Variability, compensation and homeostasis in neuron and network function

On the Homology of the Dominant and Non-Dominant Corticospinal Tracts: A Novel Neurophysiological Assessment

OBJECTIVES

The homology of hemispheric cortical areas plays a crucial role in brain functionality. Here, we extend this concept to the homology of the dominant and non-dominant hemi-bodies, investigating the relationship of the two corticospinal tracts (CSTs). The evoked responses provide an estimate of the number of in-phase recruitments via their amplitude as a suitable indicator of the neuronal projections' integrity. An innovative concept derived from experience in the somatosensory system is that their morphology reflects the recruitment pattern of the whole circuit.

METHODS

CST homology was assessed via the Fréchet distance between the morphologies of motor-evoked potentials (MEPs) using a transcranial magnetic stimulation (TMS) in the homologous left- and right-hand first dorsal interosseous muscles of 40 healthy volunteers (HVs). We tested the working hypothesis that the inter-side Fréchet distance was higher than the two intra-side distances.

RESULTS

In addition to a clear confirmation of the working hypothesis (p < 0.0001 for both hemi-bodies) verified in all single subjects, we observed that the intra-side Fréchet distance was higher for the dominant than the non-dominant one. Interhemispheric morphology similarity increased with right-handedness prevalence (p = 0.004).

CONCLUSIONS

The newly introduced measure of circuit recruitment patterning represents a potential benchmark for the evaluation of inter-lateral mechanisms expressing the relationship between homologous hemilateral structures subtending learning and suggests that variability in recruitment patterning physiologically increases in circuits expressing greater functionality.

Modeling incomplete penetrance in long QT syndrome type 3 through ion channel heterogeneity: an in silico population study

Many cardiac diseases are characterized by an increased late sodium current, including heart failure, hypertrophic cardiomyopathy, and inherited long QT syndrome type 3 (LQT3). The late sodium current in LQT3 is caused by a gain-of-function mutation in the voltage-gated sodium channel Nav1.5. Despite a well-defined genetic cause of LQT3, treatment remains inconsistent because of incomplete penetrance of the mutation and variability of antiarrhythmic efficacy. Here, we investigate the relationship between LQT3-associated mutation incomplete penetrance and variability in ion channel expression, simulating a population of 1,000 individuals with the O'Hara-Rudy model of the human ventricular myocyte. We first simulate healthy electrical activity (i.e., in the absence of a mutation) and then incorporate heterozygous expression for three LQT3-associated mutations (Y1795C, I1768V, and ΔKPQ), to directly compare the effects of each mutation on individuals across a diverse population. For all mutations, we find that susceptibility, defined by either the presence of an early afterdepolarization (EAD) or prolonged action potential duration (APD), primarily depends on the balance between the conductance of IKr and INa, for which individuals with a higher IKr-to-INa ratio are less susceptible. Furthermore, we find distinct differences across the population, observing individuals susceptible to zero, one, two, or all three mutations. Individuals tend to be less susceptible with an appropriate balance of repolarizing currents, typically via increased IKs or IK1. Interestingly, the more critical repolarizing current is mutation specific. We conclude that the balance between key currents plays a significant role in mutant-specific presentation of the disease phenotype in LQT3.NEW & NOTEWORTHY An in silico population approach investigates the relationship between variability in ion channel expression and gain-of-function mutations in the voltage-gated sodium channel associated with the congenital disorder long QT syndrome type 3 (LQT3). We find that ion channel variability can contribute to incomplete penetrance of the mutation, with mutant-specific differences in ion channel conductances leading to susceptibility to proarrhythmic action potential duration prolongation or early afterdepolarizations.

Renal sympathetic activity: A key modulator of pressure natriuresis in hypertension

Hypertension is a complex disorder ensuing necessarily from alterations in the pressure-natriuresis relationship, the main determinant of long-term control of blood pressure. This mechanism sets natriuresis to the level of blood pressure, so that increasing pressure translates into higher osmotically driven diuresis to reduce volemia and control blood pressure. External factors affecting the renal handling of sodium regulate the pressure-natriuresis relationship so that more or less natriuresis is attained for each level of blood pressure. Hypertension can thus only develop following primary alterations in the pressure to natriuresis balance, or by abnormal activity of the regulation network. On the other hand, increased sympathetic tone is a very frequent finding in most forms of hypertension, long regarded as a key element in the pathophysiological scenario. In this article, we critically analyze the interplay of the renal component of the sympathetic nervous system and the pressure-natriuresis mechanism in the development of hypertension. A special focus is placed on discussing recent findings supporting a role of baroreceptors as a component, along with the afference of reno-renal reflex, of the input to the nucleus tractus solitarius, the central structure governing the long-term regulation of renal sympathetic efferent tone.

Charles Bonnet syndrome in patients with Stargardt disease: prevalence and risk factors

AIMS

To describe the prevalence of the Charles Bonnet syndrome (CBS) and search for potential CBS risk factors in a Dutch Stargardt disease (STGD1) cohort.

METHODS

Eighty-three patients with STGD1 were screened for CBS. They underwent a full eye examination. All patients completed the social functioning domain of the 36-Item Short Form Health Survey questionnaire. Participants suspected of CBS were interviewed to further evaluate their visual hallucinations.

RESULTS

CBS prevalence was 8.4%. Six out of seven patients with CBS were women. CBS was not associated with age (p=0.279, Mann-Whitney). Patients with CBS had a significant lower social functioning score (p<0.05, Mann-Whitney). All seven patients with CBS were in the category of vision impairment (visual acuity <6/12, but ≥3/60). Moreover, first hallucinations manifested after a drop in visual acuity. The retinal atrophic area of the worst eye tended to be lower in the CBS group (range 0.11-9.86 mm²) as compared with controls (range 0-180 mm²). There was no relation between the position of the scotoma and the location of the visual hallucinations.

CONCLUSION

The relative high CBS prevalence in STGD1 suggests that CBS may be more prevalent in younger ophthalmic patients than currently presumed. In this specific group of patients, we established social isolation and acquired vision impairment as risk factors for CBS. There was a female preponderance among patients with CBS. Age and retinal pigment epithelium atrophy were not identified as significant risk factors. We should actively diagnose CBS in patients of any age who fulfil the criteria for the category vision impairment, especially in cases where social isolation is suspected.

Galactic cosmic radiation exposure causes multifaceted neurocognitive impairments

Technological advancements have facilitated the implementation of realistic, terrestrial-based complex 33-beam galactic cosmic radiation simulations (GCR Sim) to now probe central nervous system functionality. This work expands considerably on prior, simplified GCR simulations, yielding new insights into responses of male and female mice exposed to 40-50 cGy acute or chronic radiations relevant to deep space travel. Results of the object in updated location task suggested that exposure to acute or chronic GCR Sim induced persistent impairments in hippocampus-dependent memory formation and reconsolidation in female mice that did not manifest robustly in irradiated male mice. Interestingly, irradiated male mice, but not females, were impaired in novel object recognition and chronically irradiated males exhibited increased aggressive behavior on the tube dominance test. Electrophysiology studies used to evaluate synaptic plasticity in the hippocampal CA1 region revealed significant reductions in long-term potentiation after each irradiation paradigm in both sexes. Interestingly, network-level disruptions did not translate to altered intrinsic electrophysiological properties of CA1 pyramidal cells, whereas acute exposures caused modest drops in excitatory synaptic signaling in males. Ultrastructural analyses of CA1 synapses found smaller postsynaptic densities in larger spines of chronically exposed mice compared to controls and acutely exposed mice. Myelination was also affected by GCR Sim with acutely exposed mice exhibiting an increase in the percent of myelinated axons; however, the myelin sheathes on small calibur (< 0.3 mm) and larger (> 0.5 mm) axons were thinner when compared to controls. Present findings might have been predicted based on previous studies using single and mixed beam exposures and provide further evidence that space-relevant radiation exposures disrupt critical cognitive processes and underlying neuronal network-level plasticity, albeit not to the extent that might have been previously predicted.

Early cognitive comorbidities before disease onset: A common symptom towards prevention of related brain diseases?

Brain diseases are very heterogeneous; however they also display multiple common risk factors and comorbidities. With a paucity of disease-modifying therapies, prevention became a health priority. Towards prevention, one strategy is to focus on similar symptoms of brain diseases occurring before disease onset. Cognitive deficits are a promising candidate as they occur across brain diseases before disease onset. Based on recent research, this review highlights the similarity of brain diseases and discusses how early cognitive deficits can be exploited to tackle disease prevention. After briefly introducing common risk factors, I review common comorbidities across brain diseases, with a focus on cognitive deficits before disease onset, reporting both experimental and clinical findings. Next, I describe network abnormalities associated with early cognitive deficits and discuss how these abnormalities can be targeted to prevent disease onset. A scenario on brain disease etiology with the idea that early cognitive deficits may constitute a common symptom of brain diseases is proposed.

Electrophysiological heterogeneity in large populations of rabbit ventricular cardiomyocytes

AIMS

Cardiac electrophysiological heterogeneity includes: (i) regional differences in action potential (AP) waveform, (ii) AP waveform differences in cells isolated from a single region, (iii) variability of the contribution of individual ion currents in cells with similar AP durations (APDs). The aim of this study is to assess intra-regional AP waveform differences, to quantify the contribution of specific ion channels to the APD via drug responses and to generate a population of mathematical models to investigate the mechanisms underlying heterogeneity in rabbit ventricular cells.

METHODS AND RESULTS

APD in ∼50 isolated cells from subregions of the LV free wall of rabbit hearts were measured using a voltage-sensitive dye. When stimulated at 2 Hz, average APD90 value in cells from the basal epicardial region was 254 ± 25 ms (mean ± standard deviation) in 17 hearts with a mean interquartile range (IQR) of 53 ± 17 ms. Endo-epicardial and apical-basal APD90 differences accounted for ∼10% of the IQR value. Highly variable changes in APD occurred after IK(r) or ICa(L) block that included a sub-population of cells (HR) with an exaggerated (hyper) response to IK(r) inhibition. A set of 4471 AP models matching the experimental APD90 distribution was generated from a larger population of models created by random variation of the maximum conductances (Gmax) of 8 key ion channels/exchangers/pumps. This set reproduced the pattern of cell-specific responses to ICa(L) and IK(r) block, including the HR sub-population. The models exhibited a wide range of Gmax values with constrained relationships linking ICa(L) with IK(r), ICl, INCX, and INaK.

CONCLUSION

Modelling the measured range of inter-cell APDs required a larger range of key Gmax values indicating that ventricular tissue has considerable inter-cell variation in channel/pump/exchanger activity. AP morphology is retained by relationships linking specific ionic conductances. These interrelationships are necessary for stable repolarization despite large inter-cell variation of individual conductances and this explains the variable sensitivity to ion channel block.

Long-term stability of single neuron activity in the motor system

How an established behavior is retained and consistently produced by a nervous system in constant flux remains a mystery. One possible solution to ensure long-term stability in motor output is to fix the activity patterns of single neurons in the relevant circuits. Alternatively, activity in single cells could drift over time provided that the population dynamics are constrained to produce the same behavior. To arbitrate between these possibilities, we recorded single-unit activity in motor cortex and striatum continuously for several weeks as rats performed stereotyped motor behaviors-both learned and innate. We found long-term stability in single neuron activity patterns across both brain regions. A small amount of drift in neural activity, observed over weeks of recording, could be explained by concomitant changes in task-irrelevant aspects of the behavior. These results suggest that long-term stable behaviors are generated by single neuron activity patterns that are themselves highly stable.

Transient active zone remodeling in the Drosophila mushroom body supports memory

Elucidating how the distinct components of synaptic plasticity dynamically orchestrate the distinct stages of memory acquisition and maintenance within neuronal networks remains a major challenge. Specifically, plasticity processes tuning the functional and also structural state of presynaptic active zone (AZ) release sites are widely observed in vertebrates and invertebrates, but their behavioral relevance remains mostly unclear. We here provide evidence that a transient upregulation of presynaptic AZ release site proteins supports aversive olfactory mid-term memory in the Drosophila mushroom body (MB). Upon paired aversive olfactory conditioning, AZ protein levels (ELKS-family BRP/(m)unc13-family release factor Unc13A) increased for a few hours with MB-lobe-specific dynamics. Kenyon cell (KC, intrinsic MB neurons)-specific knockdown (KD) of BRP did not affect aversive olfactory short-term memory (STM) but strongly suppressed aversive mid-term memory (MTM). Different proteins crucial for the transport of AZ biosynthetic precursors (transport adaptor Aplip1/Jip-1; kinesin motor IMAC/Unc104; small GTPase Arl8) were also specifically required for the formation of aversive olfactory MTM. Consistent with the merely transitory increase of AZ proteins, BRP KD did not interfere with the formation of aversive olfactory long-term memory (LTM; i.e., 1 day). Our data suggest that the remodeling of presynaptic AZ refines the MB circuitry after paired aversive conditioning, over a time window of a few hours, to display aversive olfactory memories.

Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction

Animals both explore and avoid novel objects in the environment, but the neural mechanisms that underlie these behaviors and their dynamics remain uncharacterized. Here, we used multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq) to characterize the behavior of mice freely interacting with a novel object. Novelty elicits a characteristic sequence of behavior, starting with investigatory approach and culminating in object engagement or avoidance. Dopamine in the tail of the striatum (TS) suppresses engagement, and dopamine responses were predictive of individual variability in behavior. Behavioral dynamics and individual variability are explained by a reinforcement-learning (RL) model of threat prediction in which behavior arises from a novelty-induced initial threat prediction (akin to "shaping bonus") and a threat prediction that is learned through dopamine-mediated threat prediction errors. These results uncover an algorithmic similarity between reward- and threat-related dopamine sub-systems.

Human iPSC-Derived Cortical Neurons Display Homeostatic Plasticity

Maintaining the excitability of neurons and circuits is fundamental for healthy brain functions. The global compensatory increase in excitatory synaptic strength, in response to decreased activity, is one of the main homeostatic mechanisms responsible for such regulation. This type of plasticity has been extensively characterized in rodents in vivo and in vitro, but few data exist on human neurons maturation. We have generated an in vitro cortical model system, based on differentiated human-induced pluripotent stem cells, chronically treated with tetrodotoxin, to investigate homeostatic plasticity at different developmental stages. Our findings highlight the presence of homeostatic plasticity in human cortical networks and show that the changes in synaptic strength are due to both pre- and post-synaptic mechanisms. Pre-synaptic plasticity involves the potentiation of neurotransmitter release machinery, associated to an increase in synaptic vesicle proteins expression. At the post-synaptic level, we report an increase in the expression of post-synaptic density proteins, involved in glutamatergic receptor anchoring. These results extend our understanding of neuronal homeostasis and reveal the developmental regulation of its expression in human cortical networks. Since induced pluripotent stem cell-derived neurons can be obtained from patients with neurodevelopmental and neurodegenerative diseases, our platform offers a versatile model for assessing human neural plasticity under physiological and pathological conditions.

Multimodal parameter spaces of a complex multi-channel neuron model

One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin-Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion channel parameter values produce similar outputs for the same input stimulus. Such phenomena result in an objective function that exhibits multiple modes (i.e., multiple local minima). This non-uniqueness of local optimality poses challenges for parameter estimation with many algorithmic optimization techniques. HH models additionally have severe non-linearities resulting in further challenges for inferring parameters in an algorithmic fashion. To address these challenges with a tractable method in high-dimensional parameter spaces, we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets. The visualized solution maps show new complex structures of the multimodal posteriors, and they allow for selection of locally and globally optimal value sets, and they visually expose parameter sensitivities and regions of higher model robustness. We envision these solution maps as enabling experimentalists to improve the design of future experiments, increase scientific productivity and improve on model structure and ideation when the MCMC algorithm is applied to experimental data.

Excitatory and inhibitory hippocampal neurons differ in their homeostatic adaptation to chronic M-channel modulation

A large body of studies has investigated bidirectional homeostatic plasticity both in vitro and in vivo using numerous pharmacological manipulations of activity or behavioral paradigms. However, these experiments rarely explored in the same cellular system the bidirectionality of the plasticity and simultaneously on excitatory and inhibitory neurons. M-channels are voltage-gated potassium channels that play a crucial role in regulating neuronal excitability and plasticity. In cultured hippocampal excitatory neurons, we previously showed that chronic exposure to the M-channel blocker XE991 leads to adaptative compensations, thereby triggering at different timescales intrinsic and synaptic homeostatic plasticity. This plastic adaptation barely occurs in hippocampal inhibitory neurons. In this study, we examined whether this homeostatic plasticity induced by M-channel inhibition was bidirectional by investigating the acute and chronic effects of the M-channel opener retigabine on hippocampal neuronal excitability. Acute retigabine exposure decreased excitability in both excitatory and inhibitory neurons. Chronic retigabine treatment triggered in excitatory neurons homeostatic adaptation of the threshold current and spontaneous firing rate at a time scale of 4–24 h. These plastic changes were accompanied by a substantial decrease in the M-current density and by a small, though significant, proximal relocation of Kv7.3-FGF14 segment along the axon initial segment. Thus, bidirectional homeostatic changes were observed in excitatory neurons though not symmetric in kinetics and mechanisms. Contrastingly, in inhibitory neurons, the compensatory changes in intrinsic excitability barely occurred after 48 h, while no homeostatic normalization of the spontaneous firing rate was observed. Our results indicate that excitatory and inhibitory hippocampal neurons differ in their adaptation to chronic alterations in neuronal excitability induced by M-channel bidirectional modulation.

Neuronal circuitry for recognition memory of object and place in rodent models

Rats and mice are used for studying neuronal circuits underlying recognition memory due to their ability to spontaneously remember the occurrence of an object, its place and an association of the object and place in a particular environment. A joint employment of lesions, pharmacological interventions, optogenetics and chemogenetics is constantly expanding our knowledge of the neural basis for recognition memory of object, place, and their association. In this review, we summarize current studies on recognition memory in rodents with a focus on the novel object preference, novel location preference and object-in-place paradigms. The evidence suggests that the medial prefrontal cortex- and hippocampus-connected circuits contribute to recognition memory for object and place. Under certain conditions, the striatum, medial septum, amygdala, locus coeruleus and cerebellum are also involved. We propose that the neuronal circuitry for recognition memory of object and place is hierarchically connected and constructed by different cortical (perirhinal, entorhinal and retrosplenial cortices), thalamic (nucleus reuniens, mediodorsal and anterior thalamic nuclei) and primeval (hypothalamus and interpeduncular nucleus) modules interacting with the medial prefrontal cortex and hippocampus.

Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning

Artificial neural networks (ANNs) have been successfully trained to perform a wide range of sensory-motor behaviors. In contrast, the performance of spiking neuronal network (SNN) models trained to perform similar behaviors remains relatively suboptimal. In this work, we aimed to push the field of SNNs forward by exploring the potential of different learning mechanisms to achieve optimal performance. We trained SNNs to solve the CartPole reinforcement learning (RL) control problem using two learning mechanisms operating at different timescales: (1) spike-timing-dependent reinforcement learning (STDP-RL) and (2) evolutionary strategy (EVOL). Though the role of STDP-RL in biological systems is well established, several other mechanisms, though not fully understood, work in concert during learning in vivo. Recreating accurate models that capture the interaction of STDP-RL with these diverse learning mechanisms is extremely difficult. EVOL is an alternative method and has been successfully used in many studies to fit model neural responsiveness to electrophysiological recordings and, in some cases, for classification problems. One advantage of EVOL is that it may not need to capture all interacting components of synaptic plasticity and thus provides a better alternative to STDP-RL. Here, we compared the performance of each algorithm after training, which revealed EVOL as a powerful method for training SNNs to perform sensory-motor behaviors. Our modeling opens up new capabilities for SNNs in RL and could serve as a testbed for neurobiologists aiming to understand multi-timescale learning mechanisms and dynamics in neuronal circuits.

Synaptic balancing: A biologically plausible local learning rule that provably increases neural network noise robustness without sacrificing task performance

We introduce a novel, biologically plausible local learning rule that provably increases the robustness of neural dynamics to noise in nonlinear recurrent neural networks with homogeneous nonlinearities. Our learning rule achieves higher noise robustness without sacrificing performance on the task and without requiring any knowledge of the particular task. The plasticity dynamics—an integrable dynamical system operating on the weights of the network—maintains a multiplicity of conserved quantities, most notably the network’s entire temporal map of input to output trajectories. The outcome of our learning rule is a synaptic balancing between the incoming and outgoing synapses of every neuron. This synaptic balancing rule is consistent with many known aspects of experimentally observed heterosynaptic plasticity, and moreover makes new experimentally testable predictions relating plasticity at the incoming and outgoing synapses of individual neurons. Overall, this work provides a novel, practical local learning rule that exactly preserves overall network function and, in doing so, provides new conceptual bridges between the disparate worlds of the neurobiology of heterosynaptic plasticity, the engineering of regularized noise-robust networks, and the mathematics of integrable Lax dynamical systems.

The precise pattern of synaptic connections in a neural network entirely determines the network’s function. Learning rules are programs for modifying synapses to allow the network to attain some functional goal. By carefully tweaking synaptic connectivity, neural networks are able learn new tasks, form new memories, and stabilize neural activity. Due to the complexity of network structure, there are many different synaptic weight patterns that can in principle perform the same computation. These different solutions, however, may be more or less desirable in other other ways, such as their vulnerability to unreliable components in the network. In this paper, we present a synaptic learning rule which locates robust solutions among many synaptic weight configurations that equivalently perform the same task. Our biologically plausible rule relies on information which is entirely locally available to each neuron and possesses a number of desirable mathematical properties. Our analysis reveals that networks are most robust when the aggregate incoming and outgoing synapses are balanced in every neuron. Intriguingly, our learning rule makes predictions which resemble widely experimentally observed patterns of compensatory heterosynaptic plasticity in cortical dendrites.

A negative feedback loop in the GPCR pathway underlies efficient coding of external stimuli

Efficient navigation based on chemical cues is an essential feature shared by all animals. These cues may be encountered in complex spatiotemporal patterns and with orders of magnitude varying intensities. Nevertheless, sensory neurons accurately extract the relevant information from such perplexing signals. Here, we show how a single sensory neuron in Caenorhabditis elegans animals can cell‐autonomously encode complex stimulus patterns composed of instantaneous sharp changes and of slowly changing continuous gradients. This encoding relies on a simple negative feedback in the G‐protein‐coupled receptor (GPCR) signaling pathway in which TAX‐6/Calcineurin plays a key role in mediating the feedback inhibition. This negative feedback supports several important coding features that underlie an efficient navigation strategy, including exact adaptation and adaptation to the magnitude of the gradient's first derivative. A simple mathematical model explains the fine neural dynamics of both wild‐type and tax‐6 mutant animals, further highlighting how the calcium‐dependent activity of TAX‐6/Calcineurin dictates GPCR inhibition and response dynamics. As GPCRs are ubiquitously expressed in all sensory neurons, this mechanism may be a general solution for efficient cell‐autonomous coding of external stimuli.

Small molecule modulation of the large-conductance calcium-activated potassium channel suppresses salicylate-induced tinnitus in mice

Tinnitus is the phantom perception of sound that has no external source. A neurological signature of tinnitus, and the frequently associated hyperacusis, is an imbalance between excitatory and inhibitory activity in the central auditory system (CAS), leading to dysregulated network excitability. The large conductance, calcium-activated potassium (BK) channel is a key player in pre- and post-synaptic excitability through its mediation of K⁺ currents. Changes in BK channel activity are associated with aberrant network activity in sensory regions of the CNS, raising the possibility that BK channel modulation could regulate activity associated with tinnitus and hyperacusis. To test whether BK channel openers are able to suppress biomarkers of drug-induced tinnitus and hyperacusis, the 1,3,4 oxadiazole BMS-191011 was given to young adult CBA mice that had been administered 250 mg/kg sodium salicylate (SS). Systemic treatment with BMS-191011 reduced behavioral manifestations of SS-induced tinnitus, but not hyperacusis, probed via the gap-in-noise startle response method. Systemic BMS-191011 treatment did not influence SS-induced increases in auditory brainstem response functions, but local application at the inferior colliculus did reverse SS-suppressed spontaneous activity, particularly in the frequency region of the tinnitus percept. Thus, action of BMS-191011 in the inferior colliculus may contribute to the reduction in behaviorally measured tinnitus. Together, these findings support the utility of BK channel openers in reducing central auditory processing changes associated with the formation of the tinnitus percept.

New insights from small rhythmic circuits

Small rhythmic circuits, such as those found in invertebrates, have provided fundamental insights into how circuit dynamics depend on individual neuronal and synaptic properties. Degenerate circuits are those with different network parameters and similar behavior. New work on degenerate circuits and their modulation illustrates some of the rules that help maintain stable and robust circuit function despite environmental perturbations. Advances in neuropeptide isolation and identification provide enhanced understanding of the neuromodulation of circuits for behavior. The advent of molecular studies of mRNA expression provides new insight into animal-to-animal variability and the homeostatic regulation of excitability in neurons and networks.

IGF-1 receptor regulates upward firing rate homeostasis via the mitochondrial calcium uniporter

An emerging hypothesis is that neuronal circuits homeostatically maintain a stable spike rate despite continuous environmental changes. This firing rate homeostasis is believed to confer resilience to neurodegeneration and cognitive decline. We show that insulin-like growth factor-1 receptor (IGF-1R) is necessary for homeostatic response of mean firing rate to inactivity, termed “upward firing rate homeostasis.” We show that its mechanism of action is to couple spike bursts with downstream mitochondrial Ca²⁺ influx via the mitochondrial calcium uniporter complex (MCUc). We propose that MCUc is a homeostatic Ca²⁺ sensor that triggers the integrated homeostatic response. Firing rate homeostasis may be the principal mechanism by which IGF-1R regulates aging and neurodevelopmental and neurodegenerative disorders.

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

Repeated applications of high potassium elicit long-term changes in a motor circuit from the crab, Cancer borealis

We examined the effects of altered extracellular potassium concentration on the output of the well-studied pyloric circuit in the crab, Cancer borealis. Pyloric neurons initially become quiescent, then recover spiking and bursting activity in high potassium saline (2.5x[K⁺]). These changes in circuit robustness are maintained after the perturbation is removed; pyloric neurons are more robust to subsequent potassium perturbations even after several hours of wash in control saline. Despite this long-term "memory" of the stimulus history, we found no differences in neuronal activity in control saline. The circuit's adaptation is erased by both low potassium saline (0.4x[K⁺]) and direct hyperpolarizing current. Initial sensitivity of PD neurons to high potassium saline also varies seasonally, indicating that changes in robustness may reflect natural changes in circuit states. Thus, perturbation, followed by recovery of normal activity, can hide cryptic changes in neuronal properties that are only revealed by subsequent challenges.

Multi-Stimuli-Responsive Synapse Based on Vertical van der Waals Heterostructures

Brain-inspired intelligent systems demand diverse neuromorphic devices beyond simple functionalities. Merging biomimetic sensing with weight-updating capabilities in artificial synaptic devices represents one of the key research focuses. Here, we report a multiresponsive synapse device that integrates synaptic and optical-sensing functions. The device adopts vertically stacked graphene/h-BN/WSe₂ heterostructures, including an ultrahigh-mobility readout layer, a weight-control layer, and a dual-stimuli-responsive layer. The unique structure endows synapse devices with excellent synaptic plasticity, short response time (3 μs), and excellent optical responsivity (10⁵ A/W). To demonstrate the application in neuromorphic computing, handwritten digit recognition was simulated based on an unsupervised spiking neural network (SNN) with a precision of 90.89%, well comparable with the state-of-the-art results. Furthermore, multiterminal neuromorphic devices are demonstrated to mimic dendritic integration and photoswitching logic. Different from other synaptic devices, the research work validates multifunctional integration in synaptic devices, supporting the potential fusion of sensing and self-learning in neuromorphic networks.

Representational drift: Emerging theories for continual learning and experimental future directions

Recent work has revealed that the neural activity patterns correlated with sensation, cognition, and action often are not stable and instead undergo large scale changes over days and weeks-a phenomenon called representational drift. Here, we highlight recent observations of drift, how drift is unlikely to be explained by experimental confounds, and how the brain can likely compensate for drift to allow stable computation. We propose that drift might have important roles in neural computation to allow continual learning, both for separating and relating memories that occur at distinct times. Finally, we present an outlook on future experimental directions that are needed to further characterize drift and to test emerging theories for drift's role in computation.

Neuroprotective Role of Lactoferrin during Early Brain Development and Injury through Lifespan

Early adverse fetal environments can significantly disturb central nervous system (CNS) development and subsequently alter brain maturation. Nutritional status is a major variable to be considered during development and increasing evidence links neonate and preterm infant impaired brain growth with neurological and psychiatric diseases in adulthood. Breastfeeding is one of the main components required for healthy newborn development due to the many "constitutive" elements breastmilk contains. Maternal intake of specific nutrients during lactation may alter milk composition, thus affecting newborn nutrition and, potentially, brain development. Lactoferrin (Lf) is a major protein present in colostrum and the main protein in human milk, which plays an important role in the benefits of breastfeeding during postnatal development. It has been demonstrated that Lf has antimicrobial, as well as anti-inflammatory properties, and is potentially able to reduce the incidence of sepsis and necrotizing enterocolitis (NEC), which are particularly frequent in premature births. The anti-inflammatory effects of Lf can reduce birth-related pathologies by decreasing the release of pro-inflammatory factors and inhibiting premature cervix maturation (also related to commensal microbiome abnormalities) that could contribute to disrupting brain development. Pre-clinical evidence shows that Lf protects the developing brain from neuronal injury, enhances brain connectivity and neurotrophin production, and decreases inflammation in models of perinatal inflammatory challenge, intrauterine growth restriction (IUGR) and neonatal hypoxia-ischemia (HI). In this context, Lf can provide nutritional support for brain development and cognition and prevent the origin of neuropsychiatric diseases later in life. In this narrative review, we consider the role of certain nutrients during neurodevelopment linking to the latest research on lactoferrin with respect to neonatology. We also discuss new evidence indicating that early neuroprotective pathways modulated by Lf could prevent neurodegeneration through anti-inflammatory and immunomodulatory processes.

Inactivity and Ca2+ signaling regulate synaptic compensation in motoneurons following hibernation in American bullfrogs

Neural networks tune synaptic and cellular properties to produce stable activity. One form of homeostatic regulation involves scaling the strength of synapses up or down in a global and multiplicative manner to oppose activity disturbances. In American bullfrogs, excitatory synapses scale up to regulate breathing motor function after inactivity in hibernation, connecting homeostatic compensation to motor behavior. In traditional models of homeostatic synaptic plasticity, inactivity is thought to increase synaptic strength via mechanisms that involve reduced Ca²⁺ influx through voltage-gated channels. Therefore, we tested whether pharmacological inactivity and inhibition of voltage-gated Ca²⁺ channels are sufficient to drive synaptic compensation in this system. For this, we chronically exposed ex vivo brainstem preparations containing the intact respiratory network to tetrodotoxin (TTX) to stop activity and nimodipine to block L-type Ca²⁺ channels. We show that hibernation and TTX similarly increased motoneuron synaptic strength and that hibernation occluded the response to TTX. In contrast, inhibiting L-type Ca²⁺ channels did not upregulate synaptic strength but disrupted the apparent multiplicative scaling of synaptic compensation typically observed in response to hibernation. Thus, inactivity drives up synaptic strength through mechanisms that do not rely on reduced L-type channel function, while Ca²⁺ signaling associated with the hibernation environment independently regulates the balance of synaptic weights. Altogether, these results point to multiple feedback signals for shaping synaptic compensation that gives rise to proper network function during environmental challenges in vivo.

The E3 ligase Thin controls homeostatic plasticity through neurotransmitter release repression

Synaptic proteins and synaptic transmission are under homeostatic control, but the relationship between these two processes remains enigmatic. Here, we systematically investigated the role of E3 ubiquitin ligases, key regulators of protein degradation-mediated proteostasis, in presynaptic homeostatic plasticity (PHP). An electrophysiology-based genetic screen of 157 E3 ligase-encoding genes at the Drosophila neuromuscular junction identified thin, an ortholog of human tripartite motif-containing 32 (TRIM32), a gene implicated in several neurological disorders, including autism spectrum disorder and schizophrenia. We demonstrate that thin functions presynaptically during rapid and sustained PHP. Presynaptic thin negatively regulates neurotransmitter release under baseline conditions by limiting the number of release-ready vesicles, largely independent of gross morphological defects. We provide genetic evidence that thin controls release through dysbindin, a schizophrenia-susceptibility gene required for PHP. Thin and Dysbindin localize in proximity within presynaptic boutons, and Thin degrades Dysbindin in vitro. Thus, the E3 ligase Thin links protein degradation-dependent proteostasis of Dysbindin to homeostatic regulation of neurotransmitter release.

Pareto optimality, economy-effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons

Neurons encounter unavoidable evolutionary trade-offs between multiple tasks. They must consume as little energy as possible while effectively fulfilling their functions. Cells displaying the best performance for such multi-task trade-offs are said to be Pareto optimal, with their ion channel configurations underpinning their functionality. Ion channel degeneracy, however, implies that multiple ion channel configurations can lead to functionally similar behaviour. Therefore, instead of a single model, neuroscientists often use populations of models with distinct combinations of ionic conductances. This approach is called population (database or ensemble) modelling. It remains unclear, which ion channel parameters in the vast population of functional models are more likely to be found in the brain. Here we argue that Pareto optimality can serve as a guiding principle for addressing this issue by helping to identify the subpopulations of conductance-based models that perform best for the trade-off between economy and functionality. In this way, the high-dimensional parameter space of neuronal models might be reduced to geometrically simple low-dimensional manifolds, potentially explaining experimentally observed ion channel correlations. Conversely, Pareto inference might also help deduce neuronal functions from high-dimensional Patch-seq data. In summary, Pareto optimality is a promising framework for improving population modelling of neurons and their circuits.

State-dependent activity dynamics of hypothalamic stress effector neurons

The stress response necessitates an immediate boost in vital physiological functions from their homeostatic operation to an elevated emergency response. However, the neural mechanisms underlying this state-dependent change remain largely unknown. Using a combination of in vivo and ex vivo electrophysiology with computational modeling, we report that corticotropin releasing hormone (CRH) neurons in the paraventricular nucleus of the hypothalamus (PVN), the effector neurons of hormonal stress response, rapidly transition between distinct activity states through recurrent inhibition. Specifically, in vivo optrode recording shows that under non-stress conditions, CRH_PVN neurons often fire with rhythmic brief bursts (RB), which, somewhat counterintuitively, constrains firing rate due to long (~2 s) interburst intervals. Stressful stimuli rapidly switch RB to continuous single spiking (SS), permitting a large increase in firing rate. A spiking network model shows that recurrent inhibition can control this activity-state switch, and more broadly the gain of spiking responses to excitatory inputs. In biological CRH_PVN neurons ex vivo, the injection of whole-cell currents derived from our computational model recreates the in vivo-like switch between RB and SS, providing direct evidence that physiologically relevant network inputs enable state-dependent computation in single neurons. Together, we present a novel mechanism for state-dependent activity dynamics in CRH_PVN neurons.

Molecular Determinants of Mechanical Itch Sensitization in Chronic Itch

Chronic itch is associated with sensitization of the somatosensory nervous system. Recent studies have identified the neural circuits transmitting acute itch; however, the mechanisms by which itch transforms into a pathological state remain largely unknown. We have previously shown that Aβ low-threshold mechanoreceptors, together with spinal urocortin 3-positive (Ucn3+) excitatory interneurons and neuropeptide Y-positive (NPY+) inhibitory interneurons, form a microcircuit that transmits and gates acute mechanical itch. Here, using whole-cell patch-clamp recordings, we observed increased excitability in spinal Ucn3+ neurons under chronic itch conditions. In contrast to Ucn3+ neurons, the excitability of spinal NPY+ neurons was largely reduced under chronic itch conditions. To explore the molecular mechanisms underlying sensitization of this microcircuit, we examined the mRNA expression levels of voltage-gated ion channels in recorded spinal Ucn3+ and NPY+ neurons by single-cell quantitative real-time PCR (qRT-PCR). We found that the expression levels of Nav1.6 and Cav2.3 channels were increased in spinal Ucn3+ neurons in chronic itch mice, while the expression level of SK3 channels was decreased. By contrast, the expression levels of Nav1.6 and BK channels were decreased in spinal NPY+ neurons in chronic itch mice. To determine the contribution of different ion channels in chronic itch sensitization, we then used a Markov Chain Monte Carlo method to parameterize a large number of biophysically distinct multicompartment models of Ucn3+ and NPY+ neurons. These models included explicit representations of the ion channels that we found to be up- or down-regulated under chronic itch conditions. Our models demonstrated that changes in Nav1.6 conductance are predominantly responsible for the changes in excitability of both Ucn3+ and NPY+ neurons during chronic itch pathogenesis. Furthermore, when simulating microcircuits of our Ucn3+ and NPY+ models, we found that reduced Nav1.6 conductance in NPY+ models played a major role in opening the itch gate under chronic itch conditions. However, changing SK, BK, or R-type calcium channel conductance had negligible effects on the sensitization of this circuit. Therefore, our results suggest that Nav1.6 channels may play an essential role in mechanical itch sensitization. The findings presented here may open a new avenue for developing pharmaceutical strategies to treat chronic itch.

Competency in Navigating Arbitrary Spaces as an Invariant for Analyzing Cognition in Diverse Embodiments

One of the most salient features of life is its capacity to handle novelty and namely to thrive and adapt to new circumstances and changes in both the environment and internal components. An understanding of this capacity is central to several fields: the evolution of form and function, the design of effective strategies for biomedicine, and the creation of novel life forms via chimeric and bioengineering technologies. Here, we review instructive examples of living organisms solving diverse problems and propose competent navigation in arbitrary spaces as an invariant for thinking about the scaling of cognition during evolution. We argue that our innate capacity to recognize agency and intelligence in unfamiliar guises lags far behind our ability to detect it in familiar behavioral contexts. The multi-scale competency of life is essential to adaptive function, potentiating evolution and providing strategies for top-down control (not micromanagement) to address complex disease and injury. We propose an observer-focused viewpoint that is agnostic about scale and implementation, illustrating how evolution pivoted similar strategies to explore and exploit metabolic, transcriptional, morphological, and finally 3D motion spaces. By generalizing the concept of behavior, we gain novel perspectives on evolution, strategies for system-level biomedical interventions, and the construction of bioengineered intelligences. This framework is a first step toward relating to intelligence in highly unfamiliar embodiments, which will be essential for progress in artificial intelligence and regenerative medicine and for thriving in a world increasingly populated by synthetic, bio-robotic, and hybrid beings.

Biophysical mechanism underlying compensatory preservation of neural synchrony over the adult lifespan

We propose that the preservation of functional integration, estimated from measures of neural synchrony, is a key objective of neurocompensatory mechanisms associated with healthy human ageing. To support this proposal, we demonstrate how phase-locking at the peak alpha frequency in Magnetoencephalography recordings remains invariant over the lifespan in a large cohort of human participants, aged 18-88 years. Using empirically derived connection topologies from diffusion tensor imaging data, we create an in-silico model of whole-brain alpha dynamics. We show that enhancing inter-areal coupling can cancel the effect of increased axonal transmission delays associated with age-related degeneration of white matter tracts, albeit at slower network frequencies. By deriving analytical solutions for simplified connection topologies, we further establish the theoretical principles underlying compensatory network re-organization. Our findings suggest that frequency slowing with age- frequently observed in the alpha band in diverse populations- may be viewed as an epiphenomenon of the underlying compensatory mechanism.

Analysis of MEG data from healthy participants and whole-brain network modeling suggests that the brain compensates for age-related disruptions in connectivity by slowing down the frequency of neural synchronization.

Mechanisms Driving the Emergence of Neuronal Hyperexcitability in Fragile X Syndrome

Hyperexcitability is a shared neurophysiological phenotype across various genetic neurodevelopmental disorders, including Fragile X syndrome (FXS). Several patient symptoms are associated with hyperexcitability, but a puzzling feature is that their onset is often delayed until their second and third year of life. It remains unclear how and why hyperexcitability emerges in neurodevelopmental disorders. FXS is caused by the loss of FMRP, an RNA-binding protein which has many critical roles including protein synthesis-dependent and independent regulation of ion channels and receptors, as well as global regulation of protein synthesis. Here, we discussed recent literature uncovering novel mechanisms that may drive the progressive onset of hyperexcitability in the FXS brain. We discussed in detail how recent publications have highlighted defects in homeostatic plasticity, providing new insight on the FXS brain and suggest pharmacotherapeutic strategies in FXS and other neurodevelopmental disorders.

Loss of neuronal heterogeneity in epileptogenic human tissue impairs network resilience to sudden changes in synchrony

A myriad of pathological changes associated with epilepsy can be recast as decreases in cell and circuit heterogeneity. We thus propose recontextualizing epileptogenesis as a process where reduction in cellular heterogeneity, in part, renders neural circuits less resilient to seizure. By comparing patch clamp recordings from human layer 5 (L5) cortical pyramidal neurons from epileptogenic and non-epileptogenic tissue, we demonstrate significantly decreased biophysical heterogeneity in seizure-generating areas. Implemented computationally, this renders model neural circuits prone to sudden transitions into synchronous states with increased firing activity, paralleling ictogenesis. This computational work also explains the surprising finding of significantly decreased excitability in the population-activation functions of neurons from epileptogenic tissue. Finally, mathematical analyses reveal a bifurcation structure arising only with low heterogeneity and associated with seizure-like dynamics. Taken together, this work provides experimental, computational, and mathematical support for the theory that ictogenic dynamics accompany a reduction in biophysical heterogeneity.

Approaches to Parameter Estimation from Model Neurons and Biological Neurons

Model optimization in neuroscience has focused on inferring intracellular parameters from time series observations of the membrane voltage and calcium concentrations. These parameters constitute the fingerprints of ion channel subtypes and may identify ion channel mutations from observed changes in electrical activity. A central question in neuroscience is whether computational methods may obtain ion channel parameters with sufficient consistency and accuracy to provide new information on the underlying biology. Finding single-valued solutions in particular, remains an outstanding theoretical challenge. This note reviews recent progress in the field. It first covers well-posed problems and describes the conditions that the model and data need to meet to warrant the recovery of all the original parameters—even in the presence of noise. The main challenge is model error, which reflects our lack of knowledge of exact equations. We report on strategies that have been partially successful at inferring the parameters of rodent and songbird neurons, when model error is sufficiently small for accurate predictions to be made irrespective of stimulation.

Homeostatic plasticity and excitation-inhibition balance: The good, the bad, and the ugly

In this review, we discuss the significance of the synaptic excitation/inhibition (E/I) balance in the context of homeostatic plasticity, whose primary goal is thought to maintain neuronal firing rates at a set point. We first provide an overview of the processes through which patterned input activity drives synaptic E/I tuning and maturation of circuits during development. Next, we emphasize the importance of the E/I balance at the synaptic level (homeostatic control of message reception) as a means to achieve the goal (homeostatic control of information transmission) at the network level and consider how compromised homeostatic plasticity associated with neurological diseases leads to hyperactivity, network instability, and ultimately improper information processing. Lastly, we highlight several pathological conditions related to sensory deafferentation and describe how, in some cases, homeostatic compensation without appropriate sensory inputs can result in phantom perceptions.

SAPAP3 regulates epileptic seizures involving GluN2A in post-synaptic densities

Aberrantly synchronized neuronal discharges in the brain lead to epilepsy, a devastating neurological disease whose pathogenesis and mechanism are unclear. SAPAP3, a cytoskeletal protein expressed at high levels in the postsynaptic density (PSD) of excitatory synapses, has been well studied in the striatum, but the role of SAPAP3 in epilepsy remains elusive. In this study, we sought to investigate the molecular, cellular, electrophysiological and behavioral consequences of SAPAP3 perturbations in the mouse hippocampus. We identified a significant increase in the SAPAP3 levels in patients with temporal lobe epilepsy (TLE) and in mouse models of epilepsy. In addition, behavioral studies showed that the downregulation of SAPAP3 by shRNA decreased the seizure severity and that the overexpression of SAPAP3 by recombinant SAPAP3 yielded the opposite effect. Moreover, SAPAP3 affected action potentials (APs), miniature excitatory postsynaptic currents (mEPSCs) and N-methyl-D-aspartate receptor (NMDAR)-mediated currents in the CA1 region, which indicated that SAPAP3 plays an important role in excitatory synaptic transmission. Additionally, the levels of the GluN2A protein, which is involved in synaptic function, were perturbed in the hippocampal PSD, and this perturbation was accompanied by ultrastructural morphological changes. These results revealed a previously unknown function of SAPAP3 in epileptogenesis and showed that SAPAP3 may represent a novel target for the treatment of epilepsy.

Higher Responsiveness of Pattern Generation Circuitry to Sensory Stimulation in Healthy Humans Is Associated with a Larger Hoffmann Reflex

Simple Summary

Individual differences in the sensorimotor circuitry play an important role for understanding the nature of behavioral variability and developing personalized therapies. While the spinal network likely requires relatively rigid organization, it becomes increasingly evident that adaptability and inter-individual variability in the functioning of the neuronal circuitry is present not only in the brain but also in the spinal cord. In this study we investigated the relationship between the excitability of pattern generation circuitry and segmental reflexes in healthy humans. We found that the high individual responsiveness of pattern generation circuitries to tonic sensory input in both the upper and lower limbs was related to larger H-reflexes. The results provide further evidence for the importance of physiologically relevant assessments of spinal cord neuromodulation and the individual physiological state of reflex pathways.

Abstract

The state and excitability of pattern generators are attracting the increasing interest of neurophysiologists and clinicians for understanding the mechanisms of the rhythmogenesis and neuromodulation of the human spinal cord. It has been previously shown that tonic sensory stimulation can elicit non-voluntary stepping-like movements in non-injured subjects when their limbs were placed in a gravity-neutral unloading apparatus. However, large individual differences in responsiveness to such stimuli were observed, so that the effects of sensory neuromodulation manifest only in some of the subjects. Given that spinal reflexes are an integral part of the neuronal circuitry, here we investigated the extent to which spinal pattern generation excitability in response to the vibrostimulation of muscle proprioceptors can be related to the H-reflex magnitude, in both the lower and upper limbs. For the H-reflex measurements, three conditions were used: stationary limbs, voluntary limb movement and passive limb movement. The results showed that the H-reflex was considerably higher in the group of participants who demonstrated non-voluntary rhythmic responses than it was in the participants who did not demonstrate them. Our findings are consistent with the idea that spinal reflex measurements play important roles in assessing the rhythmogenesis of the spinal cord.

Intrinsic excitability mechanisms of neuronal ensemble formation

Neuronal ensembles are coactive groups of cortical neurons, found in spontaneous and evoked activity, that can mediate perception and behavior. To understand the mechanisms that lead to the formation of ensembles, we co-activated layer 2/3 pyramidal neurons in brain slices from mouse visual cortex, in animals of both sexes, replicating in vitro an optogenetic protocol to generate ensembles in vivo. Using whole-cell and perforated patch-clamp pair recordings we found that, after optogenetic or electrical stimulation, coactivated neurons increased their correlated activity, a hallmark of ensemble formation. Coactivated neurons showed small biphasic changes in presynaptic plasticity, with an initial depression followed by a potentiation after a recovery period. Optogenetic and electrical stimulation also induced significant increases in frequency and amplitude of spontaneous EPSPs, even after single-cell stimulation. In addition, we observed unexpected strong and persistent increases in neuronal excitability after stimulation, with increases in membrane resistance and reductions in spike threshold. A pharmacological agent that blocks changes in membrane resistance reverted this effect. These significant increases in excitability can explain the observed biphasic synaptic plasticity. We conclude that cell-intrinsic changes in excitability are involved in the formation of neuronal ensembles. We propose an ‘iceberg’ model, by which increased neuronal excitability makes subthreshold connections suprathreshold, enhancing the effect of already existing synapses, and generating a new neuronal ensemble.

In the brain, groups of neurons that are activated together – also known as neuronal ensembles – are the basic units that underpin perception and behavior. Yet, exactly how these coactive circuits are established remains under investigation.

In 1949, Canadian psychologist Donald Hebb proposed that, when brains learn something new, the neurons which are activated together connect to form ensembles, and their connections become stronger each time this specific piece of knowledge is recalled. This idea that ‘neurons that fire together, wire together’ can explain how memories are acquired and recalled, by strengthening their wiring.

However, recent studies have questioned whether strengthening connections is the only mechanism by which neural ensembles can be created. Changes in the excitability of neurons (how easily they are to fire and become activated) may also play a role. In other words, ensembles could emerge because certain neurons become more excitable and fire more readily.

To solve this conundrum, Alejandre-García et al. examined both hypotheses in the same system. Neurons in slices of the mouse visual cortex were stimulated electrically or optically, via a technique that controls neural activity with light. The activity of individual neurons and their connections was then measured with electrodes.

Spontaneous activity among connected neurons increased after stimulation, indicative of the formation of neuronal ensembles. Connected neurons also showed small changes in the strength of their connections, which first decreased and then rebounded after an initial recovery period.

Intriguingly, cells also showed unexpected strong and persistent increases in neuronal excitability after stimulation, such that neurons fired more readily to the same stimulus. In other words, neurons maintained a cellular memory of having been stimulated. The authors conclude that ensembles form because connected neurons become more excitable, which in turn, may strengthen connections of the circuit at a later stage.

These results provide fresh insights about the neural circuits underpinning learning and memory. In time, the findings could also help to understand disorders such as Alzheimer’s disease and schizophrenia, which are characterised by memory impairments and disordered thinking.

Neural mechanisms to exploit positional geometry for collision avoidance

Visual motion provides rich geometrical cues about the three-dimensional configuration of the world. However, how brains decode the spatial information carried by motion signals remains poorly understood. Here, we study a collision-avoidance behavior in Drosophila as a simple model of motion-based spatial vision. With simulations and psychophysics, we demonstrate that walking Drosophila exhibit a pattern of slowing to avoid collisions by exploiting the geometry of positional changes of objects on near-collision courses. This behavior requires the visual neuron LPLC1, whose tuning mirrors the behavior and whose activity drives slowing. LPLC1 pools inputs from object and motion detectors, and spatially biased inhibition tunes it to the geometry of collisions. Connectomic analyses identified circuitry downstream of LPLC1 that faithfully inherits its response properties. Overall, our results reveal how a small neural circuit solves a specific spatial vision task by combining distinct visual features to exploit universal geometrical constraints of the visual world.

Initial Variability and Time-Dependent Changes of Neuronal Response Features Are Cell-Type-Specific

Different cell types are commonly defined by their distinct response features. But several studies proved substantial variability between cells of the same type, suggesting rather the appraisal of response feature distributions than a limitation to "typical" responses. Moreover, there is growing evidence that time-dependent changes of response features contribute to robust and functional network output in many neuronal systems. The individually characterized Touch (T), Pressure (P), and Retzius (Rz) cells in the medicinal leech allow for a rigid analysis of response features, elucidating differences between and variability within cell types, as well as their changes over time. The initial responses of T and P cells to somatic current injection cover a wide range of spike counts, and their first spike is generated with a high temporal precision after a short latency. In contrast, all Rz cells elicit very similar low spike counts with variable, long latencies. During prolonged electrical stimulation the resting membrane potential of all three cell types hyperpolarizes. At the same time, Rz cells reduce their spiking activity as expected for a departure from the spike threshold. In contrast, both mechanoreceptor types increase their spike counts during repeated stimulation, consistent with previous findings in T cells. A control experiment reveals that neither a massive current stimulation nor the hyperpolarization of the membrane potential is necessary for the mechanoreceptors' increase in excitability over time. These findings challenge the previously proposed involvement of slow K+-channels in the time-dependent activity changes. We also find no indication for a run-down of HCN channels over time, and a rigid statistical analysis contradicts several potential experimental confounders as the basis of the observed variability. We conclude that the time-dependent change in excitability of T and P cells could indicate a cell-type-specific shift between different spiking regimes, which also could explain the high variability in the initial responses. The underlying mechanism needs to be further investigated in more naturalistic experimental situations to disentangle the effects of varying membrane properties versus network interactions. They will show if variability in individual response features serves as flexible adaptation to behavioral contexts rather than just "randomness".

TRPM3-mediated dynamic mitochondrial activity in NGF-induced latent sensitization of chronic low back pain

ABSTRACT

The transient receptor potential ion channel TRPM3 is highly prevalent on nociceptive dorsal root ganglion (DRG) neurons, but its functions in neuronal plasticity of chronic pain remain obscure. In an animal model of nonspecific low back pain (LBP), latent spinal sensitization known as nociceptive priming is induced by nerve growth factor (NGF) injection. Here we address the TRPM3-associated molecular basis of NGF-induced latent spinal sensitization at presynaptic level by studying TRPM3-mediated calcium transients in DRG neurons. By investigating TRPM3-expressing HEK cells, we further show the dynamic mitochondrial activity downstream of TRPM3 activation. NGF enhances TRPM3 function, attenuates TRPM3 tachyphylaxis, and slows intracellular calcium clearance; TRPM3 activation triggers more mitochondrial calcium loading than depolarization does, causing a steady-state mitochondrial calcium elevation and a delayed recovery of cytosolic calcium; mitochondrial calcium buffering accounts for approximately 40% of calcium influx subsequent to TRPM3 activation. TRPM3 activation provokes an outbreak of pulsatile superoxide production (mitoflash) that comes in the form of a surge in frequency being tunable. We suggest that mitoflash pulsations downstream of TRPM3 activation might be an early signaling event initiating pain sensitization. Tuning of mitoflash activity would be a novel bottom-up therapeutic strategy for chronic pain conditions such as LBP and beyond.

Synaptic Variability and Cortical Gamma Oscillation Power in Schizophrenia

OBJECTIVE

Cognitive impairments in schizophrenia are associated with lower gamma oscillation power in the prefrontal cortex (PFC). Gamma power depends in part on excitatory drive to fast-spiking parvalbumin interneurons (PVIs). Excitatory drive to cortical neurons varies in strength, which could affect how these neurons regulate network oscillations. The authors investigated whether variability in excitatory synaptic strength across PVIs could contribute to lower prefrontal gamma power in schizophrenia.

METHODS

In postmortem PFC from 20 matched pairs of comparison and schizophrenia subjects, levels of vesicular glutamate transporter 1 (VGlut1) and postsynaptic density 95 (PSD95) proteins were quantified to assess variability in excitatory synaptic strength across PVIs. A computational model network was then used to simulate how variability in excitatory synaptic strength across fast-spiking (a defining feature of PVIs) interneurons (FSIs) regulates gamma power.

RESULTS

The variability of VGlut1 and PSD95 levels at excitatory inputs across PVIs was larger in schizophrenia relative to comparison subjects. This alteration was not influenced by schizophrenia-associated comorbid factors, was not present in monkeys chronically exposed to antipsychotic medications, and was not present in calretinin interneurons. In the model network, variability in excitatory synaptic strength across FSIs regulated gamma power by affecting network synchrony. Finally, greater synaptic variability interacted synergistically with other synaptic alterations in schizophrenia (i.e., fewer excitatory inputs to FSIs and lower inhibitory strength from FSIs) to robustly reduce gamma power.

CONCLUSIONS

The study findings suggest that greater variability in excitatory synaptic strength across PVIs, in combination with other modest synaptic alterations in these neurons, can markedly lower PFC gamma power in schizophrenia.

Endosomal phosphatidylinositol 3-phosphate controls synaptic vesicle cycling and neurotransmission

Neural circuit function requires mechanisms for controlling neurotransmitter release and the activity of neuronal networks, including modulation by synaptic contacts, synaptic plasticity, and homeostatic scaling. However, how neurons intrinsically monitor and feedback control presynaptic neurotransmitter release and synaptic vesicle (SV) recycling to restrict neuronal network activity remains poorly understood at the molecular level. Here, we investigated the reciprocal interplay between neuronal endosomes, organelles of central importance for the function of synapses, and synaptic activity. We show that elevated neuronal activity represses the synthesis of endosomal lipid phosphatidylinositol 3-phosphate [PI(3)P] by the lipid kinase VPS34. Neuronal activity in turn is regulated by endosomal PI(3)P, the depletion of which reduces neurotransmission as a consequence of perturbed SV endocytosis. We find that this mechanism involves Calpain 2-mediated hyperactivation of Cdk5 downstream of receptor- and activity-dependent calcium influx. Our results unravel an unexpected function for PI(3)P-containing neuronal endosomes in the control of presynaptic vesicle cycling and neurotransmission, which may explain the involvement of the PI(3)P-producing VPS34 kinase in neurological disease and neurodegeneration.

Minimal requirements for a neuron to co-regulate many properties and the implications for ion channel correlations and robustness

Neurons regulate their excitability by adjusting their ion channel levels. Degeneracy – achieving equivalent outcomes (excitability) using different solutions (channel combinations) – facilitates this regulation by enabling a disruptive change in one channel to be offset by compensatory changes in other channels. But neurons must coregulate many properties. Pleiotropy – the impact of one channel on more than one property – complicates regulation because a compensatory ion channel change that restores one property to its target value often disrupts other properties. How then does a neuron simultaneously regulate multiple properties? Here, we demonstrate that of the many channel combinations producing the target value for one property (the single-output solution set), few combinations produce the target value for other properties. Combinations producing the target value for two or more properties (the multioutput solution set) correspond to the intersection between single-output solution sets. Properties can be effectively coregulated only if the number of adjustable channels (n_in) exceeds the number of regulated properties (n_out). Ion channel correlations emerge during homeostatic regulation when the dimensionality of solution space (n_in − n_out) is low. Even if each property can be regulated to its target value when considered in isolation, regulation as a whole fails if single-output solution sets do not intersect. Our results also highlight that ion channels must be coadjusted with different ratios to regulate different properties, which suggests that each error signal drives modulatory changes independently, despite those changes ultimately affecting the same ion channels.

Towards an efficient validation of dynamical whole-brain models

Simulating the resting-state brain dynamics via mathematical whole-brain models requires an optimal selection of parameters, which determine the model’s capability to replicate empirical data. Since the parameter optimization via a grid search (GS) becomes unfeasible for high-dimensional models, we evaluate several alternative approaches to maximize the correspondence between simulated and empirical functional connectivity. A dense GS serves as a benchmark to assess the performance of four optimization schemes: Nelder-Mead Algorithm (NMA), Particle Swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Bayesian Optimization (BO). To compare them, we employ an ensemble of coupled phase oscillators built upon individual empirical structural connectivity of 105 healthy subjects. We determine optimal model parameters from two- and three-dimensional parameter spaces and show that the overall fitting quality of the tested methods can compete with the GS. There are, however, marked differences in the required computational resources and stability properties, which we also investigate before proposing CMAES and BO as efficient alternatives to a high-dimensional GS. For the three-dimensional case, these methods generated similar results as the GS, but within less than 6% of the computation time. Our results contribute to an efficient validation of models for personalized simulations of brain dynamics.

The way forward for cognition in Parkinson's disease

This is the concluding chapter in our volume on cognition in Parkinson's disease.

Cognitive Control as a Multivariate Optimization Problem

A hallmark of adaptation in humans and other animals is our ability to control how we think and behave across different settings. Research has characterized the various forms cognitive control can take-including enhancement of goal-relevant information, suppression of goal-irrelevant information, and overall inhibition of potential responses-and has identified computations and neural circuits that underpin this multitude of control types. Studies have also identified a wide range of situations that elicit adjustments in control allocation (e.g., those eliciting signals indicating an error or increased processing conflict), but the rules governing when a given situation will give rise to a given control adjustment remain poorly understood. Significant progress has recently been made on this front by casting the allocation of control as a decision-making problem. This approach has developed unifying and normative models that prescribe when and how a change in incentives and task demands will result in changes in a given form of control. Despite their successes, these models, and the experiments that have been developed to test them, have yet to face their greatest challenge: deciding how to select among the multiplicity of configurations that control can take at any given time. Here, we will lay out the complexities of the inverse problem inherent to cognitive control allocation, and their close parallels to inverse problems within motor control (e.g., choosing between redundant limb movements). We discuss existing solutions to motor control's inverse problems drawn from optimal control theory, which have proposed that effort costs act to regularize actions and transform motor planning into a well-posed problem. These same principles may help shed light on how our brains optimize over complex control configuration, while providing a new normative perspective on the origins of mental effort.

Self-healing codes: How stable neural populations can track continually reconfiguring neural representations

The brain is capable of adapting while maintaining stable long-term memories and learned skills. Recent experiments show that neural responses are highly plastic in some circuits, while other circuits maintain consistent responses over time, raising the question of how these circuits interact coherently. We show how simple, biologically motivated Hebbian and homeostatic mechanisms in single neurons can allow circuits with fixed responses to continuously track a plastic, changing representation without reference to an external learning signal.

As an adaptive system, the brain must retain a faithful representation of the world while continuously integrating new information. Recent experiments have measured population activity in cortical and hippocampal circuits over many days and found that patterns of neural activity associated with fixed behavioral variables and percepts change dramatically over time. Such “representational drift” raises the question of how malleable population codes can interact coherently with stable long-term representations that are found in other circuits and with relatively rigid topographic mappings of peripheral sensory and motor signals. We explore how known plasticity mechanisms can allow single neurons to reliably read out an evolving population code without external error feedback. We find that interactions between Hebbian learning and single-cell homeostasis can exploit redundancy in a distributed population code to compensate for gradual changes in tuning. Recurrent feedback of partially stabilized readouts could allow a pool of readout cells to further correct inconsistencies introduced by representational drift. This shows how relatively simple, known mechanisms can stabilize neural tuning in the short term and provides a plausible explanation for how plastic neural codes remain integrated with consolidated, long-term representations.

Reciprocally inhibitory circuits operating with distinct mechanisms are differently robust to perturbation and modulation

Reciprocal inhibition is a building block in many sensory and motor circuits. We studied the features that underly robustness in reciprocally inhibitory two neuron circuits. We used the dynamic clamp to create reciprocally inhibitory circuits from pharmacologically isolated neurons of the crab stomatogastric ganglion by injecting artificial graded synaptic (I_Syn) and hyperpolarization-activated inward (I_H) currents. There is a continuum of mechanisms in circuits that generate antiphase oscillations, with ‘release’ and ‘escape’ mechanisms at the extremes, and mixed mode oscillations between these extremes. In release, the active neuron primarily controls the off/on transitions. In escape, the inhibited neuron controls the transitions. We characterized the robustness of escape and release circuits to alterations in circuit parameters, temperature, and neuromodulation. We found that escape circuits rely on tight correlations between synaptic and H conductances to generate bursting but are resilient to temperature increase. Release circuits are robust to variations in synaptic and H conductances but fragile to temperature increase. The modulatory current (I_MI) restores oscillations in release circuits but has little effect in escape circuits. Perturbations can alter the balance of escape and release mechanisms and can create mixed mode oscillations. We conclude that the same perturbation can have dramatically different effects depending on the circuits’ mechanism of operation that may not be observable from basal circuit activity.

Neurocoagulation from a Mechanistic Point of View in the Central Nervous System

Coagulation mechanisms are critical for maintaining homeostasis in the central nervous system (CNS). Thrombin, an important player of the coagulation cascade, activates protease activator receptors (PARs), members of the G-protein coupled receptor family. PAR1 is located on neurons and glia. Following thrombin activation, PAR1 signals through the extracellular signal-regulated kinase pathway, causing alterations in neuronal glutamate release and astrocytic morphological changes. Similarly, the anticoagulation factor activated protein C (aPC) can cleave PAR1, following interaction with the endothelial protein C receptor. Both thrombin and aPC are expressed on endothelial cells and pericytes in the blood-brain barrier (BBB). Thrombin-induced PAR1 activation increases cytosolic Ca²⁺ concentration in brain vessels, resulting in nitric oxide release and increasing F-actin stress fibers, damaging BBB integrity. aPC also induces PAR1 activation and preserves BBB vascular integrity via coupling to sphingosine 1 phosphate receptors. Thrombin-induced PAR1 overactivation and BBB disruption are evident in CNS pathologies. During epileptic seizures, BBB disruption promotes thrombin penetration. Thrombin induces PAR1 activation and potentiates N-methyl-D-aspartate receptors, inducing glutamate-mediated hyperexcitability. Specific PAR1 inhibition decreases status epilepticus severity in vivo. In stroke, the elevation of brain thrombin levels further compromises BBB integrity, with direct parenchymal damage, while systemic factor Xa inhibition improves neurological outcomes. In multiple sclerosis (MS), brain thrombin inhibitory capacity correlates with clinical presentation. Both thrombin inhibition by hirudin and the use of recombinant aPC improve disease severity in an MS animal model. This review presents the mechanisms underlying the effects of coagulation on the physiology and pathophysiology of the CNS.