Firing Rate Of Neurons Research Articles

Convergent escape behaviour from distinct visual processing of impending collision in fish and grasshoppers.

In animal species ranging from invertebrate to mammals, visually guided escape behaviours have been studied using looming stimuli, the two-dimensional expanding projection on a screen of an object approaching on a collision course at constant speed. The peak firing rate or membrane potential of neurons responding to looming stimuli often tracks a fixed threshold angular size of the approaching stimulus that contributes to the triggering of escape behaviours. To study whether this result holds more generally, we designed stimuli that simulate acceleration or deceleration over the course of object approach on a collision course. Under these conditions, we found that the angular threshold conveyed by collision detecting neurons in grasshoppers was sensitive to acceleration whereas the triggering of escape behaviours was less so. In contrast, neurons in goldfish identified through the characteristic features of the escape behaviours they trigger, showed little sensitivity to acceleration. This closely mirrored a broader lack of sensitivity to acceleration of the goldfish escape behaviour. Thus, although the sensory coding of simulated colliding stimuli with non-zero acceleration probably differs in grasshoppers and goldfish, the triggering of escape behaviours converges towards similar characteristics. Approaching stimuli with non-zero acceleration may help refine our understanding of neural computations underlying escape behaviours in a broad range of animal species. KEY POINTS: A companion manuscript showed that two mathematical models of collision-detecting neurons in grasshoppers and goldfish make distinct predictions for the timing of their responses to simulated objects approaching on a collision course with non-zero acceleration. Testing these experimental predictions showed that grasshopper neurons are sensitive to acceleration while goldfish neurons are not, in agreement with the distinct models proposed previously in these species using constant velocity approaches. Grasshopper and goldfish escape behaviours occurred after the stimulus reached a fixed angular size insensitive to acceleration, suggesting further downstream processing in grasshopper motor circuits to match what was observed in goldfish. Thus, in spite of different sensory processing in the two species, escape behaviours converge towards similar solutions. The use of object acceleration during approach on a collision course may help better understand the neural computations implemented for collision avoidance in a broad range of species.

Participation of the nucleus tractus solitarius in the therapeutic effect of electroacupuncture on post-stroke dysphagia through the primary motor cortex.

Post-stroke dysphagia (PSD), a common and serious disease, affects the quality of life of many patients and their families. Electroacupuncture (EA) has been commonly used effectively in the treatment of PSD, but the therapeutic mechanism is still under exploration at present. We aim to investigate the effect of the nucleus tractus solitarus (NTS) on the treatment of PSD by EA at Lianquan (CV23) through the primary motor cortex (M1). C57 male mice were used to construct a PSD mouse model using photothrombotic technique, and the swallowing function was evaluated by electromyography (EMG) recording. C-Fos-positive neurons and types of neurons in the NTS were detected by immunofluorescence. Optogenetics and chemical genetics were used to regulate the NTS, and the firing rate of neurons was recorded via multichannel recording. The results showed that most of the activated neurons in the NTS were excitatory neurons, and multichannel recording indicated that the activity levels of both pyramidal neurons and interneurons in the NTS were regulated by M1. This process was involved in the EA treatment. Furthermore, while chemogenetic inhibition of the NTS reduced the EMG signal associated with the swallowing response induced by activation of M1 in PSD mice, EA rescued this signal. Overall, the NTS was shown to participate in the regulation of PSD by EA at CV23 through M1.

Distractor-Specific Single Neuron Activity Predicts Visual Working Memory Task Outcomes


 
 
 
 This paper explores the relationship between neural activity and behavioral performance in the form of visual working memory (VWM) task outcomes, by answering the question: Are there any significant differences in the firing rates of individual neurons during the distractor presentation period of a VWM task between success and error trials that can predict the outcome of a trial? Distractor-specific single neuron firing rates during a VWM task were analyzed to answer this question. A logistic regression was used to identify the predictive capability of neural firing rate on trial outcome with the neural activity of 51 cells from the lateral prefrontal cortex (LPFC) of a primate. This study found that a best-fit logistic model could predict the behavioral performance of the primate (success or error of the VWM task) with 63.01% accuracy, with additional machine learning techniques producing scores upwards of 68% accuracy. Moreover, greater firing rates in response to the distractor, indicating less efficient distractor suppression, accompanied the error trials of the VWM task. This suggests that stronger neural responses to task-specific distractors can hinder the attentional filtering required for efficient working memory, supporting previous research that found that distractor suppression is a mechanism that heavily influences WM efficiency. These findings indicate that people, particularly children, with disorders that affect WM capacity such as ADHD may experience stronger neural responses to distractors, and therefore inefficient distractor suppression, at the single neuron level when engaging in goal-oriented behaviors, which can significantly impact learning and other developmental processes.
 
 
 

Differences in subthalamic oscillatory activity in the two hemispheres associated with severity of Parkinson's disease.

It is well known that motor features of Parkinson's disease (PD) commonly begin on one side of the body and extend to the other side with disease progression. The onset side generally remains more severely affected over the course of the disease. However, the pathophysiology underlying the asymmetry of motor manifestations remains unclear. The purpose of the present study is to examine whether alterations in neuronal activity in the subthalamic nucleus (STN) associate with PD severity. Microelectrode recording was performed in the STN during targeting for 30 patients in the treatment of deep brain stimulation. The mean spontaneous firing rate (MSFR), power density spectral analysis, and correlations were calculated. Characteristics of subthalamic oscillatory activity were compared between two hemispheres. UPDRS III scores during "Off" and "On" states were obtained for the body side of initial symptoms (BSIS) and the body side of extended symptoms (BSES). There were significant differences of MSFR (41.3 ± 11.0 Hz vs 35.2 ± 10.0 Hz) and percentage of ß frequency oscillatory neurons (51.3% vs 34.9%) between BSIS and BSES. The percentage of ß frequency oscillatory neurons correlated with the bradykinesia/rigidity scores for both sides (p < 0.05). In contrast, the percentage of tremor frequency oscillatory neurons was significantly higher in the BSES than that in the BSIS. In particular, these neurons only correlated with the tremor scores of the BSES (p < 0.05). The results suggest that increased neuronal firing rate and ß frequency oscillatory neurons in the STN are associated with contralateral side motor severity and its progression. Tremor frequency oscillatory neurons are less observed in the STN of the BSIS suggesting that ß oscillatory activity dominates and tremor frequency oscillatory activity reciprocally declines.

Single-neuron and population measures of neuronal activity in working memory tasks.

Information represented in working memory is reflected in the firing rate of neurons in the prefrontal cortex and brain areas connected to it. In recent years, there has been an increased realization that population measures capture more accurately neural correlates of cognitive functions. We examined how single neuron firing in the prefrontal and posterior parietal cortex of two male monkeys compared with population measures in spatial working memory tasks. Persistent activity was observed in the dorsolateral prefrontal and posterior parietal cortex and firing rate predicted working memory behavior, particularly in the prefrontal cortex. These findings had equivalents in population measures, including trajectories in state space that became less separated in error trials. We additionally observed rotations of stimulus representations in the neuronal state space for different task conditions, which were not obvious in firing rate measures. These results suggest that population measures provide a richer view of how neuronal activity is associated with behavior, largely confirming that persistent activity is the core phenomenon that maintains visual-spatial information in working memory.NEW & NOTEWORTHY Recordings from large numbers of neurons led to a reevaluation of neural correlates of cognitive functions, which traditionally were defined based on responses of single neurons or averages of firing rates. Analysis of neuronal recordings from the dorsolateral prefrontal and posterior parietal cortex revealed that properties of neuronal firing captured in classical studies of persistent activity can account for population representations, though some population characteristics did not have clear correlates in single neuron activity.

Characterization of perception by transcutaneous electrical Stimulation in terms of tingling intensity and temporal dynamics.

Electrotactile feedback is a cost-effective and versatile method to provide new information or to augment intrinsic tactile feedback. As tactile feedback provides critical information for human-environment interaction, electrotactile feedback, accordingly, has many purposes to improve the quality of human-environment interaction in both direct and remote settings. However, electrotactile feedback overlays tingling sensation on top of the natural tactile feedback. To better characterize electrotactile feedback and understand the origin of the tingling sensation, a need arises to characterize the human perception of electrotactile feedback qualitatively and quantitatively, while varying the key stimulation parameters, namely amplitude and frequency. This study consists of two experiments. In the first experiment, the voltage for each subject was characterized by setting perception and discomfort thresholds. In the second experiment, subjects received electrical stimulation in 9 different combinations of voltages and frequencies. On delivering stimulation with each parameter combination, subjects reported their perception in two comparative scales-pressure vs. tingling and constant vs. pulsing. Subjects also reported the location of perception for stimulation with every parameter combination. More tingling and less pressure was reported as frequency increased, while the tingling-pressure percept was not affected by the amplitude change. Additionally, less pulsing and more constant was reported as frequency increased, while the pulsing-constant percept was not affected by the amplitude change. Concurrently, the normalized level of voltage thresholds was decreased as frequency increased. Dependency of tingling-pressure percept on stimulation frequency suggests that incongruency between the stimulation frequency and the natural firing rate of the sensory neuron would be an important factor of the tingling sensation. This study is a steppingstone to further demystify the origin of the tingling percept caused by electrical stimulation, thus broadening the use of transcutaneous electrical stimulation as a way of providing tactile cue or augmentation.

Exendin-4 increases the firing activity of hippocampal CA1 neurons through TRPC4/5 channels

The central neuropeptide GLP-1 is synthesized by preproglucagon (PPG) neurons in the brain. GLP-1 receptors are widely distributed in central nervous system. Hippocampus is a key component of the limbic system which is involved in learning, memory, and cognition. Previous studies have shown that overexpression of GLP-1 receptors in the hippocampus could improve the process of learning and memory. However, up to now, the direct electrophysiological effects and possible molecular mechanisms of GLP-1 in hippocampal CAl neurons remain unexplored. The present study aims to evaluate the effects and mechanisms of GLP-1 on the spontaneous firing activity of hippocampal CAl neurons. Employing multibarrel single-unit extracellular recordings, the present study showed that micro-pressure administration of GLP-1 receptor agonist, exendin-4, significantly increased the spontaneous firing rate of hippocampal CA1 neurons in rats. Furthermore, application of the specific GLP-1 receptor antagonist, exendin(9−39), alone significantly decreased the firing rate of CA1 neurons, suggesting that endogenous GLP-1 modulates the firing activity of CA1 neurons. Co-application of exendin(9−39) completely blocked exendin-4-induced excitation of hippocampal CA1 neurons. Finally, the present study demonstrated for the first time that the transient receptor potential canonical 4 (TRPC4)/TRPC5 channels may be involved in exendin-4-induced excitation. The present studies may provide a rationale for further investigation of the modulation of GLP-1 on learning and memory as well as its possible involvement in Alzheimer's disease.

Effective full connection neural network updating using a quantized full FORCE algorithm

This paper presents a new training algorithm that can update the situation of layers’ network, and therefore, connections, neurons, and firing rate of neurons based on FORCE (first-order reduced and controlled error) training algorithm. The Quantized Full FORCE algorithm (QFF) also updates the number of neurons and connections between different layers in the network per iteration in a way that the whole firing rate of each layer is updated via selecting the best neurons and combining strong features. The update method is sequential, so that with each instance passing through the network, the network structure is updated with the Full FORCE algorithm. The algorithm updates the structure of networks with a multiple/single middle layer of the supervised version of feed forward networks such as Multilayer perceptron (MLP), changing them into partially-connected networks. A combination of principal component analysis PCA and Linear Discriminant Analysis (LDA) algorithms has been used to cluster the network input features. The paper focuses on the deep supervised MLP network with backpropagation (BP) and various datasets and its comparison with other MLP based stat of art methods and hybrid evolutionary algorithms. We achieved 98.15 percent accuracy for facial expression 98.6 and 97.7 percent for Wisconsin breast Cancer and Iris Flower in respectively. The training algorithm employed in the study enjoys a lower computational complexity while yielding faster and more accurate convergence, starting with a very low level of errors of 0.009 in comparison with the full connection network and it solves the challenge of getting stuck in local minima and poor convergence of Gradient Decent with BP.

Feasibility of dopamine as a vector-valued feedback signal in the basal ganglia

It is well established that midbrain dopaminergic neurons support reinforcement learning (RL) in the basal ganglia by transmitting a reward prediction error (RPE) to the striatum. In particular, different computational models and experiments have shown that a striatum-wide RPE signal can support RL over a small discrete set of actions (e.g., no/no-go, choose left/right). However, there is accumulating evidence that the basal ganglia functions not as a selector between predefined actions but rather as a dynamical system with graded, continuous outputs. To reconcile this view with RL, there is a need to explain how dopamine could support learning of continuous outputs, rather than discrete action values. Inspired by the recent observations that besides RPE, the firing rates of midbrain dopaminergic neurons correlate with motor and cognitive variables, we propose a model in which dopamine signal in the striatum carries a vector-valued error feedback signal (a loss gradient) instead of a homogeneous scalar error (a loss). We implement a local, "three-factor" corticostriatal plasticity rule involving the presynaptic firing rate, a postsynaptic factor, and the unique dopamine concentration perceived by each striatal neuron. With this learning rule, we show that such a vector-valued feedback signal results in an increased capacity to learn a multidimensional series of real-valued outputs. Crucially, we demonstrate that this plasticity rule does not require precise nigrostriatal synapses but remains compatible with experimental observations of random placement of varicosities and diffuse volume transmission of dopamine.

Kv12-encoded K+ channels drive the day-night switch in the repetitive firing rates of SCN neurons.

Considerable evidence suggests that day-night rhythms in the functional expression of subthreshold potassium (K+) channels regulate daily oscillations in the spontaneous firing rates of neurons in the suprachiasmatic nucleus (SCN), the master circadian pacemaker in mammals. The K+ conductance(s) driving these daily rhythms in the repetitive firing rates of SCN neurons, however, have not been identified. To test the hypothesis that subthreshold Kv12.1/Kv12.2-encoded K+ channels play a role, we obtained current-clamp recordings from SCN neurons in slices prepared from adult mice harboring targeted disruptions in the Kcnh8 (Kv12.1-/-) or Kcnh3 (Kv12.2-/-) locus. We found that mean nighttime repetitive firing rates were higher in Kv12.1-/- and Kv12.2-/- than in wild type (WT), SCN neurons. In marked contrast, mean daytime repetitive firing rates were similar in Kv12.1-/-, Kv12.2-/-, and WT SCN neurons, and the day-night difference in mean repetitive firing rates, a hallmark feature of WT SCN neurons, was eliminated in Kv12.1-/- and Kv12.2-/- SCN neurons. Similar results were obtained with in vivo shRNA-mediated acute knockdown of Kv12.1 or Kv12.2 in adult SCN neurons. Voltage-clamp experiments revealed that Kv12-encoded current densities in WT SCN neurons are higher at night than during the day. In addition, the pharmacological block of Kv12-encoded currents increased the mean repetitive firing rate of nighttime, but not daytime, in WT SCN neurons. Dynamic clamp-mediated subtraction of modeled Kv12-encoded currents also selectively increased the mean repetitive firing rates of nighttime WT SCN neurons. Despite the elimination of the nighttime decrease in the mean repetitive firing rates of SCN neurons, however, locomotor (wheel-running) activity remained rhythmic in Kv12.1-/-, Kv12.2-/-, and Kv12.1-targeted shRNA-expressing, and Kv12.2-targeted shRNA-expressing animals.

How coupled slow oscillations, spindles and ripples coordinate neuronal processing and communication during human sleep

Learning and plasticity rely on fine-tuned regulation of neuronal circuits during offline periods. An unresolved puzzle is how the sleeping brain, in the absence of external stimulation or conscious effort, coordinates neuronal firing rates (FRs) and communication within and across circuits to support synaptic and systems consolidation. Using intracranial electroencephalography combined with multiunit activity recordings from the human hippocampus and surrounding medial temporal lobe (MTL) areas, we show that, governed by slow oscillation (SO) up-states, sleep spindles set a timeframe for ripples to occur. This sequential coupling leads to a stepwise increase in (1) neuronal FRs, (2) short-latency cross-correlations among local neuronal assemblies and (3) cross-regional MTL interactions. Triggered by SOs and spindles, ripples thus establish optimal conditions for spike-timing-dependent plasticity and systems consolidation. These results unveil how the sequential coupling of specific sleep rhythms orchestrates neuronal processing and communication during human sleep.

Application of deep reinforcement learning for spike sorting under multi-class imbalance

Spike sorting is the basis for analyzing spike firing patterns encoded in high-dimensional information spaces. With the fact that high-density microelectrode arrays record multiple neurons simultaneously, the data collected often suffers from two problems: a few overlapping spikes and different neuronal firing rates, which both belong to the multi-class imbalance problem. Since deep reinforcement learning (DRL) assign targeted attention to categories through reward functions, we propose ImbSorter to implement spike sorting under multi-class imbalance. We describe spike sorting as a Markov sequence decision and construct a dynamic reward function (DRF) to improve the sensitivity of the agent to minor classes based on the inter-class imbalance ratios. The agent is eventually guided by the optimal strategy to classify spikes. We consider the Wave_Clus dataset, which contains overlapping spikes and diverse noise levels, and the macaque dataset, which has a multi-scale imbalance. ImbSorter is compared with classical DRL architectures, traditional machine learning algorithms, and advanced overlapping spike sorting techniques on these two above datasets. ImbSorter obtained improved results on the Macro_F1. The results show ImbSorter has a promising ability to resist overlapping and noise interference. It has high stability and promising performance in processing spikes with different degrees of skewed distribution.

Propagation of Oscillations in the Indirect Pathway of the Basal Ganglia.

Oscillatory signals propagate in the basal ganglia from prototypic neurons in the external globus pallidus (GPe) to their target neurons in the substantia nigra pars reticulata (SNr), internal pallidal segment, and subthalamic nucleus. Neurons in the GPe fire spontaneously, so oscillatory input signals can be encoded as changes in timing of action potentials within an ongoing spike train. When GPe neurons were driven by an oscillatory current in male and female mice, these spike-timing changes produced spike-oscillation coherence over a range of frequencies extending at least to 100 Hz. Using the known kinetics of the GPe→SNr synapse, we calculated the postsynaptic currents that would be generated in SNr neurons from the recorded GPe spike trains. The ongoing synaptic barrage from spontaneous firing, frequency-dependent short-term depression, and stochastic fluctuations at the synapse embed the input oscillation into a noisy sequence of synaptic currents in the SNr. The oscillatory component of the resulting synaptic current must compete with the noisy spontaneous synaptic barrage for control of postsynaptic SNr neurons, which have their own frequency-dependent sensitivities. Despite this, SNr neurons subjected to synaptic conductance changes generated from recorded GPe neuron firing patterns also became coherent with oscillations over a broad range of frequencies. The presynaptic, synaptic, and postsynaptic frequency sensitivities were all dependent on the firing rates of presynaptic and postsynaptic neurons. Firing rate changes, often assumed to be the propagating signal in these circuits, do not encode most oscillation frequencies, but instead determine which signal frequencies propagate effectively and which are suppressed.SIGNIFICANCE STATEMENT Oscillations are present in all the basal ganglia nuclei, include a range of frequencies, and change over the course of learning and behavior. Exaggerated oscillations are a hallmark of basal ganglia pathologies, and each has a specific frequency range. Because of its position as a hub in the basal ganglia circuitry, the globus pallidus is a candidate origin for oscillations propagating between nuclei. We imposed low-amplitude oscillations on individual globus pallidus neurons at specific frequencies and measured the coherence between the oscillation and firing as a function of frequency. We then used these responses to measure the effectiveness of oscillatory propagation to other basal ganglia nuclei. Propagation was effective for oscillation frequencies as high as 100 Hz.

Interactions between the Astrocytic Volume-Regulated Anion Channel and Aquaporin 4 in Hyposmotic Regulation of Vasopressin Neuronal Activity in the Supraoptic Nucleus.

We assessed interactions between the astrocytic volume-regulated anion channel (VRAC) and aquaporin 4 (AQP4) in the supraoptic nucleus (SON). Acute SON slices and cultures of hypothalamic astrocytes prepared from rats received hyposmotic challenge (HOC) with/without VRAC or AQP4 blockers. In acute slices, HOC caused an early decrease with a late rebound in the neuronal firing rate of vasopressin neurons, which required activity of astrocytic AQP4 and VRAC. HOC also caused a persistent decrease in the excitatory postsynaptic current frequency, supported by VRAC and AQP4 activity in early HOC; late HOC required only VRAC activity. These events were associated with the dynamics of glial fibrillary acidic protein (GFAP) filaments, the late retraction of which was mediated by VRAC activity; this activity also mediated an HOC-evoked early increase in AQP4 expression and late subside in GFAP-AQP4 colocalization. AQP4 activity supported an early HOC-evoked increase in VRAC levels and its colocalization with GFAP. In cultured astrocytes, late HOC augmented VRAC currents, the activation of which depended on AQP4 pre-HOC/HOC activity. HOC caused an early increase in VRAC expression followed by a late rebound, requiring AQP4 and VRAC, or only AQP4 activity, respectively. Astrocytic swelling in early HOC depended on AQP4 activity, and so did the early extension of GFAP filaments. VRAC and AQP4 activity supported late regulatory volume decrease, the retraction of GFAP filaments, and subside in GFAP-VRAC colocalization. Taken together, astrocytic morphological plasticity relies on the coordinated activities of VRAC and AQP4, which are mutually regulated in the astrocytic mediation of HOC-evoked modulation of vasopressin neuronal activity.

Effects of NALCN-Encoded Na+ Leak Currents on the Repetitive Firing Properties of SCN Neurons Depend on K+-Driven Rhythmic Changes in Input Resistance.

Neurons in the suprachiasmatic nucleus (SCN) generate circadian changes in the rates of spontaneous action potential firing that regulate and synchronize daily rhythms in physiology and behavior. Considerable evidence suggests that daily rhythms in the repetitive firing rates (higher during the day than at night) of SCN neurons are mediated by changes in subthreshold potassium (K+) conductance(s). An alternative "bicycle" model for circadian regulation of membrane excitability in clock neurons, however, suggests that an increase in NALCN-encoded sodium (Na+) leak conductance underlies daytime increases in firing rates. The experiments reported here explored the role of Na+ leak currents in regulating daytime and nighttime repetitive firing rates in identified adult male and female mouse SCN neurons: vasoactive intestinal peptide-expressing (VIP+), neuromedin S-expressing (NMS+) and gastrin-releasing peptide-expressing (GRP+) cells. Whole-cell recordings from VIP+, NMS+, and GRP+ neurons in acute SCN slices revealed that Na+ leak current amplitudes/densities are similar during the day and at night, but have a larger impact on membrane potentials in daytime neurons. Additional experiments, using an in vivo conditional knockout approach, demonstrated that NALCN-encoded Na+ currents selectively regulate daytime repetitive firing rates of adult SCN neurons. Dynamic clamp-mediated manipulation revealed that the effects of NALCN-encoded Na+ currents on the repetitive firing rates of SCN neurons depend on K+ current-driven changes in input resistances. Together, these findings demonstrate that NALCN-encoded Na+ leak channels contribute to regulating daily rhythms in the excitability of SCN neurons by a mechanism that depends on K+ current-mediated rhythmic changes in intrinsic membrane properties.SIGNIFICANCE STATEMENT Elucidating the ionic mechanisms responsible for generating daily rhythms in the rates of spontaneous action potential firing of neurons in the suprachiasmatic nucleus (SCN), the master circadian pacemaker in mammals, is an important step toward understanding how the molecular clock controls circadian rhythms in physiology and behavior. While numerous studies have focused on identifying subthreshold K+ channel(s) that mediate day-night changes in the firing rates of SCN neurons, a role for Na+ leak currents has also been suggested. The results of the experiments presented here demonstrate that NALCN-encoded Na+ leak currents differentially modulate daily rhythms in the daytime/nighttime repetitive firing rates of SCN neurons as a consequence of rhythmic changes in subthreshold K+ currents.

Nucleus accumbens shell neurons’ early sensitivity to cocaine is associated with future increases in drug intake

The striatum, both dorsal and ventral, is strongly implicated in substance use disorder. Chronic consumption of abused substances, such as cocaine, can cause an oversaturation of mesostriatal dopamine, which results in alterations in the firing of striatal neurons. While most preclinical studies of drug self-administration (S-A) are focused on these alterations, individual differences in a subject's early responses to drugs can also account for substantial differences in addiction susceptibility. In this study, we modeled longitudinal pharmacokinetics using data from a previous longitudinal study (Coffey et al., 2015) and aimed to determine if firing in specific dorsal and ventral striatal subregions was subject to changes across chronic cocaine S-A, and if individual animal differences in striatal firing in response to early drug exposure correlated with increases in drug intake. We observed that the firing patterns of nucleus accumbens (NAc) core and shell neurons exhibited increasing sensitivity to cocaine over the first 6 S-A sessions and maintained a strong negative correlation between drug intake and neuronal firing rates across chronic S-A. Moreover, we observed that the early sensitivity of NAc shell neurons to cocaine correlated with future increases in drug intake. Specifically, rats whose NAc shell neurons were most inhibited by increasing levels of cocaine upon first exposure exhibited the strongest increases in cocaine intake over time. If this difference can be linked to a genetic difference, or druggable targets, it may be possible to screen for similar addiction susceptibility in humans or develop novel preemptive pharmacotherapies.

Spike-frequency adaptation inhibits the pairwise spike correlation.

The spike train output correlation with pairwise neurons determines the neural population coding, which depends on the average firing rate of individual neurons. Spike frequency adaptation (SFA), which serves as an essential cellular encoding strategy, modulates the firing rates of individual neurons. However, the mechanism by which the SFA modulates the output correlation of the spike trains remains unclear. We introduce a pairwise neuron model that receives correlated inputs to generate spike trains, and the output correlation is qualified using Pearson correlation coefficient. The SFA is modeled using adaptation currents to examine its effect on the output correlation. Moreover, we use dynamic thresholds to explore the effect of SFA on output correlation. Furthermore, a simple phenomenological neuron model with a threshold-linear transfer function is utilized to confirm the effect of SFA on decreasing the output correlation. The results show that the adaptation currents decreased the output correlation by reducing the firing rate of a single neuron. At the onset of a correlated input, a transient process shows a decrease in interspike intervals (ISIs), resulting in a temporary increase in the correlation. When the adaptation current is sufficiently activated, the correlation reached a steady state, and the ISIs are maintained at higher values. The enhanced adaptation current achieved by increasing the adaptation conductance further reduces the pairwise correlation. While the time and slide windows influence the correlation, they make no difference in the effect of SFA on decreasing the output correlation. Moreover, SFA simulated by dynamic thresholds also decreases the output correlation. Furthermore, the simple phenomenological neuron model with a threshold-linear transfer function confirms the effect of SFA on decreasing the output correlation. The strength of the signal input and the slope of the linear component of the transfer function, the latter of which can be decreased by SFA, could together modulate the strength of the output correlation. Stronger SFA will decrease the slope and hence decrease the output correlation. The results reveal that the SFA reduces the output correlation with pairwise neurons in the network by reducing the firing rate of individual neurons. This study provides a link between cellular non-linear mechanisms and network coding strategies.

A Review of Neural Data and Modelling to Explain How a Semicircular Canal Dehiscence (SCD) Causes Enhanced VEMPs, Skull Vibration Induced Nystagmus (SVIN), and the Tullio Phenomenon

Angular acceleration stimulation of a semicircular canal causes an increased firing rate in primary canal afferent neurons that result in nystagmus in healthy adult animals. However, increased firing rate in canal afferent neurons can also be caused by sound or vibration in patients after a semicircular canal dehiscence, and so these unusual stimuli will also cause nystagmus. The recent data and model by Iversen and Rabbitt show that sound or vibration may increase firing rate either by neural activation locked to the individual cycles of the stimulus or by slow changes in firing rate due to fluid pumping (“acoustic streaming”), which causes cupula deflection. Both mechanisms will act to increase the primary afferent firing rate and so trigger nystagmus. The primary afferent data in guinea pigs indicate that in some situations, these two mechanisms may oppose each other. This review has shown how these three clinical phenomena—skull vibration-induced nystagmus, enhanced vestibular evoked myogenic potentials, and the Tullio phenomenon—have a common tie: they are caused by the new response of semicircular canal afferent neurons to sound and vibration after a semicircular canal dehiscence.

Multi-channel microelectrode arrays for detection of single-cell level neural information in the hippocampus CA1 under general anesthesia induced by low-dose isoflurane

Timely monitoring of anesthesia status during surgery is important to prevent an overdose of isoflurane anesthesia. Therefore, in-depth studies of the neural mechanisms of anesthetics are warranted. Hippocampal CA1 plays an important role during anesthesia. Currently, a high spatiotemporal resolution microdevice technology for the accurate detection of deep brain nuclei is lacking. In this research, four-shank 32-channel implantable microelectrode arrays (MEAs) were developed for the real-time recording of single-cell level neural information in rat hippocampal CA1. Platinum nanoparticles were modified onto the microelectrodes to substantially enhance the electrical properties of the microelectrode arrays. The modified MEAs exhibited low impedance (11.5 ± 1 kΩ) and small phase delay (-18.5 ± 2.54°), which enabled the MEAs to record single-cell level neural information with a high signal-to-noise ratio. The MEAs were implanted into the CA1 nuclei of the anesthetized rats, and the electrophysiological signals were recorded under different degrees of anesthesia mediated by low-dose concentrations of isoflurane. The recorded signals were analyzed in depth. Isoflurane caused an inhibition of spike firing rate in hippocampal CA1 neurons, while inducing low-frequency oscillations in CA1, thus enhancing the low-frequency power of local field potentials. In this manner, the spike firing rate and the power of local field potentials in CA1 could characterize the degree of isoflurane anesthesia. The present study provides a technical tool to study the neural mechanisms of isoflurane anesthesia and a research method for monitoring the depth of isoflurane anesthesia in clinical practice.

Developing a 3-D computational model of neurons in the central amygdala to understand pharmacological targets for pain.

Neuropathic and nociplastic pain are major causes of pain and involve brain areas such as the central nucleus of the amygdala (CeA). Within the CeA, neurons expressing protein kinase c-delta (PKCδ) or somatostatin (SST) have opposing roles in pain-like modulation. In this manuscript, we describe our progress towards developing a 3-D computational model of PKCδ and SST neurons in the CeA and the use of this model to explore the pharmacological targeting of these two neural populations in modulating nociception. Our 3-D model expands upon our existing 2-D computational framework by including a realistic 3-D spatial representation of the CeA and its subnuclei and a network of directed links that preserves morphological properties of PKCδ and SST neurons. The model consists of 13,000 neurons with cell-type specific properties and behaviors estimated from laboratory data. During each model time step, neuron firing rates are updated based on an external stimulus, inhibitory signals are transmitted between neurons via the network, and a measure of nociceptive output from the CeA is calculated as the difference in firing rates of pro-nociceptive PKCδ neurons and anti-nociceptive SST neurons. Model simulations were conducted to explore differences in output for three different spatial distributions of PKCδ and SST neurons. Our results show that the localization of these neuron populations within CeA subnuclei is a key parameter in identifying spatial and cell-type pharmacological targets for pain.