https://github.com/jannikfranzen/theta_neuron.

Long-term morphological and functional dynamics of human stem cell-derived neuronal networks on high-density micro-electrode arrays.

Comprehensive electrophysiological characterizations of human induced pluripotent stem cell (hiPSC)-derived neuronal networks are essential to determine to what extent these in vitro models recapitulate the functional features of in vivo neuronal circuits. High-density micro-electrode arrays (HD-MEAs) offer non-invasive recording with the best spatial and temporal resolution possible to date. For 3 months, we tracked the morphology and activity features of developing networks derived from a transgenic hiPSC line in which neurogenesis is inducible by neurogenic transcription factor overexpression. Our morphological data revealed large-scale structural changes from homogeneously distributed neurons in the first month to the formation of neuronal clusters over time. This led to a constant shift in position of neuronal cells and clusters on HD-MEAs and corresponding changes in spatial distribution of the network activity maps. Network activity appeared as scarce action potentials (APs), evolved as local bursts with longer duration and changed to network-wide synchronized bursts with higher frequencies but shorter duration over time, resembling the emerging burst features found in the developing human brain. Instantaneous firing rate data indicated that the fraction of fast spiking neurons (150–600 Hz) increases sharply after 63 days post induction (dpi). Inhibition of glutamatergic synapses erased burst features from network activity profiles and confirmed the presence of mature excitatory neurotransmission. The application of GABAergic receptor antagonists profoundly changed the bursting profile of the network at 120 dpi. This indicated a GABAergic switch from excitatory to inhibitory neurotransmission during circuit development and maturation. Our results suggested that an emerging GABAergic system at older culture ages is involved in regulating spontaneous network bursts. In conclusion, our data showed that long-term and continuous microscopy and electrophysiology readouts are crucial for a meaningful characterization of morphological and functional maturation in stem cell-derived human networks. Most importantly, assessing the level and duration of functional maturation is key to subject these human neuronal circuits on HD-MEAs for basic and biomedical applications.

Directional Preference in Avian Midbrain Saliency Computing Nucleus Reflects a Well-Designed Receptive Field Structure.

Simple SummaryDirectional preference neurons has been found in many vertebrate sensory systems. The isthmi pars magnocellularis (Imc) in avian midbrain, playing a key role in visual selective attention, shows impressive motion directional preference, but little is known about the physiological basis of this phenomenon. Herein, artificial visual stimuli, statistical analyses, and a neural computational model were used to unravel this mystery. This study deepens the understanding of the relationship between the directional preference and special receptive field structure of pigeon’s (Columba livia) Imc neuron.Neurons responding sensitively to motions in several rather than all directions have been identified in many sensory systems. Although this directional preference has been demonstrated by previous studies to exist in the isthmi pars magnocellularis (Imc) of pigeon (Columba livia), which plays a key role in the midbrain saliency computing network, the dynamic response characteristics and the physiological basis underlying this phenomenon are unclear. Herein, dots moving in 16 directions and a biologically plausible computational model were used. We found that pigeon Imc’s significant responses for objects moving in preferred directions benefit the long response duration and high instantaneous firing rate. Furthermore, the receptive field structures predicted by a computational model, which captures the actual directional tuning curves, agree with the real data collected from population Imc units. These results suggested that directional preference in Imc may be internally prebuilt by elongating the vertical axis of the receptive field, making predators attack from the dorsal-ventral direction and conspecifics flying away in the ventral-dorsal direction, more salient for avians, which is of great ecological and physiological significance for survival.

Extensions of a neural-fluctuation perspective on simultaneous notched-noise masking

A previous paper [Maxwell et al., J. Acoust. Soc. Am. 3523, 3537 (2020)] suggested that listeners may identify the target interval in a simultaneous notched-noise task using neural fluctuations, which change with the addition of a tone to the masker. Neural fluctuations are changes in the instantaneous firing rate of auditory-nerve fibers, generally at a longer (slower) time scale than temporal fine structure, that are shaped by peripheral nonlinearities. Differences in neural-fluctuation amplitudes across the peripheral tonotopic axis are proposed to lead to differences in rates across the population of auditory midbrain neurons, due to the sensitivity of these neurons to amplitude modulation. Here, using similar methods to Maxwell et al. (2020) and stimuli matching those of Baker and Rosen (J.Ascoust. Soc. Am. 454, 462 (2006)], we extend previous work by demonstrating that neural fluctuations may account for additional features of simultaneous notched-noise masking results. These features include (1) increase in filter bandwidth at higher stimulus frequencies, (2) higher bandwidth with increasing masker level, and (3) filter asymmetry. These results, in conjunction with the previous conclusion that auditory-nerve excitation patterns cannot explain notched-noise thresholds in all cases, support the interpretation that psychophysical auditory filters may be based on the output of the subcortical auditory system. [Work supported by NIDCD-R01-010813.]

Cyclic, Condition-Independent Activity in Primary Motor Cortex Predicts Corrective Movement Behavior

Reaching movements are known to have large condition-independent (CI) neural activity and cyclic neural dynamics. A new precision center-out task was performed by rhesus macaques to test the hypothesis that cyclic, CI neural activity in the primary motor cortex (M1) occurs not only during initial reaching movements but also during subsequent corrective movements. Corrective movements were observed to be discrete with time courses and bell-shaped speed profiles similar to the initial movements. CI cyclic neural trajectories were similar and repeated for initial and each additional corrective submovement. The phase of the cyclic CI neural activity predicted the time of peak movement speed more accurately than regression of instantaneous firing rate, even when the subject made multiple corrective movements. Rather than being controlled as continuations of the initial reach, a discrete cycle of motor cortex activity encodes each corrective submovement.

The relationship of agonist muscle single motor unit firing rates and elbow extension limb movement kinematics.

This study explored the relationship between single motor unit (MU) firing rates (FRs) and limb movement velocity during voluntary shortening contractions when accounting for the effects of time course variability between different kinematic comparisons. Single MU trains recorded by intramuscular electromyography in agonist muscles of the anconeus (n = 15 participants) and lateral head of the triceps brachii (n = 6) were measured during each voluntary shortening contraction. Elbow extension movements consisted of a targeted velocity occurring along the sagittal plane at 25, 50, 75 and 100% of maximum velocity. To account for the effect of differences in contraction time course between parameters, each MU potential was time locked throughout the shortening muscle contraction and linked with separated kinematic parameters of the elbow joint. Across targeted movement velocities, instantaneous FRs were significantly correlated with elbow extension rate of torque development (r = 0.45) and torque (r = 0.40), but FRs were not correlated with velocity (r = 0.03, p = n.s.). Instead, FRs had a weak indirect relationship with limb movement velocity and position assessed through multiple correlation of the stepwise kinematic progression. Results show that voluntary descending synaptic inputs correspond to a more direct relationship between agonist muscle FRs and torque during shortening contractions, but not velocity. Instead, FRs were indirectly correlated to preparing the magnitude of imminent movement velocity of the lagging limb through torque.

Variability and Randomness of the Instantaneous Firing Rate.

The apparent stochastic nature of neuronal activity significantly affects the reliability of neuronal coding. To quantify the encountered fluctuations, both in neural data and simulations, the notions of variability and randomness of inter-spike intervals have been proposed and studied. In this article we focus on the concept of the instantaneous firing rate, which is also based on the spike timing. We use several classical statistical models of neuronal activity and we study the corresponding probability distributions of the instantaneous firing rate. To characterize the firing rate variability and randomness under different spiking regimes, we use different indices of statistical dispersion. We find that the relationship between the variability of interspike intervals and the instantaneous firing rate is not straightforward in general. Counter-intuitively, an increase in the randomness (based on entropy) of spike times may either decrease or increase the randomness of instantaneous firing rate, in dependence on the neuronal firing model. Finally, we apply our methods to experimental data, establishing that instantaneous rate analysis can indeed provide additional information about the spiking activity.

Offset analgesia is reduced on the palm and increases with stimulus duration.

A noxious stimulus following a more intense stimulus often feels less painful than continuous noxious stimulation. This effect, known as offset analgesia (OA), may be due to descending inhibitory control, to changes in peripheral neural transmission or both. The timing and location of noxious thermal stimulation were manipulated to better understand the peripheral and central contributions to OA. In a first experiment, participants (n=29) provided continuous pain ratings as stimuli were delivered to the palm or dorsum of each hand. Offset trials included 44°C (T1), 45°C (T2) and 44°C (T3) stimulation periods. Baseline trials were identical except the T3 temperature fell to 35°C. Constant trials were 44°C throughout. The duration of T1 and T2 was either 1s or 6s, whereas T3 was always 12s. In a second experiment, participants (n=43) rated pain levels of noxious stimuli presented to the forearms with varying T1 and T2 durations (3, 6, 10 or 13s) and a 20s T3 period. OA effects became stronger with increasing inducing durations. OA, however, was not found on the palm even at longer durations. The increase in OA with duration suggests that accumulated nociceptive signalling is more important to triggering OA than is a decrease in nociceptors' instantaneous firing rates. The lack of OA on the palm, however, implies a key role for the rapidly adapting Type II AMH fibres that may be absent or not readily activated on the palm. Unravelling the relative central and peripheral contribution to OA requires further investigation. Offset analgesia (OA) is a fundamentally temporal phenomenon dependent on dynamic changes in stimulus intensity. Here we demonstrate increased OA with increased stimulus duration. This finding implies the more slowly-responding AMH-I peripheral mechanoreceptors contribute to OA. The more rapidly responding AMH-II peripheral mechanoreceptors, however, may be absent or more difficult to activate in the palm where we did not observe OA. This finding implies that the AMH-II receptors are necessary for OA. Our studies suggest methods to unravel the different peripheral and central contributions to OA.

High-density surface electromyography to assess motor unit firing rate in Charcot-Marie-Tooth disease type 1A patients

ObjectiveThe aim of this study was to elucidate the characteristics of the motor unit (MU) firing rate in Charcot-Marie-Tooth disease type 1A (CMT1A) patients and its longitudinal change using high-density surface-electromyography (surface-EMG) and MU decomposition analysis. MethodsNineteen patients with CMT1A and 21 force-matched healthy controls prospectively underwent surface-EMG recording of the vastus lateralis muscle during ramp-up and sustained contractions on performing isometric knee extension. After decomposition analysis, instantaneous firing rates (IFRs) of individually identified MUs were calculated. In CMT1A patients, follow-up measurements were performed one year after the baseline. Comparison of IFRs and clinical variables between CMT1A patients and controls at the baseline and between the baseline and after one year in CMT1A patients was performed. ResultsMean IFRs of MUs were lower in CMT1A patients than in controls. This was true at various force levels in ramp-up contractions (p < 0.01. e.g., 10.3 (CMT1A patients) vs. 12.2 (controls) pulses-per-second (pps) at 22.5–27.5% of maximal voluntary contraction (MVC) in MUs recruited at <7.5% of MVC) and at any time-point during sustained contractions (p < 0.001. e.g., 8.0 vs. 9.3 pps, respectively, at 10–20 seconds). In CMT1A patients, mean IFRs at 0–10 seconds of sustained contraction were significantly decreased over one year (from 8.06 to 7.52 pps; p = 0.027), whereas the disease severity score and MVC of knee extension did not change over time. ConclusionCMT1A patients had a lower individual MU firing rate. SignificanceThe MU firing rate is a potential short-term biomarker of axonal damage in CMT1A patients.

Feedback-dependent neuronal properties make focal dystonias so focal.

Focal dystonia, by definition, affects a specific body part; however, it may have a widespread neural substrate. We tested this hypothesis by examining the intrinsic behaviour and the neuronal properties that are modulated by changes in the physiological behaviour of their connections, that is feedback dependence, of the isolated pallidal neurons. During deep brain stimulation surgery in 12 patients with isolated cervical dystonia (without hand involvement), we measured spontaneous as well as evoked single-unit properties in response to fist making (hand movement) or shoulder shrug (neck movements). We measured the activity of isolated neurons that were only sensitive to the neck movements, hand movement, or not responsive to hand or neck movements. The spontaneous firing behaviour, such as the instantaneous firing rate and its regularity, was comparable in all three types of neurons. The neck movement-sensitive neurons had prominent bursting behaviour in comparison with the hand neurons. The feedback dependence of the neck movement-sensitive neurons was also significantly impaired when compared to hand movement-sensitive neurons. Motor-evoked change in firing rate of neck movement-sensitive neurons rapidly declined; the decay time constant was much shorter compared to hand movement-sensitive neurons. These results suggest that in isolated cervical dystonia, at the resolution of single neurons, the deficits are much widespread, affecting the neurons that drive the neck movement as well as the hand movements. We speculate that clinically discernable dystonia occurs when additional abnormality is added to baseline dysfunctional network, and one source of such abnormality may involve feedback.

Dynamic contraction dependence on the instantaneous motor unit firing rates

The effect of voluntary muscle contraction is to regulate position change about a joint involving recruitment and rate coding of motor units (MU) to produce torque results in the shortening velocity component. Unlike isometric (static) muscle contractions, isotonic (dynamic) muscle contractions can involve feedback to slow the shortening velocity as the joint reaches the terminal position. During dynamic conditions of increasing velocity targets, evidence indicates that the neuromuscular initiation of movement is greater than at isometric conditions, possibly being enhanced by the motor neuron secondary firing rate range. Our objective was to further characterize voluntary neuromuscular control at the level of instantaneous motor unit action potential firings during dynamic shortening muscle contractions. During isometric and isotonic contractions, intramuscular fine‐wire electromyography (EMG) was used to record MU potentials from the anconeus and triceps brachii muscles in healthy younger men and women (range 20–35 years, n= 15). Isotonic contractions were normalized to the maximal isotonic velocity and were loaded at 20% of the maximal isometric voluntary contraction torque. For analysis, the EMG was digitally high‐pass filtered (1 kHz, 3rd order Butterworth) and MU action potential waveforms were determined and sorted by template matching (Spike2 Wavemark, version 7) with manual inspection. Torque, velocity and position were also measured and at each individual firing rate time‐point and these vectors were aligned. From this, 627 MU trains (mean±standard deviation 42±19, n= 15) during the isotonic contractions in the anconeus muscle and 226 MU trains (38±14, n= 6) in the long head of the triceps brachii muscle were measured. To explore a more comprehensive model related to firing rates, the torque, velocity and position were modelled by linear regression with the residuals of MU train number, participant, testing session, sex, muscle and target velocity removed. From the full model when comparing slope coefficients, there was no significant interactions between torque (β= 48.1x) and velocity (β= −28.5x) on firing rate dependence (p= 0.2). Position (β= 0.4x) had a smaller coefficient on firing rates as compared to torque and velocity and a significant interaction effect with torque (p&lt; 0.05), but not velocity (p= 0.4). These results indicate that a model of instantaneous firing rate dependence on torque, velocity and position requires a re‐examination, especially because the interaction of torque and velocity were significantly correlated (r= 0.5, p&lt; 0.05). Shortening velocity of muscles causing elbow joint extension must be first initiated by a rate of torque development, further supporting that a different conceptual approach may be necessary to understand MU output during dynamic contractions. It may be important in this task to model firing rates as the independent variable (time vector) that will affect succeeding dependent changes of torque, velocity and position.Support or Funding InformationSupported by NSERC.

Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning.

Objective: In brain machine interfaces (BMIs), the functional mapping between neural activities and kinematic parameters varied over time owing to changes in neural recording conditions. The variability in neural recording conditions might result in unstable long-term decoding performance. Relevant studies trained decoders with several days of training data to make them inherently robust to changes in neural recording conditions. However, these decoders might not be robust to changes in neural recording conditions when only a few days of training data are available. In time-series prediction and feedback control system, an error feedback was commonly adopted to reduce the effects of model uncertainty. This motivated us to introduce an error feedback to a neural decoder for dealing with the variability in neural recording conditions.Approach: We proposed an evolutionary constructive and pruning neural network with error feedback (ECPNN-EF) as a neural decoder. The ECPNN-EF with partially connected topology decoded the instantaneous firing rates of each sorted unit into forelimb movement of a rat. Furthermore, an error feedback was adopted as an additional input to provide kinematic information and thus compensate for changes in functional mapping. The proposed neural decoder was trained on data collected from a water reward-related lever-pressing task for a rat. The first 2 days of data were used to train the decoder, and the subsequent 10 days of data were used to test the decoder.Main Results: The ECPNN-EF under different settings was evaluated to better understand the impact of the error feedback and partially connected topology. The experimental results demonstrated that the ECPNN-EF achieved significantly higher daily decoding performance with smaller daily variability when using the error feedback and partially connected topology.Significance: These results suggested that the ECPNN-EF with partially connected topology could cope with both within- and across-day changes in neural recording conditions. The error feedback in the ECPNN-EF compensated for decreases in decoding performance when neural recording conditions changed. This mechanism made the ECPNN-EF robust against changes in functional mappings and thus improved the long-term decoding stability when only a few days of training data were available.

Neuromodulatory Selection of Motor Neuron Recruitment Patterns in a Visuomotor Behavior Increases Speed.

Animals generate locomotion at different speeds to suit their behavioral needs. Spinal circuits generate locomotion at these varying speeds by sequential activation of different spinal interneurons and motor neurons. Larval zebrafish can generate slow swims for prey capture and exploration by activation of secondary motor neurons and much faster and vigorous swims during escape and struggle via additional activation of primary motor neurons. Neuromodulators are known to alter the motor output of spinal circuits, but their precise role in speed regulation is not wellunderstood. Here, in the context of optomotor response (OMR), an innate evoked locomotor behavior, we show that dopamine (DA) provides an additional layer to regulation of swim speed in larval zebrafish. Activation of D1-like receptors increases swim speed during OMR in free-swimming larvae. By analyzing tail bend kinematics in head-restrained larvae, we show that the increase in speed is actuated by larger tail bends. Whole-cell patch-clamp recordings from motor neurons reveal that, during OMR, typically only secondary motor neurons are active, whereas primary motor neurons are quiescent. Activation of D1-like receptors increases intrinsic excitability and excitatory synaptic drive in primary and secondary motor neurons. These actions result in greater recruitment of motor neurons during OMR. Our findings provide an example of neuromodulatory reconfiguration of spinal motor neuron speed modules where members are selectively recruited and motor drive is increased to effect changes in locomotor speed. VIDEO ABSTRACT.