Variability, compensation, and modulation in neurons and circuits

Dynamic Input Conductances Shape Neuronal Spiking

Reliable neuron activity is ensured by a tight regulation of the ion channels that resides in the neuron’s membrane. Understanding the causal mechanisms that relate this regulation to physiological and pathological neuronal activity is a necessary step for developing efficient therapies for neurological diseases associated with abnormal nervous system activity.

Assessing the role of biophysical parameter variations in neuronal activity is critical to the understanding of modulation, robustness, and homeostasis of neuronal signalling. The paper proposes that this question can be addressed through the analysis of dynamic input conductances. Those voltage-dependent curves aggregate the concomitant activity of all ion channels in distinct timescales. They are shown to shape the current−voltage dynamical relationships that determine neuronal spiking. We propose an experimental protocol to measure dynamic input conductances in neurons. In addition, we provide a computational method to extract dynamic input conductances from arbitrary conductance-based models and to analyze their sensitivity to arbitrary parameters. We illustrate the relevance of the proposed approach for modulation, compensation, and robustness studies in a published neuron model based on data of the stomatogastric ganglion of the crab Cancer borealis.

A modeling approach on why simple central pattern generators are built of irregular neurons

The crustacean pyloric Central Pattern Generator (CPG) is a nervous circuit that endogenously provides periodic motor patterns. Even after about 40 years of intensive studies, the rhythm genesis is still not rigorously understood in this CPG, mainly because it is made of neurons with irregular intrinsic activity. Using mathematical models we addressed the question of using a network of irregularly behaving elements to generate periodic oscillations, and we show some advantages of using non-periodic neurons with intrinsic behavior in the transition from bursting to tonic spiking (as found in biological pyloric CPGs) as building components. We studied two- and three-neuron model CPGs built either with Hindmarsh-Rose or with conductance-based Hodgkin-Huxley-like model neurons. By changing a model’s parameter we could span the neuron’s intrinsic dynamical behavior from slow periodic bursting to fast tonic spiking, passing through a transition where irregular bursting was observed. Two-neuron CPG, half center oscillator (HCO), was obtained for each intrinsic behavior of the neurons by coupling them with mutual symmetric synaptic inhibition. Most of these HCOs presented regular antiphasic bursting activity and the changes of the bursting frequencies was studied as a function of the inhibitory synaptic strength. Among all HCOs, those made of intrinsic irregular neurons presented a wider burst frequency range while keeping a reliable regular oscillatory (bursting) behavior. HCOs of periodic neurons tended to be either hard to change their behavior with synaptic strength variations (slow periodic burster neurons) or unable to perform a physiologically meaningful rhythm (fast tonic spiking neurons). Moreover, 3-neuron CPGs with connectivity and output similar to those of the pyloric CPG presented the same results.

Phase diagram of spiking neural networks

In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations, and trials and errors, but here, I take a different perspective, inspired by evolution, I systematically simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable. I stimulate networks with pulses and then measure their: dynamic range, dominant frequency of population activities, total duration of activities, maximum rate of population and the occurrence time of maximum rate. The results are organized in phase diagram. This phase diagram gives an insight into the space of parameters – excitatory to inhibitory ratio, sparseness of connections and synaptic weights. This phase diagram can be used to decide the parameters of a model. The phase diagrams show that networks which are configured according to the common values, have a good dynamic range in response to an impulse and their dynamic range is robust in respect to synaptic weights, and for some synaptic weights they oscillates in α or β frequencies, independent of external stimuli.

Compensatory plasticity restores locomotion after chronic removal of descending projections

Homeostatic plasticity is an important attribute of neurons and their networks, enabling functional recovery after perturbation. Furthermore, the directed nature of this plasticity may hold a key to the restoration of locomotion after spinal cord injury. Here we studied the recovery of crawling in the leech Hirudo verbana after descending cephalic fibers were surgically separated from crawl central pattern generators shown previously to be regulated by dopamine. We observed that immediately after nerve cord transection leeches were unable to crawl, but remarkably, after a day to weeks, animals began to show elements of crawling and intersegmental coordination. Over a similar time course, excessive swimming due to the loss of descending inhibition returned to control levels. Additionally, removal of the brain did not prevent crawl recovery, indicating that connectivity of severed descending neurons was not essential. After crawl recovery, a subset of animals received a second transection immediately below the anterior-most ganglion remaining. Similar to their initial transection, a loss of crawling with subsequent recovery was observed. These data, in recovered individuals, support the idea that compensatory plasticity directly below the site of injury is essential for the initiation and coordination of crawling. We maintain that the leech provides a valuable model to understand the neural mechanisms underlying locomotor recovery after injury because of its experimental accessibility, segmental organization, and dependence on higher-order control involved in the initiation, modulation, and coordination of locomotor behavior.

RIM-binding protein links synaptic homeostasis to the stabilization and replenishment of high release probability vesicles

Here we define activities of RIM-binding protein (RBP) that are essential for baseline neurotransmission and presynaptic homeostatic plasticity. At baseline, rbp mutants have a ∼10-fold decrease in the apparent Ca(2+) sensitivity of release that we attribute to (1) impaired presynaptic Ca(2+) influx, (2) looser coupling of vesicles to Ca(2+) influx, and (3) limited access to the readily releasable vesicle pool (RRP). During homeostatic plasticity, RBP is necessary for the potentiation of Ca(2+) influx and the expansion of the RRP. Remarkably, rbp mutants also reveal a rate-limiting stage required for the replenishment of high release probability (p) vesicles following vesicle depletion. This rate slows ∼4-fold at baseline and nearly 7-fold during homeostatic signaling in rbp. These effects are independent of altered Ca(2+) influx and RRP size. We propose that RBP stabilizes synaptic efficacy and homeostatic plasticity through coordinated control of presynaptic Ca(2+) influx and the dynamics of a high-p vesicle pool.

Tonic 5nM DA stabilizes neuronal output by enabling bidirectional activity-dependent regulation of the hyperpolarization activated current via PKA and calcineurin

Volume transmission results in phasic and tonic modulatory signals. The actions of tonic dopamine (DA) at type 1 DA receptors (D1Rs) are largely undefined. Here we show that tonic 5nM DA acts at D1Rs to stabilize neuronal output over minutes by enabling activity-dependent regulation of the hyperpolarization activated current (I _h). In the presence but not absence of 5nM DA, I _h maximal conductance (G _max) was adjusted according to changes in slow wave activity in order to maintain spike timing. Our study on the lateral pyloric neuron (LP), which undergoes rhythmic oscillations in membrane potential with depolarized plateaus, demonstrated that incremental, bi-directional changes in plateau duration produced corresponding alterations in LP I _hG _max when preparations were superfused with saline containing 5nM DA. However, when preparations were superfused with saline alone there was no linear correlation between LP I _hG_max and duty cycle. Thus, tonic nM DA modulated the capacity for activity to modulate LP I _h G _max; this exemplifies metamodulation (modulation of modulation). Pretreatment with the Ca2+-chelator, BAPTA, or the specific PKA inhibitor, PKI, prevented all changes in LP I _h in 5nM DA. Calcineurin inhibitors blocked activity-dependent changes enabled by DA and revealed a PKA-mediated, activity-independent enhancement of LP I _hG _max. These data suggested that tonic 5nM DA produced two simultaneous, PKA-dependent effects: a direct increase in LP I _h G _max and a priming event that permitted calcineurin regulation of LP I _h. The latter produced graded reductions in LP I _hG _max with increasing duty cycles. We also demonstrated that this metamodulation preserved the timing of LP’s first spike when network output was perturbed with bath-applied 4AP. In sum, 5nM DA permits slow wave activity to provide feedback that maintains spike timing, suggesting that one function of low-level, tonic modulation is to stabilize specific features of a dynamic output.

How can motor systems retain performance over a wide temperature range? Lessons from the crustacean stomatogastric nervous system

Marine invertebrates, such as lobsters and crabs, deal with a widely and wildly fluctuating temperature environment. Here, we describe the effects of changing temperature on the motor patterns generated by the stomatogastric nervous system of the crab, Cancer borealis. Over a broad range of "permissive" temperatures, the pyloric rhythm increases in frequency but maintains its characteristic phase relationships. Nonetheless, at more extreme high temperatures, the normal triphasic pyloric rhythm breaks down, or "crashes". We present both experimental and computational approaches to understanding the stability of both single neurons and networks to temperature perturbations, and discuss data that shows that the "crash" temperatures themselves may be environmentally regulated. These approaches provide insight into how the nervous system can be stable to a global perturbation, such as temperature, in spite of the fact that all biological processes are temperature dependent.

Homeostatic control of presynaptic neurotransmitter release

It is well established that the active properties of nerve and muscle cells are stabilized by homeostatic signaling systems. In organisms ranging from Drosophila to humans, neurons restore baseline function in the continued presence of destabilizing perturbations by rebalancing ion channel expression, modifying neurotransmitter receptor surface expression and trafficking, and modulating neurotransmitter release. This review focuses on the homeostatic modulation of presynaptic neurotransmitter release, termed presynaptic homeostasis. First, we highlight criteria that can be used to define a process as being under homeostatic control. Next, we review the remarkable conservation of presynaptic homeostasis at the Drosophila, mouse, and human neuromuscular junctions and emerging parallels at synaptic connections in the mammalian central nervous system. We then highlight recent progress identifying cellular and molecular mechanisms. We conclude by reviewing emerging parallels between the mechanisms of homeostatic signaling and genetic links to neurological disease.

Using multi-compartment ensemble modeling as an investigative tool of spatially distributed biophysical balances: application to hippocampal oriens-lacunosum/moleculare (O-LM) cells

Multi-compartmental models of neurons provide insight into the complex, integrative properties of dendrites. Because it is not feasible to experimentally determine the exact density and kinetics of each channel type in every neuronal compartment, an essential goal in developing models is to help characterize these properties. To address biological variability inherent in a given neuronal type, there has been a shift away from using hand-tuned models towards using ensembles or populations of models. In collectively capturing a neuron's output, ensemble modeling approaches uncover important conductance balances that control neuronal dynamics. However, conductances are never entirely known for a given neuron class in terms of its types, densities, kinetics and distributions. Thus, any multi-compartment model will always be incomplete. In this work, our main goal is to use ensemble modeling as an investigative tool of a neuron's biophysical balances, where the cycling between experiment and model is a design criterion from the start. We consider oriens-lacunosum/moleculare (O-LM) interneurons, a prominent interneuron subtype that plays an essential gating role of information flow in hippocampus. O-LM cells express the hyperpolarization-activated current (Ih). Although dendritic Ih could have a major influence on the integrative properties of O-LM cells, the compartmental distribution of Ih on O-LM dendrites is not known. Using a high-performance computing cluster, we generated a database of models that included those with or without dendritic Ih. A range of conductance values for nine different conductance types were used, and different morphologies explored. Models were quantified and ranked based on minimal error compared to a dataset of O-LM cell electrophysiological properties. Co-regulatory balances between conductances were revealed, two of which were dependent on the presence of dendritic Ih. These findings inform future experiments that differentiate between somatic and dendritic Ih, thereby continuing a cycle between model and experiment.

Nonlinear and Stochastic Dynamics in the Heart

In a normal human life span, the heart beats about 2 to 3 billion times. Under diseased conditions, a heart may lose its normal rhythm and degenerate suddenly into much faster and irregular rhythms, called arrhythmias, which may lead to sudden death. The transition from a normal rhythm to an arrhythmia is a transition from regular electrical wave conduction to irregular or turbulent wave conduction in the heart, and thus this medical problem is also a problem of physics and mathematics. In the last century, clinical, experimental, and theoretical studies have shown that dynamical theories play fundamental roles in understanding the mechanisms of the genesis of the normal heart rhythm as well as lethal arrhythmias. In this article, we summarize in detail the nonlinear and stochastic dynamics occurring in the heart and their links to normal cardiac functions and arrhythmias, providing a holistic view through integrating dynamics from the molecular (microscopic) scale, to the organelle (mesoscopic) scale, to the cellular, tissue, and organ (macroscopic) scales. We discuss what existing problems and challenges are waiting to be solved and how multi-scale mathematical modeling and nonlinear dynamics may be helpful for solving these problems.

Mapping the natural variation in whole bone stiffness and strength across skeletal sites

Traits of the skeletal system are coordinately adjusted to establish mechanical homeostasis in response to genetic and environmental factors. Prior work demonstrated that this 'complex adaptive' process is not perfect, revealing a two-fold difference in whole bone stiffness of the tibia across a population. Robustness (specifically, total cross-sectional area relative to length) varies widely across skeletal sites and between sexes. However, it is unknown whether the natural variation in whole bone stiffness and strength also varies across skeletal sites and between men and women. We tested the hypotheses that: 1) all major long bones of the appendicular skeleton demonstrate inherent, systemic constraints in the degree to which morphological and compositional traits can be adjusted for a given robustness; and 2) these traits covary in a predictable manner independent of body size and robustness. We assessed the functional relationships among robustness, cortical area (Ct.Ar), cortical tissue mineral density (Ct.TMD), and bone strength index (BSI) across the long bones of the upper and lower limbs of 115 adult men and women. All bones showed a significant (p<0.001) positive regression between BSI and robustness after adjusting for body size, with slender bones being 1.7-2.3 times less stiff and strong in men and 1.3-2.8 times less stiff and strong in women compared to robust bones. Our findings are the first to document the natural inter-individual variation in whole bone stiffness and strength that exist within populations and that is predictable based on skeletal robustness for all major long bones. Documenting and further understanding this natural variation in strength may be critical for differentially diagnosing and treating skeletal fragility.

Connexin hemichannels contribute to spontaneous electrical activity in the human fetal cortex

Before the human cortex is able to process sensory information, young postmitotic neurons must maintain occasional bursts of action-potential firing to attract and keep synaptic contacts, to drive gene expression, and to transition to mature membrane properties. Before birth, human subplate (SP) neurons are spontaneously active, displaying bursts of electrical activity (plateau depolarizations with action potentials). Using whole-cell recordings in acute cortical slices, we investigated the source of this early activity. The spontaneous depolarizations in human SP neurons at midgestation (17-23 gestational weeks) were not completely eliminated by tetrodotoxin--a drug that blocks action potential firing and network activity--or by antagonists of glutamatergic, GABAergic, or glycinergic synaptic transmission. We then turned our focus away from standard chemical synapses to connexin-based gap junctions and hemichannels. PCR and immunohistochemical analysis identified the presence of connexins (Cx26/Cx32/Cx36) in the human fetal cortex. However, the connexin-positive cells were not found in clusters but, rather, were dispersed in the SP zone. Also, gap junction-permeable dyes did not diffuse to neighboring cells, suggesting that SP neurons were not strongly coupled to other cells at this age. Application of the gap junction and hemichannel inhibitors octanol, flufenamic acid, and carbenoxolone significantly blocked spontaneous activity. The putative hemichannel antagonist lanthanum alone was a potent inhibitor of the spontaneous activity. Together, these data suggest that connexin hemichannels contribute to spontaneous depolarizations in the human fetal cortex during the second trimester of gestation.

A detailed, conductance-based computer model of intrinsic sensory neurons of the gastrointestinal tract

Intrinsic sensory neurons (ISNs) of the enteric nervous system respond to stimuli such as muscle tension, muscle length, distortion of the mucosa, and the chemical content in the lumen. ISNs form recurrent networks that probably drive many intestinal motor patterns and reflexes. ISNs express a large number of voltage- and calcium-gated ion channels, some of which are modified by inflammation or repeated physiological stimuli, but how interactions between different ionic currents in ISNs produce both normal and pathological behaviors in the intestine remains unclear. We constructed a model of ISNs including voltage-gated sodium and potassium channels, N-type calcium channels, big conductance calcium-dependent potassium (BK) channels, calcium-dependent nonspecific cation channels (NSCa), intermediate conductance calcium-dependent potassium (IK) channels, hyperpolarization-activated cation (Ih) channels, and internal calcium dynamics. The model was based on data from the literature and our electrophysiological studies. The model reproduced responses to short or long depolarizing current pulses and responses to long hyperpolarizing current pulses. Sensitivity analysis showed that Ih, IK, NSCa, and BK have the largest influence on the number of action potentials observed during prolonged depolarizations. The model also predicts that changes to the voltage of activation for Ih have a large influence on excitability, but changes to the time constant of activation for Ih have a minor effect. Our model identifies how interactions between different iconic currents influence the excitability of ISNs and highlights an important role for Ih in enteric neuroplasticity resulting from disease.

Inter-subject variability in human atrial action potential in sinus rhythm versus chronic atrial fibrillation

AIMS

Human atrial electrophysiology exhibits high inter-subject variability in both sinus rhythm (SR) and chronic atrial fibrillation (cAF) patients. Variability is however rarely investigated in experimental and theoretical electrophysiological studies, thus hampering the understanding of its underlying causes but also its implications in explaining differences in the response to disease and treatment. In our study, we aim at investigating the ability of populations of human atrial cell models to capture the inter-subject variability in action potential (AP) recorded in 363 patients both under SR and cAF conditions.

METHODS AND RESULTS

Human AP recordings in atrial trabeculae (n = 469) from SR and cAF patients were used to calibrate populations of computational SR and cAF atrial AP models. Three populations of over 2000 sampled models were generated, based on three different human atrial AP models. Experimental calibration selected populations of AP models yielding AP with morphology and duration in range with experimental recordings. Populations using the three original models can mimic variability in experimental AP in both SR and cAF, with median conductance values in SR for most ionic currents deviating less than 30% from their original peak values. All cAF populations show similar variations in G(K1), G(Kur) and G(to), consistent with AF-related remodeling as reported in experiments. In all SR and cAF model populations, inter-subject variability in I(K1) and I(NaK) underlies variability in APD90, variability in I(Kur), I(CaL) and I(NaK) modulates variability in APD50 and combined variability in Ito and I(Kur) determines variability in APD20. The large variability in human atrial AP triangulation is mostly determined by I(K1) and either I(NaK) or I(NaCa) depending on the model.

CONCLUSION

Experimentally-calibrated human atrial AP models populations mimic AP variability in SR and cAF patient recordings, and identify potential ionic determinants of inter-subject variability in human atrial AP duration and morphology in SR versus cAF.

Elevated serotonergic signaling amplifies synaptic noise and facilitates the emergence of epileptiform network oscillations

Serotonin fibers densely innervate the cortical sheath to regulate neuronal excitability, but its role in shaping network dynamics remains undetermined. We show that serotonin provides an excitatory tone to cortical neurons in the form of spontaneous synaptic noise through 5-HT3 receptors, which is persistent and can be augmented using fluoxetine, a selective serotonin re-uptake inhibitor. Augmented serotonin signaling also increases cortical network activity by enhancing synaptic excitation through activation of 5-HT2 receptors. This in turn facilitates the emergence of epileptiform network oscillations (10-16 Hz) known as fast runs. A computational model of cortical dynamics demonstrates that these two combined mechanisms, increased background synaptic noise and enhanced synaptic excitation, are sufficient to replicate the emergence fast runs and their statistics. Consistent with these findings, we show that blocking 5-HT2 receptors in vivo significantly raises the threshold for convulsant-induced seizures.

Time and timing in the acoustic recognition system of crickets

The songs of many insects exhibit precise timing as the result of repetitive and stereotyped subunits on several time scales. As these signals encode the identity of a species, time and timing are important for the recognition system that analyzes these signals. Crickets are a prominent example as their songs are built from sound pulses that are broadcast in a long trill or as a chirped song. This pattern appears to be analyzed on two timescales, short and long. Recent evidence suggests that song recognition in crickets relies on two computations with respect to time; a short linear-nonlinear (LN) model that operates as a filter for pulse rate and a longer integration time window for monitoring song energy over time. Therefore, there is a twofold role for timing. A filter for pulse rate shows differentiating properties for which the specific timing of excitation and inhibition is important. For an integrator, however, the duration of the time window is more important than the precise timing of events. Here, we first review evidence for the role of LN-models and integration time windows for song recognition in crickets. We then parameterize the filter part by Gabor functions and explore the effects of duration, frequency, phase, and offset as these will correspond to differently timed patterns of excitation and inhibition. These filter properties were compared with known preference functions of crickets and katydids. In a comparative approach, the power for song discrimination by LN-models was tested with the songs of over 100 cricket species. It is demonstrated how the acoustic signals of crickets occupy a simple 2-dimensional space for song recognition that arises from timing, described by a Gabor function, and time, the integration window. Finally, we discuss the evolution of recognition systems in insects based on simple sensory computations.

Channeling the Central Dogma

How do neurons and networks achieve their characteristic electrical activity, regulate this activity homeostatically, and yet show population variability in expression? In this issue of Neuron, O'Leary et al. (2014) address some of these thorny questions in this theoretical analysis that starts with the Central Dogma.

Cell types, network homeostasis, and pathological compensation from a biologically plausible ion channel expression model

How do neurons develop, control, and maintain their electrical signaling properties in spite of ongoing protein turnover and perturbations to activity? From generic assumptions about the molecular biology underlying channel expression, we derive a simple model and show how it encodes an "activity set point" in single neurons. The model generates diverse self-regulating cell types and relates correlations in conductance expression observed in vivo to underlying channel expression rates. Synaptic as well as intrinsic conductances can be regulated to make a self-assembling central pattern generator network; thus, network-level homeostasis can emerge from cell-autonomous regulation rules. Finally, we demonstrate that the outcome of homeostatic regulation depends on the complement of ion channels expressed in cells: in some cases, loss of specific ion channels can be compensated; in others, the homeostatic mechanism itself causes pathological loss of function.

Region-specific regulation of voltage-gated intrinsic currents in the developing optic tectum of the Xenopus tadpole

Across the rostrocaudal (RC) axis of the Xenopus tadpole optic tectum exists a developmental gradient. This gradient has served as a useful model to study many aspects of synapse and dendrite maturation. To compliment these studies, we characterized how the intrinsic excitability, the ease in which a neuron can fire action potentials, might also be changing across the same axis. Whole-cell recordings from tectal neurons at different points along the RC axis revealed a graded increase in intrinsic excitability: compared with neurons at the caudal end of the tectum, neurons at the rostral end fired more action potentials in response to current injection and expressed greater peak Na⁺ and K⁺ currents, the major intrinsic currents in these neurons that underlie the action potential. We also observed, along the same axis and in the same direction, a previously described increase in the amount of synaptic drive received by individual neurons (Wu GY, Malinow R, Cline HT. Science 274: 972-976, 1996). Thus as synaptic activity ramps up across the RC axis, so does intrinsic excitability. The reduction of overall circuit activity induced a compensatory scaling up of peak Na⁺ and K⁺ currents only in the caudal portion of the tectum, suggesting a region-specific, compensatory form of plasticity.

Neural and genetic degeneracy underlies Caenorhabditis elegans feeding behavior

Degenerate networks, in which structurally distinct elements can perform the same function or yield the same output, are ubiquitous in biology. Degeneracy contributes to the robustness and adaptability of networks in varied environmental and evolutionary contexts. However, how degenerate neural networks regulate behavior in vivo is poorly understood, especially at the genetic level. Here, we identify degenerate neural and genetic mechanisms that underlie excitation of the pharynx (feeding organ) in the nematode Caenorhabditis elegans using cell-specific optogenetic excitation and inhibition. We show that the pharyngeal neurons MC, M2, M4, and I1 form multiple direct and indirect excitatory pathways in a robust network for control of pharyngeal pumping. I1 excites pumping via MC and M2 in a state-dependent manner. We identify nicotinic and muscarinic receptors through which the pharyngeal network regulates feeding rate. These results identify two different mechanisms by which degeneracy is manifest in a neural circuit in vivo.

Unaltered striatal dopamine release levels in young Parkin knockout, Pink1 knockout, DJ-1 knockout and LRRK2 R1441G transgenic mice

Parkinson's disease (PD) is one of the most prevalent neurodegenerative brain diseases; it is accompanied by extensive loss of dopamine (DA) neurons of the substantia nigra that project to the putamen, leading to impaired motor functions. Several genes have been associated with hereditary forms of the disease and transgenic mice have been developed by a number of groups to produce animal models of PD and to explore the basic functions of these genes. Surprisingly, most of the various mouse lines generated such as Parkin KO, Pink1 KO, DJ-1 KO and LRRK2 transgenic have been reported to lack degeneration of nigral DA neuron, one of the hallmarks of PD. However, modest impairments of motor behavior have been reported, suggesting the possibility that the models recapitulate at least some of the early stages of PD, including early dysfunction of DA axon terminals. To further evaluate this possibility, here we provide for the first time a systematic comparison of DA release in four different mouse lines, examined at a young age range, prior to potential age-dependent compensations. Using fast scan cyclic voltammetry in striatal sections prepared from young, 6–8 weeks old mice, we examined sub-second DA overflow evoked by single pulses and action potential trains. Unexpectedly, none of the models displayed any dysfunction of DA overflow or reuptake. These results, compatible with the lack of DA neuron loss in these models, suggest that molecular dysfunctions caused by the absence or mutation of these individual genes are not sufficient to perturb the function and survival of mouse DA neurons.

Rostral spinal cord segments are sufficient to generate a rhythm for both locomotion and scratching but affect their hip extensor phases differently

Rostral segments of the spinal cord hindlimb enlargement are more important than caudal segments for generating locomotion and scratching rhythms in limbed vertebrates, but the adequacy of rostral segments has not been directly compared between locomotion and scratching. We separated caudal segments from immobilized low-spinal turtles by sequential spinal cord transections. After separation of the caudal four segments of the five-segment hindlimb enlargement, the remaining enlargement segment and five preenlargement segments still produced rhythms for forward swimming and both rostral and pocket scratching. The swimming rhythm frequency was usually maintained. Some animals continued to generate swimming and scratching rhythms even with no enlargement segments remaining, using only preenlargement segments. The preenlargement segments and rostral-most enlargement segment were also sufficient to maintain hip flexor (HF) motoneuron quiescence between HF bursts [which normally occurs during each hip extensor (HE) phase] during swimming. In contrast, the HF-quiescent phase was increasingly absent (i.e., HE-phase deletions) during rostral and pocket scratching. Moreover, respiratory motoneurons that normally burst during HE bursts continued to burst during the HF quiescence of swimming even with the caudal segments separated. Thus the same segments are sufficient to generate the basic rhythms for both locomotion and scratching. These segments are also sufficient to produce a reliable HE phase during locomotion but not during rostral or pocket scratching. We hypothesize that the rostral HE-phase interneurons that rhythmically inhibit HF motoneurons and interneurons are sufficient to generate HF quiescence during HE-biased swimming but not during the more HF-biased rostral and pocket scratching.

Criticality and degeneracy in injury-induced changes in primary afferent excitability and the implications for neuropathic pain

Neuropathic pain remains notoriously difficult to treat despite numerous drug targets. Here, we offer a novel explanation for this intractability. Computer simulations predicted that qualitative changes in primary afferent excitability linked to neuropathic pain arise through a switch in spike initiation dynamics when molecular pathologies reach a tipping point (criticality), and that this tipping point can be reached via several different molecular pathologies (degeneracy). We experimentally tested these predictions by pharmacologically blocking native conductances and/or electrophysiologically inserting virtual conductances. Multiple different manipulations successfully reproduced or reversed neuropathic changes in primary afferents from naïve or nerve-injured rats, respectively, thus confirming the predicted criticality and its degenerate basis. Degeneracy means that several different molecular pathologies are individually sufficient to cause hyperexcitability, and because several such pathologies co-occur after nerve injury, that no single pathology is uniquely necessary. Consequently, single-target-drugs can be circumvented by maladaptive plasticity in any one of several ion channels.

DOI:http://dx.doi.org/10.7554/eLife.02370.001

Although the pain associated with an injury is unpleasant, it normally serves an important purpose: to make you avoid its source. However, some pain appears to arise from nowhere. Frustratingly, this type of pain, known as neuropathic pain, does not respond to common painkillers and is thus very difficult to treat.

The neurons that transmit pain and other sensory information do so using electrical signals. In response to a stimulus, ions travel through channels in the membrane of a neuron, which leads to a change in the electrical potential of the membrane. When this change is large enough, a voltage spike is produced: this signal is ultimately transmitted to the brain.

When certain neurons fire too easily or too often, neuropathic pain can arise. This hyperexcitability can make something painful feel even worse, or it can make things hurt that shouldn’t. To prevent this, extensive research has been devoted to identify drugs that target particular types of ion channels and block them. However, despite the discovery of many promising drugs, those drugs have been frustratingly ineffective in clinical trials.

Using simulations and experiments, Ratté et al. have examined the behavior of a type of neuron that normally conducts information about touch, but the brain sometimes misinterprets this information as pain. Increasing the flow of ions through the cell membrane in these simulations eventually causes a ‘tipping point’ to be crossed, which triggers a dramatic, discontinuous change in spiking pattern. However, as several different types of ion channels contribute to the current, there are several different ways in which the tipping point can be crossed.

This ability to produce the same result by multiple means is a common feature of complex systems. Known as degeneracy, it makes systems more robust, as a given result can still be achieved if one particular attempt to achieve this result fails. The work of Ratté et al. helps to explain why drugs that target just one type of ion channel may fail to relieve neuropathic pain: maladaptive changes in any one of several other ion channels may circumvent the therapeutic effect.

DOI:http://dx.doi.org/10.7554/eLife.02370.002

Inputs drive cell phenotype variability

What is the significance of the extensive variability observed in individual members of a single-cell phenotype? This question is particularly relevant to the highly differentiated organization of the brain. In this study, for the first time, we analyze the in vivo variability within a neuronal phenotype in terms of input type. We developed a large-scale gene-expression data set from several hundred single brainstem neurons selected on the basis of their specific synaptic input types. The results show a surprising organizational structure in which neuronal variability aligned with input type along a continuum of sub-phenotypes and corresponding gene regulatory modules. Correlations between these regulatory modules and specific cellular states were stratified by synaptic input type. Moreover, we found that the phenotype gradient and correlated regulatory modules were maintained across subjects. As these specific cellular states are a function of the inputs received, the stability of these states represents “attractor”-like states along a dynamic landscape that is influenced and shaped by inputs, enabling distinct state-dependent functional responses. We interpret the phenotype gradient as arising from analog tuning of underlying regulatory networks driven by distinct inputs to individual cells. Our results change the way we understand how a phenotypic population supports robust biological function by integrating the environmental experience of individual cells. Our results provide an explanation of the functional significance of the pervasive variability observed within a cell type and are broadly applicable to understanding the relationship between cellular input history and cell phenotype within all tissues.

Population of computational rabbit-specific ventricular action potential models for investigating sources of variability in cellular repolarisation

Variability is observed at all levels of cardiac electrophysiology. Yet, the underlying causes and importance of this variability are generally unknown, and difficult to investigate with current experimental techniques. The aim of the present study was to generate populations of computational ventricular action potential models that reproduce experimentally observed intercellular variability of repolarisation (represented by action potential duration) and to identify its potential causes. A systematic exploration of the effects of simultaneously varying the magnitude of six transmembrane current conductances (transient outward, rapid and slow delayed rectifier K⁺, inward rectifying K⁺, L-type Ca²⁺, and Na⁺/K⁺ pump currents) in two rabbit-specific ventricular action potential models (Shannon et al. and Mahajan et al.) at multiple cycle lengths (400, 600, 1,000 ms) was performed. This was accomplished with distributed computing software specialised for multi-dimensional parameter sweeps and grid execution. An initial population of 15,625 parameter sets was generated for both models at each cycle length. Action potential durations of these populations were compared to experimentally derived ranges for rabbit ventricular myocytes. 1,352 parameter sets for the Shannon model and 779 parameter sets for the Mahajan model yielded action potential duration within the experimental range, demonstrating that a wide array of ionic conductance values can be used to simulate a physiological rabbit ventricular action potential. Furthermore, by using clutter-based dimension reordering, a technique that allows visualisation of multi-dimensional spaces in two dimensions, the interaction of current conductances and their relative importance to the ventricular action potential at different cycle lengths were revealed. Overall, this work represents an important step towards a better understanding of the role that variability in current conductances may play in experimentally observed intercellular variability of rabbit ventricular action potential repolarisation.

The sign rule and beyond: boundary effects, flexibility, and noise correlations in neural population codes

Over repeat presentations of the same stimulus, sensory neurons show variable responses. This “noise” is typically correlated between pairs of cells, and a question with rich history in neuroscience is how these noise correlations impact the population's ability to encode the stimulus. Here, we consider a very general setting for population coding, investigating how information varies as a function of noise correlations, with all other aspects of the problem – neural tuning curves, etc. – held fixed. This work yields unifying insights into the role of noise correlations. These are summarized in the form of theorems, and illustrated with numerical examples involving neurons with diverse tuning curves. Our main contributions are as follows. (1) We generalize previous results to prove a sign rule (SR) — if noise correlations between pairs of neurons have opposite signs vs. their signal correlations, then coding performance will improve compared to the independent case. This holds for three different metrics of coding performance, and for arbitrary tuning curves and levels of heterogeneity. This generality is true for our other results as well. (2) As also pointed out in the literature, the SR does not provide a necessary condition for good coding. We show that a diverse set of correlation structures can improve coding. Many of these violate the SR, as do experimentally observed correlations. There is structure to this diversity: we prove that the optimal correlation structures must lie on boundaries of the possible set of noise correlations. (3) We provide a novel set of necessary and sufficient conditions, under which the coding performance (in the presence of noise) will be as good as it would be if there were no noise present at all.

Sensory systems communicate information to the brain — and brain areas communicate between themselves — via the electrical activities of their respective neurons. These activities are “noisy”: repeat presentations of the same stimulus do not yield to identical responses every time. Furthermore, the neurons' responses are not independent: the variability in their responses is typically correlated from cell to cell. How does this change the impact of the noise — for better or for worse? Our goal here is to classify (broadly) the sorts of noise correlations that are either good or bad for enabling populations of neurons to transmit information. This is helpful as there are many possibilities for the noise correlations, and the set of possibilities becomes large for even modestly sized neural populations. We prove mathematically that, for larger populations, there are many highly diverse ways that favorable correlations can occur. These often differ from the noise correlation structures that are typically identified as beneficial for information transmission – those that follow the so-called “sign rule.” Our results help in interpreting some recent data that seems puzzling from the perspective of this rule.

Network processes involved in the mediation of short-term habituation in Aplysia: contribution of intrinsic regulation of excitability and synaptic augmentation

Short-term habituation (STH) is the decrease in behavioral responding observed during repeated stimulation at regular intervals. For siphon-elicited siphon withdrawal in Aplysia (S-SWR), we previously showed that the amplitude of responses measured in LFS-type siphon motor neurons (LFS MNs) during training is dependent on the stimulus interval used and is training-site specific. The major source of excitation from siphon stimulation onto the LFS MNs comes from the L29 interneurons. Here we examined the role of the L29s in STH by addressing two questions:

(1) What are the relative contributions of intrinsic regulation of excitability and network inhibition on L29 activity during STH training?

By activating L29s with intracellular current injection, we found that intrinsic changes in excitability occur, but only at short training intervals (1 s). We also demonstrated that network inhibition is not required for regulating L29 responses during training, indicating that any expression of inhibition is redundant to the excitability changes.

(2) How does L29 synaptic plasticity contribute to the maintenance of training site-specificity exhibited in LFS MNs?

When training stimuli are delivered 1 s apart [1 s, interstimulus interval (ISI)], L29 responses decrease in both stimulated (trained) and un-stimulated (untrained) pathways, yet site-specificity of training is maintained in the LFS MNs. Our results suggest that activity-dependent synaptic facilitation (augmentation; AUG) expressed by the L29s acts to compensate for the decreased activity in the untrained pathway. First, we demonstrated that the L29-LFS synapse exhibits significant AUG with L29 activation at a 1 s ISI. Second, we showed that the induction of AUG prevents the reduction in siphon-evoked LFS responses that is otherwise observed with decreased L29 activity. Collectively, our results support a role for the L29s in regulating network dynamics during STH training, but only at rapid (1 s ISI) training intervals.

Investigating habits: strategies, technologies and models

Understanding habits at a biological level requires a combination of behavioral observations and measures of ongoing neural activity. Theoretical frameworks as well as definitions of habitual behaviors emerging from classic behavioral research have been enriched by new approaches taking account of the identification of brain regions and circuits related to habitual behavior. Together, this combination of experimental and theoretical work has provided key insights into how brain circuits underlying action-learning and action-selection are organized, and how a balance between behavioral flexibility and fixity is achieved. New methods to monitor and manipulate neural activity in real time are allowing us to have a first look “under the hood” of a habit as it is formed and expressed. Here we discuss ideas emerging from such approaches. We pay special attention to the unexpected findings that have arisen from our own experiments suggesting that habitual behaviors likely require the simultaneous activity of multiple distinct components, or operators, seen as responsible for the contrasting dynamics of neural activity in both cortico-limbic and sensorimotor circuits recorded concurrently during different stages of habit learning. The neural dynamics identified thus far do not fully meet expectations derived from traditional models of the structure of habits, and the behavioral measures of habits that we have made also are not fully aligned with these models. We explore these new clues as opportunities to refine an understanding of habits.

Physiological sharp wave-ripples and interictal events in vitro: what's the difference?

Sharp wave-ripples and interictal events are physiological and pathological forms of transient high activity in the hippocampus with similar features. Sharp wave-ripples have been shown to be essential in memory consolidation, whereas epileptiform (interictal) events are thought to be damaging. It is essential to grasp the difference between physiological sharp wave-ripples and pathological interictal events to understand the failure of control mechanisms in the latter case. We investigated the dynamics of activity generated intrinsically in the Cornu Ammonis region 3 of the mouse hippocampus in vitro, using four different types of intervention to induce epileptiform activity. As a result, sharp wave-ripples spontaneously occurring in Cornu Ammonis region 3 disappeared, and following an asynchronous transitory phase, activity reorganized into a new form of pathological synchrony. During epileptiform events, all neurons increased their firing rate compared to sharp wave-ripples. Different cell types showed complementary firing: parvalbumin-positive basket cells and some axo-axonic cells stopped firing as a result of a depolarization block at the climax of the events in high potassium, 4-aminopyridine and zero magnesium models, but not in the gabazine model. In contrast, pyramidal cells began firing maximally at this stage. To understand the underlying mechanism we measured changes of intrinsic neuronal and transmission parameters in the high potassium model. We found that the cellular excitability increased and excitatory transmission was enhanced, whereas inhibitory transmission was compromised. We observed a strong short-term depression in parvalbumin-positive basket cell to pyramidal cell transmission. Thus, the collapse of pyramidal cell perisomatic inhibition appears to be a crucial factor in the emergence of epileptiform events.

From the intrinsic properties to the functional role of a neuron phenotype: an example from electric fish during signal trade-off

This review deals with the question: what is the relationship between the properties of a neuron and the role that the neuron plays within a given neural circuit? Answering this kind of question requires collecting evidence from multiple neuron phenotypes and comparing the role of each type in circuits that perform well-defined computational tasks. The focus here is on the spherical neurons in the electrosensory lobe of the electric fish Gymnotus omarorum. They belong to the one-spike-onset phenotype expressed at the early stages of signal processing in various sensory modalities and diverse taxa. First, we refer to the one-spike neuron intrinsic properties, their foundation on a low-threshold K(+) conductance, and the potential roles of this phenotype in different circuits within a comparative framework. Second, we present a brief description of the active electric sense of weakly electric fish and the particularities of spherical one-spike-onset neurons in the electrosensory lobe of G. omarorum. Third, we introduce one of the specific tasks in which these neurons are involved: the trade-off between self- and allo-generated signals. Fourth, we discuss recent evidence indicating a still-undescribed role for the one-spike phenotype. This role deals with the blockage of the pathway after being activated by the self-generated electric organ discharge and how this blockage favors self-generated electrosensory information in the context of allo-generated interference. Based on comparative analysis we conclude that one-spike-onset neurons may play several functional roles in animal sensory behavior. There are specific adaptations of the neuron's 'response function' to the circuit and task. Conversely, the way in which a task is accomplished depends on the intrinsic properties of the neurons involved. In short, the role of a neuron within a circuit depends on the neuron and its functional context.

Natural antisense transcripts regulate the neuronal stress response and excitability

Neurons regulate ionic fluxes across their plasma membrane to maintain their excitable properties under varying environmental conditions. However, the mechanisms that regulate ion channels abundance remain poorly understood. Here we show that pickpocket 29 (ppk29), a gene that encodes a Drosophila degenerin/epithelial sodium channel (DEG/ENaC), regulates neuronal excitability via a protein-independent mechanism. We demonstrate that the mRNA 3′UTR of ppk29 affects neuronal firing rates and associated heat-induced seizures by acting as a natural antisense transcript (NAT) that regulates the neuronal mRNA levels of seizure (sei), the Drosophila homolog of the human Ether-à-go-go Related Gene (hERG) potassium channel. We find that the regulatory impact of ppk29 mRNA on sei is independent of the sodium channel it encodes. Thus, our studies reveal a novel mRNA dependent mechanism for the regulation of neuronal excitability that is independent of protein-coding capacity.

DOI:http://dx.doi.org/10.7554/eLife.01849.001

Neurons communicate with one another via electrical signals known as action potentials. These signals are generated when a stimulus causes sodium and potassium ion channels in the cell membrane to open, leading to an influx of sodium ions, followed by an efflux of potassium ions. Changes in temperature affect the rate at which ion channels open and close, and thus affect how easy it is for a stimulus to trigger an action potential. In response to a sudden rise in temperature, neurons must adjust the number of ion channels in their membranes to ensure that they do not become hyperexcitable, which could result in epilepsy.

Now, Zheng et al. have revealed one possible mechanism for how neurons do this. In the fruit fly, Drosophila, a gene for a potassium channel is found on the same chromosomal location as a gene for a sodium channel, and some of the genetic elements that regulate the expression of these two genes even overlap. However, the genes are on opposite strands of the DNA double helix. This means that when the genes are transcribed to produce molecules of messenger RNA (mRNA), which is usually single stranded, some of the mRNA molecules will pair up to form double-stranded mRNA molecules. This is significant because such RNA ‘duplexes’ have been shown to inhibit the translation of conventional single-stranded mRNA molecules into proteins, or to lead to their complete degradation.

Zheng et al. found that flies with mutations in the potassium channel gene display seizures in response to sudden changes in temperature. However, insects with mutations in the sodium channel gene are not affected because, surprisingly, they have a higher than expected number of potassium channels. It turns out that the mutant sodium channel mRNA molecules are unable to form RNA duplexes with potassium channel mRNA molecules: these duplexes would normally limit the number of potassium channels so, in their absence, the number of potassium channels increases, and this protects the flies from seizures.

Zheng et al. also uncovered a novel mechanism by which mRNA molecules can regulate gene expression independent of their role as templates for proteins. Further work is required to determine whether this mechanism is also present in other organisms, including humans.

DOI:http://dx.doi.org/10.7554/eLife.01849.002

Homeostatic signaling and the stabilization of neural function

The brain is astonishing in its complexity and capacity for change. This has fascinated scientists for more than a century, filling the pages of this journal for the past 25 years. But a paradigm shift is underway. It seems likely that the plasticity that drives our ability to learn and remember can only be meaningful in the context of otherwise stable, reproducible, and predictable baseline neural function. Without the existence of potent mechanisms that stabilize neural function, our capacity to learn and remember would be lost in the chaos of daily experiential change. This underscores two great mysteries in neuroscience. How are the functional properties of individual neurons and neural circuits stably maintained throughout life? And, in the face of potent stabilizing mechanisms, how can neural circuitry be modified during neural development, learning, and memory? Answers are emerging in the rapidly developing field of homeostatic plasticity.

Correlated conductance parameters in leech heart motor neurons contribute to motor pattern formation

Neurons can have widely differing intrinsic membrane properties, in particular the density of specific conductances, but how these contribute to characteristic neuronal activity or pattern formation is not well understood. To explore the relationship between conductances, and in particular how they influence the activity of motor neurons in the well characterized leech heartbeat system, we developed a new multi-compartmental Hodgkin-Huxley style leech heart motor neuron model. To do so, we evolved a population of model instances, which differed in the density of specific conductances, capable of achieving specific output activity targets given an associated input pattern. We then examined the sensitivity of measures of output activity to conductances and how the model instances responded to hyperpolarizing current injections. We found that the strengths of many conductances, including those with differing dynamics, had strong partial correlations and that these relationships appeared to be linked by their influence on heart motor neuron activity. Conductances that had positive correlations opposed one another and had the opposite effects on activity metrics when perturbed whereas conductances that had negative correlations could compensate for one another and had similar effects on activity metrics.

Functional integration of skeletal traits: an intraskeletal assessment of bone size, mineralization, and volume covariance

Understanding the functional integration of skeletal traits and how they naturally vary within and across populations will benefit assessments of functional adaptation directed towards interpreting bone stiffness in contemporary and past humans. Moreover, investigating how these traits intraskeletally vary will guide us closer towards predicting fragility from a single skeletal site. Using an osteological collection of 115 young adult male and female African-Americans, we assessed the functional relationship between bone robustness (i.e. total area/length), cortical tissue mineral density (Ct.TMD), and cortical area (Ct.Ar) for the upper and lower limbs. All long bones demonstrated significant trait covariance (p < 0.005) independent of body size, with slender bones having 25-50% less Ct.Ar and 5-8% higher Ct.TMD compared to robust bones. Robustness statistically explained 10.2-28% of Ct.TMD and 26.6-64.6% of Ct.Ar within male and female skeletal elements. This covariance is systemic throughout the skeleton, with either the slender or robust phenotype consistently represented within all long bones for each individual. These findings suggest that each person attains a unique trait set by adulthood that is both predictable by robustness and partially independent of environmental influences. The variation in these functionally integrated traits allows for the maximization of tissue stiffness and minimization of mass so that regardless of which phenotype is present, a given bone is reasonably stiff and strong, and sufficiently adapted to perform routine, habitual loading activities. Covariation intrinsic to functional adaptation suggests that whole bone stiffness depends upon particular sets of traits acquired during growth, presumably through differing levels of cellular activity, resulting in differing tissue morphology and composition. The outcomes of this intraskeletal examination of robustness and its correlates may have significant value in our progression towards improved clinical assessments of bone strength and fragility.

Neurons within the same network independently achieve conserved output by differentially balancing variable conductance magnitudes

Biological and theoretical evidence suggest that individual neurons may achieve similar outputs by differentially balancing variable underlying ionic conductances. Despite the substantial amount of data consistent with this idea, a direct biological demonstration that cells with conserved output, particularly within the same network, achieve these outputs via different solutions has been difficult to achieve. Here we demonstrate definitively that neurons from native neural networks with highly similar output achieve this conserved output by differentially tuning underlying conductance magnitudes. Multiple motor neurons of the crab (Cancer borealis) cardiac ganglion have highly conserved output within a preparation, despite showing a 2-4-fold range of conductance magnitudes. By blocking subsets of these currents, we demonstrate that the remaining conductances become unbalanced, causing disparate output as a result. Therefore, as strategies to understand neuronal excitability become increasingly sophisticated, it is important that such variability in excitability of neurons, even among those within the same individual, is taken into account.

Distinct inspiratory rhythm and pattern generating mechanisms in the preBötzinger complex

In the mammalian respiratory central pattern generator, the preBötzinger complex (preBötC) produces rhythmic bursts that drive inspiratory motor output. Cellular mechanisms initiated by each burst are hypothesized to be necessary to determine the timing of the subsequent burst, playing a critical role in rhythmogenesis. To explore mechanisms relating inspiratory burst generation to rhythmogenesis, we compared preBötC and hypoglossal (XII) nerve motor activity in medullary slices from neonatal mice in conditions where periods between successive inspiratory XII bursts were highly variable and distributed multimodally. This pattern resulted from rhythmic preBötC neural population activity that consisted of bursts, concurrent with XII bursts, intermingled with significantly smaller "burstlets". Burstlets occurred at regular intervals during significantly longer XII interburst intervals, at times when a XII burst was expected. When a preBötC burst occurred, its high amplitude inspiratory component (I-burst) was preceded by a preinspiratory component that closely resembled the rising phase of burstlets. Cadmium (8 μM) eliminated preBötC and XII bursts, but rhythmic preBötC burstlets persisted. Burstlets and preinspiratory activity were observed in ~90% of preBötC neurons that were active during I-bursts. When preBötC excitability was raised significantly, burstlets could leak through to motor output in medullary slices and in vivo in adult anesthetized rats. Thus, rhythmic bursting, a fundamental mode of nervous system activity and an essential element of breathing, can be deconstructed into a rhythmogenic process producing low amplitude burstlets and preinspiratory activity that determine timing, and a pattern-generating process producing suprathreshold I-bursts essential for motor output.

Natural scenes viewing alters the dynamics of functional connectivity in the human brain

Spontaneous fMRI fluctuations are organized in large-scale spatiotemporal structures, or resting-state networks (RSN). However, it is unknown how task performance affects RSN dynamics. We use MEG to measure slow (∼0.1 Hz) coherent fluctuations of band-limited power (BLP), a robust correlate of RSN, during rest and movie observation and compare them to fMRI-RSN. BLP correlation, especially in α band, dropped in multiple RSN during movie although overall topography was maintained. Variability of power correlation increased in visual occipital cortex, and transient decrements corresponded to scenes perceived as "event boundaries." Additionally, stronger task-dependent interactions developed between vision and language networks in θ and β bands, and default and language networks in γ band. The topography of fMRI connectivity and relative changes induced by the movie were well matched to MEG. We conclude that resting-state and task network interactions are clearly different in the frequency domain despite maintenance of underlying network topography.

A dual operator view of habitual behavior reflecting cortical and striatal dynamics

Habits are notoriously difficult to break and, if broken, are usually replaced by new routines. To examine the neural basis of these characteristics, we recorded spike activity in cortical and striatal habit sites as rats learned maze tasks. Overtraining induced a shift from purposeful to habitual behavior. This shift coincided with the activation of neuronal ensembles in the infralimbic neocortex and the sensorimotor striatum, which became engaged simultaneously but developed changes in spike activity with distinct time courses and stability. The striatum rapidly acquired an action-bracketing activity pattern insensitive to reward devaluation but sensitive to running automaticity. A similar pattern developed in the upper layers of the infralimbic cortex, but it formed only late during overtraining and closely tracked habit states. Selective optogenetic disruption of infralimbic activity during overtraining prevented habit formation. We suggest that learning-related spiking dynamics of both striatum and neocortex are necessary, as dual operators, for habit crystallization.

Correlations in ion channel expression emerge from homeostatic tuning rules

Experimental observations reveal that the expression levels of different ion channels vary across neurons of a defined type, even when these neurons exhibit stereotyped electrical properties. However, there are robust correlations between different ion channel expression levels, although the mechanisms that determine these correlations are unknown. Using generic model neurons, we show that correlated conductance expression can emerge from simple homeostatic control mechanisms that couple expression rates of individual conductances to cellular readouts of activity. The correlations depend on the relative rates of expression of different conductances. Thus, variability is consistent with homeostatic regulation and the structure of this variability reveals quantitative relations between regulation dynamics of different conductances. Furthermore, we show that homeostatic regulation is remarkably insensitive to the details that couple the regulation of a given conductance to overall neuronal activity because of degeneracy in the function of multiple conductances and can be robust to "antihomeostatic" regulation of a subset of conductances expressed in a cell.

Neurotransmitter switching in the adult brain regulates behavior

Neurotransmitters have been thought to be fixed throughout life, but whether sensory stimuli alter behaviorally relevant transmitter expression in the mature brain is unknown. We found that populations of interneurons in the adult rat hypothalamus switched between dopamine and somatostatin expression in response to exposure to short- and long-day photoperiods. Changes in postsynaptic dopamine receptor expression matched changes in presynaptic dopamine, whereas somatostatin receptor expression remained constant. Pharmacological blockade or ablation of these dopaminergic neurons led to anxious and depressed behavior, phenocopying performance after exposure to the long-day photoperiod. Induction of newly dopaminergic neurons through exposure to the short-day photoperiod rescued the behavioral consequences of lesions. Natural stimulation of other sensory modalities may cause changes in transmitter expression that regulate different behaviors.

Multiple mechanisms switch an electrically coupled, synaptically inhibited neuron between competing rhythmic oscillators

Rhythmic oscillations are common features of nervous systems. One of the fundamental questions posed by these rhythms is how individual neurons or groups of neurons are recruited into different network oscillations. We modeled competing fast and slow oscillators connected to a hub neuron with electrical and inhibitory synapses. We explore the patterns of coordination shown in the network as a function of the electrical coupling and inhibitory synapse strengths with the help of a novel visualization method that we call the "parameterscape." The hub neuron can be switched between the fast and slow oscillators by multiple network mechanisms, indicating that a given change in network state can be achieved by degenerate cellular mechanisms. These results have importance for interpreting experiments employing optogenetic, genetic, and pharmacological manipulations to understand circuit dynamics.

Neural circuits for peristaltic wave propagation in crawling Drosophila larvae: analysis and modeling

Drosophila larvae crawl by peristaltic waves of muscle contractions, which propagate along the animal body and involve the simultaneous contraction of the left and right side of each segment. Coordinated propagation of contraction does not require sensory input, suggesting that movement is generated by a central pattern generator (CPG). We characterized crawling behavior of newly hatched Drosophila larvae by quantifying timing and duration of segmental boundary contractions. We developed a CPG network model that recapitulates these patterns based on segmentally repeated units of excitatory and inhibitory (EI) neuronal populations coupled with immediate neighboring segments. A single network with symmetric coupling between neighboring segments succeeded in generating both forward and backward propagation of activity. The CPG network was robust to changes in amplitude and variability of connectivity strength. Introducing sensory feedback via "stretch-sensitive" neurons improved wave propagation properties such as speed of propagation and segmental contraction duration as observed experimentally. Sensory feedback also restored propagating activity patterns when an inappropriately tuned CPG network failed to generate waves. Finally, in a two-sided CPG model we demonstrated that two types of connectivity could synchronize the activity of two independent networks: connections from excitatory neurons on one side to excitatory contralateral neurons (E to E), and connections from inhibitory neurons on one side to excitatory contralateral neurons (I to E). To our knowledge, such I to E connectivity has not yet been found in any experimental system; however, it provides the most robust mechanism to synchronize activity between contralateral CPGs in our model. Our model provides a general framework for studying the conditions under which a single locally coupled network generates bilaterally synchronized and longitudinally propagating waves in either direction.

Potassium conductance dynamics confer robust spike-time precision in a neuromorphic model of the auditory brain stem

A fundamental question in neuroscience is how neurons perform precise operations despite inherent variability. This question also applies to neuromorphic engineering, where low-power microchips emulate the brain using large populations of diverse silicon neurons. Biological neurons in the auditory pathway display precise spike timing, critical for sound localization and interpretation of complex waveforms such as speech, even though they are a heterogeneous population. Silicon neurons are also heterogeneous, due to a key design constraint in neuromorphic engineering: smaller transistors offer lower power consumption and more neurons per unit area of silicon, but also more variability between transistors and thus between silicon neurons. Utilizing this variability in a neuromorphic model of the auditory brain stem with 1,080 silicon neurons, we found that a low-voltage-activated potassium conductance (g(KL)) enables precise spike timing via two mechanisms: statically reducing the resting membrane time constant and dynamically suppressing late synaptic inputs. The relative contribution of these two mechanisms is unknown because blocking g(KL) in vitro eliminates dynamic adaptation but also lengthens the membrane time constant. We replaced g(KL) with a static leak in silico to recover the short membrane time constant and found that silicon neurons could mimic the spike-time precision of their biological counterparts, but only over a narrow range of stimulus intensities and biophysical parameters. The dynamics of g(KL) were required for precise spike timing robust to stimulus variation across a heterogeneous population of silicon neurons, thus explaining how neural and neuromorphic systems may perform precise operations despite inherent variability.

High prevalence of multistability of rest states and bursting in a database of a model neuron

Flexibility in neuronal circuits has its roots in the dynamical richness of their neurons. Depending on their membrane properties single neurons can produce a plethora of activity regimes including silence, spiking and bursting. What is less appreciated is that these regimes can coexist with each other so that a transient stimulus can cause persistent change in the activity of a given neuron. Such multistability of the neuronal dynamics has been shown in a variety of neurons under different modulatory conditions. It can play either a functional role or present a substrate for dynamical diseases. We considered a database of an isolated leech heart interneuron model that can display silent, tonic spiking and bursting regimes. We analyzed only the cases of endogenous bursters producing functional half-center oscillators (HCOs). Using a one parameter (the leak conductance ()) bifurcation analysis, we extended the database to include silent regimes (stationary states) and systematically classified cases for the coexistence of silent and bursting regimes. We showed that different cases could exhibit two stable depolarized stationary states and two hyperpolarized stationary states in addition to various spiking and bursting regimes. We analyzed all cases of endogenous bursters and found that 18% of the cases were multistable, exhibiting coexistences of stationary states and bursting. Moreover, 91% of the cases exhibited multistability in some range of . We also explored HCOs built of multistable neuron cases with coexisting stationary states and a bursting regime. In 96% of cases analyzed, the HCOs resumed normal alternating bursting after one of the neurons was reset to a stationary state, proving themselves robust against this perturbation.

It is often not appreciated that different activity regimes can coexist with each other in a given neuron so that a transient stimulus can cause a persistent change of activity. Such multistability of the neuronal dynamics has in fact been shown in a variety of neurons and can play either a functional role or present a substrate for neurological diseases. We explored the propensity for multistability in a database of a leech heart interneuron model, testing each case (parameter set) in a database for multistability. We found a large proportion of multistable cases, especially the coexistence of silent and bursting regimes. This was a surprising result, since these cells pace the heartbeat of the leech, and the coexistence of silence and bursting could disrupt the functional pattern, threatening the viability of the leech. Analysis of networks of mutually inhibitory multistable neurons, however, showed robustness in maintaining functional activity, suggesting that the mutually inhibitory coupling can act as a protective mechanism against failures induced by multistability.