Regulation of afferent transmission in the feeding circuitry of Aplysia

Although feeding in Aplysia is mediated by a central pattern generator (CPG), the activity of this CPG is modified by afferent input. To determine how afferent activity produces the widespread changes in motor programs that are necessary if behavior is to be modified, we have studied two classes of feeding sensory neurons. We have shown that afferent-induced changes in activity are widespread because sensory neurons make a number of synaptic connections. For example, sensory neurons make monosynaptic excitatory connections with feeding motor neurons. Sensori-motor transmission is, however, regulated so that changes in the periphery do not disrupt ongoing activity. This results from the fact that sensory neurons are also electrically coupled to feeding interneurons. During motor programs sensory neurons are, therefore, rhythmically depolarized via central input. These changes in membrane potential profoundly affect sensori-motor transmission. For example, changes in membrane potential alter spike propagation in sensory neurons so that spikes are only actively transmitted to particular output regions when it is behaviorally appropriate. To summarize, afferent activity alters motor output because sensory neurons make direct contact with motor neurons. Sensori-motor transmission is, however, centrally regulated so that changes in the periphery alter motor programs in a phase-dependent manner.     

The chronotron: a neuron that learns to fire temporally precise spike patterns

In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons), one that provides high memory capacity (E-learning), and one that has a higher biological plausibility (I-learning). With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm.     

Allatostatin A Signalling in Drosophila Regulates Feeding and Sleep and Is Modulated by PDF

Feeding and sleep are fundamental behaviours with significant interconnections and cross-modulations. The circadian system and peptidergic signals are important components of this modulation, but still little is known about the mechanisms and networks by which they interact to regulate feeding and sleep. We show that specific thermogenetic activation of peptidergic Allatostatin A (AstA)-expressing PLP neurons and enteroendocrine cells reduces feeding and promotes sleep in the fruit fly Drosophila. The effects of AstA cell activation are mediated by AstA peptides with receptors homolog to galanin receptors subserving similar and apparently conserved functions in vertebrates. We further identify the PLP neurons as a downstream target of the neuropeptide pigment-dispersing factor (PDF), an output factor of the circadian clock. PLP neurons are contacted by PDF-expressing clock neurons, and express a functional PDF receptor demonstrated by cAMP imaging. Silencing of AstA signalling and continuous input to AstA cells by tethered PDF changes the sleep/activity ratio in opposite directions but does not affect rhythmicity. Taken together, our results suggest that pleiotropic AstA signalling by a distinct neuronal and enteroendocrine AstA cell subset adapts the fly to a digestive energy-saving state which can be modulated by PDF.     

Appetitive odor learning does not change olfactory coding in a subpopulation of honeybee antennal lobe neurons

Odors elicit spatio-temporal patterns of activity in the olfactory bulb of vertebrates and the antennal lobe of insects. There have been several reports of changes in these patterns following olfactory learning. These studies pose a conundrum: how can an animal learn to efficiently respond to a particular odor with an adequate response, if its primary representation already changes during this process? In this study, we offer a possible solution for this problem. We measured odor-evoked calcium responses in a subpopulation of uniglomerular AL output neurons in honeybees. We show that their responses to odors are remarkably resistant to plasticity following a variety of appetitive olfactory learning paradigms. There was no significant difference in the changes of odor-evoked activity between single and multiple trial forward or backward conditioning, differential conditioning, or unrewarded successive odor stimulation. In a behavioral learning experiment we show that these neurons are necessary for conditioned odor responses. We conclude that these uniglomerular projection neurons are necessary for reliable odor coding and are not modified by learning in this paradigm. The role that other projection neurons play in olfactory learning remains to be investigated.     

Stochastically Gating Ion Channels Enable Patterned Spike Firing through Activity-Dependent Modulation of Spike Probability

The transformation of synaptic input into patterns of spike output is a fundamental operation that is determined by the particular complement of ion channels that a neuron expresses. Although it is well established that individual ion channel proteins make stochastic transitions between conducting and non-conducting states, most models of synaptic integration are deterministic, and relatively little is known about the functional consequences of interactions between stochastically gating ion channels. Here, we show that a model of stellate neurons from layer II of the medial entorhinal cortex implemented with either stochastic or deterministically gating ion channels can reproduce the resting membrane properties of stellate neurons, but only the stochastic version of the model can fully account for perithreshold membrane potential fluctuations and clustered patterns of spike output that are recorded from stellate neurons during depolarized states. We demonstrate that the stochastic model implements an example of a general mechanism for patterning of neuronal output through activity-dependent changes in the probability of spike firing. Unlike deterministic mechanisms that generate spike patterns through slow changes in the state of model parameters, this general stochastic mechanism does not require retention of information beyond the duration of a single spike and its associated afterhyperpolarization. Instead, clustered patterns of spikes emerge in the stochastic model of stellate neurons as a result of a transient increase in firing probability driven by activation of HCN channels during recovery from the spike afterhyperpolarization. Using this model, we infer conditions in which stochastic ion channel gating may influence firing patterns in vivo and predict consequences of modifications of HCN channel function for in vivo firing patterns.     

A pair of inhibitory neurons are required to sustain labile memory in the Drosophila mushroom body

Labile memory is thought to be held in the brain as persistent neural network activity. However, it is not known how biologically relevant memory circuits are organized and operate. Labile and persistent appetitive memory in Drosophila requires output after training from the α'β' subset of mushroom body (MB) neurons and from a pair of modulatory dorsal paired medial (DPM) neurons. DPM neurons innervate the entire MB lobe region and appear to be pre- and postsynaptic to the MB, consistent with a recurrent network model. Here we identify a role after training for synaptic output from the GABAergic anterior paired lateral (APL) neurons. Blocking synaptic output from APL neurons after training disrupts labile memory but does not affect long-term memory. APL neurons contact DPM neurons most densely in the α'β' lobes, although their processes are intertwined and contact throughout all of the lobes. Furthermore, APL contacts MB neurons in the α' lobe but makes little direct contact with those in the distal α lobe. We propose that APL neurons provide widespread inhibition to stabilize and maintain synaptic specificity of a labile memory trace in a recurrent DPM and MB α'β' neuron circuit.     

Recurrent hypoglycemia alters hypothalamic expression of the regulatory proteins FosB and synaptophysin

A limiting factor to the clinical management of diabetes is iatrogenic hypoglycemia. With multiple hypoglycemic episodes, the collective neuroendocrine response that restores euglycemia is impaired. In our animal model of recurrent hypoglycemia (RH), neuroendocrine deficits are accompanied by a decrease in medial hypothalamic activation. Here we tested the hypothesis that the medial hypothalamus may exhibit unique changes in the expression of regulatory proteins in response to RH. We report that expression of the immediate early gene FosB is increased in medial hypothalamic nuclei, anterior hypothalamus, and posterior paraventricular nucleus of the thalamus (THPVN) of the thalamus following RH. We identified the hypothalamic PVN, a key autonomic output site, among the regions expressing FosB. To identify the subtype(s) of neuronal populations that express FosB, we screened candidate neuropeptides of the PVN for coexpression using dual fluorescence immunohistochemistry. Among the neuropeptides analyzed [including oxytocin, vasopressin, thyrotropin-releasing hormone, and corticotropin-releasing factor (CRF)], FosB was only identified in CRF-positive neurons. Inhibitory gamma-aminobutyric acid-positive processes appear to impinge on these FosB-expressing neurons. Finally, we observed a significant decrease in the presynaptic marker synaptophysin within the PVN of RH-treated vs. saline-treated rats, suggesting that rapid alterations of synaptic morphology may occur in association with RH. Collectively, these data suggest that RH stress triggers cellular changes that support synaptic plasticity, in specific neuroanatomical sites, which may contribute to the development of hypoglycemia-associated autonomic failure.     

Conventional measures of intrinsic excitability are poor estimators of neuronal activity under realistic synaptic inputs

Activity-dependent regulation of intrinsic excitability has been shown to greatly contribute to the overall plasticity of neuronal circuits. Such neuroadaptations are commonly investigated in patch clamp experiments using current step stimulation and the resulting input-output functions are analyzed to quantify alterations in intrinsic excitability. However, it is rarely addressed, how such changes translate to the function of neurons when they operate under natural synaptic inputs. Still, it is reasonable to expect that a strong correlation and near proportional relationship exist between static firing responses and those evoked by synaptic drive. We challenge this view by performing a high-yield electrophysiological analysis of cultured mouse hippocampal neurons using both standard protocols and simulated synaptic inputs via dynamic clamp. We find that under these conditions the neurons exhibit vastly different firing responses with surprisingly weak correlation between static and dynamic firing intensities. These contrasting responses are regulated by two intrinsic K-currents mediated by Kv1 and K_ir channels, respectively. Pharmacological manipulation of the K-currents produces differential regulation of the firing output of neurons. Static firing responses are greatly increased in stuttering type neurons under blocking their Kv1 channels, while the synaptic responses of the same neurons are less affected. Pharmacological blocking of K_ir-channels in delayed firing type neurons, on the other hand, exhibit the opposite effects. Our subsequent computational model simulations confirm the findings in the electrophysiological experiments and also show that adaptive changes in the kinetic properties of such currents can even produce paradoxical regulation of the firing output.     

Modelling of the qualitative discrimination of odours in the first two layers of olfactory system by Jutten and Hérault algorithm.

The olfactory system discriminates many different odours although membrane receptors, and neuroreceptors that bear them, are only broadly tuned to volatile molecules. We investigated how mixed and redundant information carried by neuroreceptors could be efficiently coded by output neurons in the second neuronal layer in the brain. We showed that a good discrimination is yielded if neuroreceptors that terminate on the same output neuron belong to several types, if these types are in different proportions for each output neuron, and if each output neuron exerts an action on all others so that their independence is maximized, according to the Jutten and Hérault algorithm for separating linear mixtures of independent sources. The proposed model details the computations made by neurons and leads to a better understanding of several observed properties of the olfactory system, among which the inhibitory actions of local neurons of the second layer and the identifiability of insect glomeruli.     

Short-term memory of motor network performance via activity-dependent potentiation of Na+/K+ pump function

Brain networks memorize previous performance to adjust their output in light of past experience. These activity-dependent modifications generally result from changes in synaptic strengths or ionic conductances, and ion pumps have only rarely been demonstrated to play a dynamic role. Locomotor behavior is produced by central pattern generator (CPG) networks and modified by sensory and descending signals to allow for changes in movement frequency, intensity, and duration, but whether or how the CPG networks recall recent activity is largely unknown. In Xenopus frog tadpoles, swim bout duration correlates linearly with interswim interval, suggesting that the locomotor network retains a short-term memory of previous output. We discovered an ultraslow, minute-long afterhyperpolarization (usAHP) in network neurons following locomotor episodes. The usAHP is mediated by an activity- and sodium spike-dependent enhancement of electrogenic Na(+)/K(+) pump function. By integrating spike frequency over time and linking the membrane potential of spinal neurons to network performance, the usAHP plays a dynamic role in short-term motor memory. Because Na(+)/K(+) pumps are ubiquitously expressed in neurons of all animals and because sodium spikes inevitably accompany network activity, the usAHP may represent a phylogenetically conserved but largely overlooked mechanism for short-term memory of neural network function.     

Analysis of cell-type-specific chromatin modifications and gene expression in Drosophila neurons that direct reproductive behavior

Examining the role of chromatin modifications and gene expression in neurons is critical for understanding how the potential for behaviors are established and maintained. We investigate this question by examining Drosophila melanogaster fru P1 neurons that underlie reproductive behaviors in both sexes. We developed a method to purify cell-type-specific chromatin (Chromatag), using a tagged histone H2B variant that is expressed using the versatile Gal4/UAS gene expression system. Here, we use Chromatag to evaluate five chromatin modifications, at three life stages in both sexes. We find substantial changes in chromatin modification profiles across development and fewer differences between males and females. Additionally, we find chromatin modifications that persist in different sets of genes from pupal to adult stages, which may point to genes important for cell fate determination in fru P1 neurons. We generated cell-type-specific RNA-seq data sets, using translating ribosome affinity purification (TRAP). We identify actively translated genes in fru P1 neurons, revealing novel stage- and sex-differences in gene expression. We also find chromatin modification enrichment patterns that are associated with gene expression. Next, we use the chromatin modification data to identify cell-type-specific super-enhancer-containing genes. We show that genes with super-enhancers in fru P1 neurons differ across development and between the sexes. We validated that a set of genes are expressed in fru P1 neurons, which were chosen based on having a super-enhancer and TRAP-enriched expression in fru P1 neurons.     

Finite volume and asymptotic methods for stochastic neuron models with correlated inputs

We consider a pair of stochastic integrate and fire neurons receiving correlated stochastic inputs. The evolution of this system can be described by the corresponding Fokker?CPlanck equation with non-trivial boundary conditions resulting from the refractory period and firing threshold. We propose a finite volume method that is orders of magnitude faster than the Monte Carlo methods traditionally used to model such systems. The resulting numerical approximations are proved to be accurate, nonnegative and integrate to 1. We also approximate the transient evolution of the system using an Ornstein?CUhlenbeck process, and use the result to examine the properties of the joint output of cell pairs. The results suggests that the joint output of a cell pair is most sensitive to changes in input variance, and less sensitive to changes in input mean and correlation.     

LMAN lesions prevent song degradation after deafening without reducing HVC neuron addition

In some songbirds perturbing auditory feedback can promote changes in song structure well beyond the end of song learning. One factor that may drive vocal change in such deafened birds is the ongoing addition of new vocal-motor neurons into the song system. Without auditory feedback to guide their incorporation, the addition of these new neurons could disrupt the established song pattern. To assess this hypothesis, the authors determined if neuronal recruitment into the vocal motor nucleus HVC is affected by neural signals that influence vocal change in adult deafened birds. Such signals appear to be conveyed via LMAN, a nucleus in the anterior forebrain that is necessary for vocal change after deafening. Here the authors tested whether LMAN lesions might restrict song degradation after deafening by reducing the addition or survival of new HVC neurons that would otherwise corrupt the ongoing song pattern. Using [3H]thymidine autoradiography to identify neurons generated in adult zebra finches, it was shown here that LMAN lesions do not reduce the number or percent of new HVC neurons surviving for either several weeks or months after [3H]thymidine labeling. However, the authors confirmed previous reports that LMAN lesions restrict vocal change after deafening. These data suggest that neurons incorporated into the adult HVC may form behaviorally adaptive connections without requiring auditory feedback, and that any role such neurons may play in promoting vocal change after adult deafening requires anterior forebrain pathway output.     

The Mechanism of Saccade Motor Pattern Generation Investigated by a Large-Scale Spiking Neuron Model of the Superior Colliculus

The subcortical saccade-generating system consists of the retina, superior colliculus, cerebellum and brainstem motoneuron areas. The superior colliculus is the site of sensory-motor convergence within this basic visuomotor loop preserved throughout the vertebrates. While the system has been extensively studied, there are still several outstanding questions regarding how and where the saccade eye movement profile is generated and the contribution of respective parts within this system. Here we construct a spiking neuron model of the whole intermediate layer of the superior colliculus based on the latest anatomy and physiology data. The model consists of conductance-based spiking neurons with quasi-visual, burst, buildup, local inhibitory, and deep layer inhibitory neurons. The visual input is given from the superficial superior colliculus and the burst neurons send the output to the brainstem oculomotor nuclei. Gating input from the basal ganglia and an integral feedback from the reticular formation are also included.We implement the model in the NEST simulator and show that the activity profile of bursting neurons can be reproduced by a combination of NMDA-type and cholinergic excitatory synaptic inputs and integrative inhibitory feedback. The model shows that the spreading neural activity observed in vivo can keep track of the collicular output over time and reset the system at the end of a saccade through activation of deep layer inhibitory neurons. We identify the model parameters according to neural recording data and show that the resulting model recreates the saccade size-velocity curves known as the saccadic main sequence in behavioral studies. The present model is consistent with theories that the superior colliculus takes a principal role in generating the temporal profiles of saccadic eye movements, rather than just specifying the end points of eye movements.     

Deafening drives cell-type-specific changes to dendritic spines in a sensorimotor nucleus important to learned vocalizations

Hearing loss prevents vocal learning and causes learned vocalizations to deteriorate, but how vocalization-related auditory feedback acts on neural circuits that control vocalization remains poorly understood. We deafened adult zebra finches, which rely on auditory feedback to maintain their learned songs, to test the hypothesis that deafening modifies synapses on neurons in a sensorimotor nucleus important to song production. Longitudinal in vivo imaging revealed that deafening selectively decreased the size and stability of dendritic spines on neurons that provide input to a striatothalamic pathway important to audition-dependent vocal plasticity, and changes in spine size preceded and predicted subsequent vocal degradation. Moreover, electrophysiological recordings from these neurons showed that structural changes were accompanied by functional weakening of both excitatory and inhibitory synapses, increased intrinsic excitability, and changes in spontaneous action potential output. These findings shed light on where and how auditory feedback acts within sensorimotor circuits to shape learned vocalizations.     

The Neuropeptide PDF Acts Directly on Evening Pacemaker Neurons to Regulate Multiple Features of Circadian Behavior

Discrete clusters of circadian clock neurons temporally organize daily behaviors such as sleep and wake. In Drosophila, a network of just 150 neurons drives two peaks of timed activity in the morning and evening. A subset of these neurons expresses the neuropeptide pigment dispersing factor (PDF), which is important for promoting morning behavior as well as maintaining robust free-running rhythmicity in constant conditions. Yet, how PDF acts on downstream circuits to mediate rhythmic behavior is unknown. Using circuit-directed rescue of PDF receptor mutants, we show that PDF targeting of just ~30 non-PDF evening circadian neurons is sufficient to drive morning behavior. This function is not accompanied by large changes in core molecular oscillators in light-dark, indicating that PDF RECEPTOR likely regulates the output of these cells under these conditions. We find that PDF also acts on this focused set of non-PDF neurons to regulate both evening activity phase and period length, consistent with modest resetting effects on core oscillators. PDF likely acts on more distributed pacemaker neuron targets, including the PDF neurons themselves, to regulate rhythmic strength. Here we reveal defining features of the circuit-diagram for PDF peptide function in circadian behavior, revealing the direct neuronal targets of PDF as well as its behavioral functions at those sites. These studies define a key direct output circuit sufficient for multiple PDF dependent behaviors.     

A Model of Self-Organizing Head-Centered Visual Responses in Primate Parietal Areas

We present a hypothesis for how head-centered visual representations in primate parietal areas could self-organize through visually-guided learning, and test this hypothesis using a neural network model. The model consists of a competitive output layer of neurons that receives afferent synaptic connections from a population of input neurons with eye position gain modulated retinal receptive fields. The synaptic connections in the model are trained with an associative trace learning rule which has the effect of encouraging output neurons to learn to respond to subsets of input patterns that tend to occur close together in time. This network architecture and synaptic learning rule is hypothesized to promote the development of head-centered output neurons during periods of time when the head remains fixed while the eyes move. This hypothesis is demonstrated to be feasible, and each of the core model components described is tested and found to be individually necessary for successful self-organization.     

Circuits constructed from identified Aplysia neurons exhibit multiple patterns of persistent activity.

We have used identified neurons from the abdominal ganglion of the mollusc Aplysia to construct and analyze two circuits in vitro. Each of these circuits was capable of producing two patterns of persistent activity; that is, they had bistable output states. The output could be switched between the stable states by a brief, external input. One circuit consisted of cocultured L10 and left upper quadrant (LUQ) neurons that formed reciprocal, inhibitory connections. In one stable state L10 was active and the LUQ was quiescent, whereas in the other stable state L10 was quiescent and the LUQ was active. A second circuit consisted of co-cultured L7 and L12 neurons that formed reciprocal, excitatory connections. In this circuit, both cells were quiescent in one stable state and both cells fired continuously in the other state. Bistable output in both circuits resulted from the nonlinear firing characteristics of each neuron and the feedback between the two neurons. We explored how the stability of the neuronal output could be controlled by the background currents injected into each neuron. We observed a relatively well-defined range of currents for which bistability occurred, consistent with the values expected from the measured strengths of the connections and a simple model. Outside of the range, the output was stable in only a single state. These results suggest how stable patterns of output are produced by some in vivo circuits and how command neurons from higher neural centers may control the activity of these circuits. The criteria that guided us in forming our circuits in culture were derived from theoretical studies on the properties of certain neuronal network models (e.g., Hopfield, J. J. 1984. Proc. Natl. Acad. Sci. USA. 81:3088-3092). Our results show that circuits consisting of only two co-cultured neurons can exhibit bistable output states of the form hypothesized to occur in populations of neurons.