Synaptic modulation of the interspike interval signatures of bursting pyloric neurons

Synaptic Dynamics Convey Differential Sensitivity to Input Pattern Changes in Two Muscles Innervated by the Same Motor Neurons

Postsynaptic responses depend on input patterns as well as short-term synaptic plasticity, summation, and postsynaptic membrane properties, but the interactions of those dynamics with realistic input patterns are not well understood. We recorded the responses of the two pyloric dilator (PD) muscles, cpv2a and cpv2b, that are innervated by and receive identical periodic bursting input from the same two motor neurons in the lobster Homarus americanus. Cpv2a and cpv2b showed quantitative differences in membrane nonlinearities and synaptic summation. At a short timescale, responses in both muscles were dominated by facilitation, albeit with different frequency and time dependence. Realistic burst stimulations revealed more substantial differences. Across bursts, cpv2a showed transient depression, whereas cpv2b showed transient facilitation. Steady-state responses to bursting input also differed substantially. Neither muscle had a monotonic dependence on frequency, but cpv2b showed particularly pronounced bandpass filtering. Cpv2a was sensitive to changes in both burst frequency and intra-burst spike frequency, whereas, despite its much slower responses, cpv2b was largely insensitive to changes in burst frequency. Cpv2a was sensitive to both burst duration and number of spikes per burst, whereas cpv2b was sensitive only to the former parameter. Neither muscle showed consistent sensitivity to changes in the overall spike interval structure, but cpv2b was surprisingly sensitive to changes in the first intervals in each burst, a parameter known to be regulated by dopamine (DA) modulation of spike propagation of the presynaptic axon. These findings highlight how seemingly minor circuit output changes mediated by neuromodulation could be read out differentially at the two synapses.

Detection of Activation Sequences in Spiking-Bursting Neurons by means of the Recognition of Intraburst Neural Signatures

Bursting activity is present in many cells of different nervous systems playing important roles in neural information processing. Multiple assemblies of bursting neurons act cooperatively to produce coordinated spatio-temporal patterns of sequential activity. A major goal in neuroscience is unveiling the mechanisms underlying neural information processing based on this sequential dynamics. Experimental findings have revealed the presence of precise cell-type-specific intraburst firing patterns in the activity of some bursting neurons. This characteristic neural signature coexists with the information encoded in other aspects of the spiking-bursting signals, and its functional meaning is still unknown. We investigate the ability of a neuron conductance-based model to detect specific presynaptic activation sequences taking advantage of intraburst fingerprints identifying the source of the signals building up a sequential pattern of activity. Our simulations point out that a reader neuron could use this information to contextualize incoming signals and accordingly compute a characteristic response by relying on precise phase relationships among the activity of different emitters. This would provide individual neurons enhanced capabilities to control and negotiate sequential dynamics. In this regard, we discuss the possible implications of the proposed contextualization mechanism for neural information processing.

Bridging pro-inflammatory signals, synaptic transmission and protection in spinal explants in vitro

Multiple sclerosis is characterized by tissue atrophy involving the brain and the spinal cord, where reactive inflammation contributes to the neurodegenerative processes. Recently, the presence of synapse alterations induced by the inflammatory responses was suggested by experimental and clinical observations, in experimental autoimmune encephalomyelitis mouse model and in patients, respectively. Further knowledge on the interplay between pro-inflammatory agents, neuroglia and synaptic dysfunction is crucial to the design of unconventional protective molecules. Here we report the effects, on spinal cord circuits, of a cytokine cocktail that partly mimics the signature of T lymphocytes sub population Th1. In embryonic mouse spinal organ-cultures, containing neuronal cells and neuroglia, cytokines induced inflammatory responses accompanied by a significant increase in spontaneous synaptic activity. We suggest that cytokines specifically altered signal integration in spinal networks by speeding the decay of GABA_A responses. This hypothesis is supported by the finding that synapse protection by a non-peptidic NGF mimetic molecule prevented both the changes in the time course of GABA events and in network activity that were left unchanged by the cytokine production from astrocytes and microglia present in the cultured tissue. In conclusion, we developed an important tool for the study of synaptic alterations induced by inflammation, that takes into account the role of neuronal and not neuronal resident cells.

Ionic mechanisms underlying history-dependence of conduction delay in an unmyelinated axon

Axonal conduction velocity can change substantially during ongoing activity, thus modifying spike interval structures and, potentially, temporal coding. We used a biophysical model to unmask mechanisms underlying the history-dependence of conduction. The model replicates activity in the unmyelinated axon of the crustacean stomatogastric pyloric dilator neuron. At the timescale of a single burst, conduction delay has a non-monotonic relationship with instantaneous frequency, which depends on the gating rates of the fast voltage-gated Na⁺ current. At the slower timescale of minutes, the mean value and variability of conduction delay increase. These effects are because of hyperpolarization of the baseline membrane potential by the Na⁺/K⁺ pump, balanced by an h-current, both of which affect the gating of the Na⁺ current. We explore the mechanisms of history-dependence of conduction delay in axons and develop an empirical equation that accurately predicts this history-dependence, both in the model and in experimental measurements.

Implementing Signature Neural Networks with Spiking Neurons

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks.

Dynamics of Time Delay-Induced Multiple Synchronous Behaviors in Inhibitory Coupled Neurons

The inhibitory synapse can induce synchronous behaviors different from the anti-phase synchronous behaviors, which have been reported in recent studies. In the present paper, synchronous behaviors are investigated in the motif model composed of reciprocal inhibitory coupled neurons with endogenous bursting and time delay. When coupling strength is weak, synchronous behavior appears at a single interval of time delay within a bursting period. When coupling strength is strong, multiple synchronous behaviors appear at different intervals of time delay within a bursting period. The different bursting patterns of synchronous behaviors, and time delays and coupling strengths that can induce the synchronous bursting patterns can be well interpreted by the dynamics of the endogenous bursting pattern of isolated neuron, which is acquired by the fast-slow dissection method, combined with the inhibitory coupling current. For an isolated neuron, when a negative impulsive current with suitable strength is applied at different phases of the bursting, multiple different bursting patterns can be induced. For a neuron in the motif, the inhibitory coupling current, of which the application time and strength is modulated by time delay and coupling strength, can cause single or multiple synchronous firing patterns like the negative impulsive current when time delay and coupling strength is suitable. The difference compared to the previously reported multiple synchronous behaviors that appear at time delays wider than a period of the endogenous firing is discussed. The results present novel examples of synchronous behaviors in the neuronal network with inhibitory synapses and provide a reasonable explanation.

Pituitary Adenylate Cyclase-Activating Peptide in the Central Amygdala Causes Anorexia and Body Weight Loss via the Melanocortin and the TrkB Systems

Growing evidence suggests that the pituitary adenylate cyclase-activating polypeptide (PACAP)/PAC1 receptor system represents one of the main regulators of the behavioral, endocrine, and autonomic responses to stress. Although induction of anorexia is a well-documented effect of PACAP, the central sites underlying this phenomenon are poorly understood. The present studies addressed this question by examining the neuroanatomical, behavioral, and pharmacological mechanisms mediating the anorexia produced by PACAP in the central nucleus of the amygdala (CeA), a limbic structure implicated in the emotional components of ingestive behavior. Male rats were microinfused with PACAP (0-1 μg per rat) into the CeA and home-cage food intake, body weight change, microstructural analysis of food intake, and locomotor activity were assessed. Intra-CeA (but not intra-basolateral amygdala) PACAP dose-dependently induced anorexia and body weight loss without affecting locomotor activity. PACAP-treated rats ate smaller meals of normal duration, revealing that PACAP slowed feeding within meals by decreasing the regularity and maintenance of feeding from pellet-to-pellet; postprandial satiety was unaffected. Intra-CeA PACAP-induced anorexia was blocked by coinfusion of either the melanocortin receptor 3/4 antagonist SHU 9119 or the tyrosine kinase B (TrKB) inhibitor k-252a, but not the CRF receptor antagonist D-Phe-CRF(12-41). These results indicate that the CeA is one of the brain areas through which the PACAP system promotes anorexia and that PACAP preferentially lessens the maintenance of feeding in rats, effects opposite to those of palatable food. We also demonstrate that PACAP in the CeA exerts its anorectic effects via local melanocortin and the TrKB systems, and independently from CRF.

Neural dynamics based on the recognition of neural fingerprints

Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g., individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i) the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii) the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e., specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible, and powerful strategy.

Separating burst from background spikes in multichannel neuronal recordings using return map analysis

We propose a preprocessing method to separate coherent neuronal network activity, referred to as “bursts”, from background spikes. High background activity in neuronal recordings reduces the effectiveness of currently available burst detection methods. For long-term, stationary recordings, burst and background spikes have a bimodal ISI distribution which makes it easy to select the threshold to separate burst and background spikes. Finite, nonstationary recordings lead to noisy ISIs for which the bimodality is not that clear. We introduce a preprocessing method to separate burst from background spikes to improve burst detection reliability because it efficiently uses both single and multichannel activity. The method is tested using a stochastic model constrained by data available in the literature and recordings from primary cortical neurons cultured on multielectrode arrays. The separation between burst and background spikes is obtained using the interspike interval return map. The cutoff threshold is the key parameter to separate the burst and background spikes. We compare two methods for selecting the threshold. The 2-step method, in which threshold selection is based on fixed heuristics. The iterative method, in which the optimal cutoff threshold is directly estimated from the data. The proposed preprocessing method significantly increases the reliability of several established burst detection algorithms, both for simulated and real recordings. The preprocessing method makes it possible to study the effects of diseases or pharmacological manipulations, because it can deal efficiently with nonstationarity in the data.

Noise, transient dynamics, and the generation of realistic interspike interval variation in square-wave burster neurons

First return maps of interspike intervals for biological neurons that generate repetitive bursts of impulses can display stereotyped structures (neuronal signatures). Such structures have been linked to the possibility of multicoding and multifunctionality in neural networks that produce and control rhythmical motor patterns. In some cases, isolating the neurons from their synaptic network reveals irregular, complex signatures that have been regarded as evidence of intrinsic, chaotic behavior. We show that incorporation of dynamical noise into minimal neuron models of square-wave bursting (either conductance-based or abstract) produces signatures akin to those observed in biological examples, without the need for fine tuning of parameters or ad hoc constructions for inducing chaotic activity. The form of the stochastic term is not strongly constrained and can approximate several possible sources of noise, e.g., random channel gating or synaptic bombardment. The cornerstone of this signature generation mechanism is the rich, transient, but deterministic dynamics inherent in the square-wave (saddle-node and homoclinic) mode of neuronal bursting. We show that noise causes the dynamics to populate a complex transient scaffolding or skeleton in state space, even for models that (without added noise) generate only periodic activity (whether in bursting or tonic spiking mode).

Generalization of the dynamic clamp concept in neurophysiology and behavior

The idea of closed-loop interaction in in vitro and in vivo electrophysiology has been successfully implemented in the dynamic clamp concept strongly impacting the research of membrane and synaptic properties of neurons. In this paper we show that this concept can be easily generalized to build other kinds of closed-loop protocols beyond (or in addition to) electrical stimulation and recording in neurophysiology and behavioral studies for neuroethology. In particular, we illustrate three different examples of goal-driven real-time closed-loop interactions with drug microinjectors, mechanical devices and video event driven stimulation. Modern activity-dependent stimulation protocols can be used to reveal dynamics (otherwise hidden under traditional stimulation techniques), achieve control of natural and pathological states, induce learning, bridge between disparate levels of analysis and for a further automation of experiments. We argue that closed-loop interaction calls for novel real time analysis, prediction and control tools and a new perspective for designing stimulus-response experiments, which can have a large impact in neuroscience research.

Dopamine modulation of Ih improves temporal fidelity of spike propagation in an unmyelinated axon

We studied how conduction delays of action potentials in an unmyelinated axon depended on the history of activity and how this dependence was changed by the neuromodulator dopamine (DA). The pyloric dilator axons of the stomatogastric nervous system in the lobster, Homarus americanus, exhibited substantial activity-dependent hyperpolarization and changes in spike shape during repetitive activation. The conduction delays varied by several milliseconds per centimeter, and, during activation with realistic burst patterns or Poisson-like patterns, changes in delay occurred over multiple timescales. The mean delay increased, whereas the resting membrane potential hyperpolarized with a time constant of several minutes. Concomitantly with the mean delay, the variability of delay also increased. The variability of delay was not a linear or monotonic function of instantaneous spike frequency or spike shape parameters, and the relationship between these parameters changed with the increase in mean delay. Hyperpolarization was counteracted by a hyperpolarization-activated inward current (I(h)), and the magnitude of I(h) critically determined the temporal fidelity of spike propagation. Pharmacological block of I(h) increased the change in delay and the variability of delay, and increasing I(h) by application of DA diminished both. Consequently, the temporal fidelity of pattern propagation was substantially improved in DA. Standard measurements of changes in excitability or delay with paired stimuli or tonic stimulation failed to capture the dynamics of spike conduction. These results indicate that spike conduction can be extremely sensitive to the history of axonal activity and to the presence of neuromodulators, with potentially important consequences for temporal coding.

Co-variation of ionic conductances supports phase maintenance in stomatogastric neurons

Neuronal networks produce reliable functional output throughout the lifespan of an animal despite ceaseless molecular turnover and a constantly changing environment. Central pattern generators, such as those of the crustacean stomatogastric ganglion (STG), are able to robustly maintain their functionality over a wide range of burst periods. Previous experimental work involving extracellular recordings of the pyloric pattern of the STG has demonstrated that as the burst period varies, the inter-neuronal delays are altered proportionally, resulting in burst phases that are roughly invariant. The question whether spike delays within bursts are also proportional to pyloric period has not been explored in detail. The mechanism by which the pyloric neurons accomplish phase maintenance is currently not obvious. Previous studies suggest that the co-regulation of certain ion channel properties may play a role in governing neuronal activity. Here, we observed in long-term recordings of the pyloric rhythm that spike delays can vary proportionally with burst period, so that spike phase is maintained. We then used a conductance-based model neuron to determine whether co-varying ionic membrane conductances results in neural output that emulates the experimentally observed phenomenon of spike phase maintenance. Next, we utilized a model neuron database to determine whether conductance correlations exist in model neuron populations with highly maintained spike phases. We found that co-varying certain conductances, including the sodium and transient calcium conductance pair, causes the model neuron to maintain a specific spike phase pattern. Results indicate a possible relationship between conductance co-regulation and phase maintenance in STG neurons.

Single synapse information coding in intraburst spike patterns of central pattern generator motor neurons

Burst firing is ubiquitous in nervous systems and has been intensively studied in central pattern generators (CPGs). Previous works have described subtle intraburst spike patterns (IBSPs) that, despite being traditionally neglected for their lack of relation to CPG motor function, were shown to be cell-type specific and sensitive to CPG connectivity. Here we address this matter by investigating how a bursting motor neuron expresses information about other neurons in the network. We performed experiments on the crustacean stomatogastric pyloric CPG, both in control conditions and interacting in real-time with computer model neurons. The sensitivity of postsynaptic to presynaptic IBSPs was inferred by computing their average mutual information along each neuron burst. We found that details of input patterns are nonlinearly and inhomogeneously coded through a single synapse into the fine IBSPs structure of the postsynaptic neuron following burst. In this way, motor neurons are able to use different time scales to convey two types of information simultaneously: muscle contraction (related to bursting rhythm) and the behavior of other CPG neurons (at a much shorter timescale by using IBSPs as information carriers). Moreover, the analysis revealed that the coding mechanism described takes part in a previously unsuspected information pathway from a CPG motor neuron to a nerve that projects to sensory brain areas, thus providing evidence of the general physiological role of information coding through IBSPs in the regulation of neuronal firing patterns in remote circuits by the CNS.

Delay-induced intermittent transition of synchronization in neuronal networks with hybrid synapses

We study the dependence of synchronization transitions in scale-free networks of bursting neurons with hybrid synapses on the information transmission delay and the probability of inhibitory synapses. It is shown that, irrespective of the probability of inhibitory synapses, the delay always plays a subtle role during synchronization transition of the scale-free neuronal networks. In particular, regions of irregular and regular propagating excitatory fronts appear intermittently as the delay increases. These delay-induced synchronization transitions are manifested as well-expressed minima in the measure for spatiotemporal synchrony. In addition, it is found that, for smaller and larger probability of inhibitory synapses, intermittent synchronization transition is relatively profound, while for the moderate probability of inhibitory synapses, synchronization transition seems less profound. More interestingly, it is found that as the probability of inhibitory synapses is large, regions of synchronization are upscattering.

History-dependent excitability as a single-cell substrate of transient memory for information discrimination

Neurons react differently to incoming stimuli depending upon their previous history of stimulation. This property can be considered as a single-cell substrate for transient memory, or context-dependent information processing: depending upon the current context that the neuron "sees" through the subset of the network impinging on it in the immediate past, the same synaptic event can evoke a postsynaptic spike or just a subthreshold depolarization. We propose a formal definition of History-Dependent Excitability (HDE) as a measure of the propensity to firing in any moment in time, linking the subthreshold history-dependent dynamics with spike generation. This definition allows the quantitative assessment of the intrinsic memory for different single-neuron dynamics and input statistics. We illustrate the concept of HDE by considering two general dynamical mechanisms: the passive behavior of an Integrate and Fire (IF) neuron, and the inductive behavior of a Generalized Integrate and Fire (GIF) neuron with subthreshold damped oscillations. This framework allows us to characterize the sensitivity of different model neurons to the detailed temporal structure of incoming stimuli. While a neuron with intrinsic oscillations discriminates equally well between input trains with the same or different frequency, a passive neuron discriminates better between inputs with different frequencies. This suggests that passive neurons are better suited to rate-based computation, while neurons with subthreshold oscillations are advantageous in a temporal coding scheme. We also address the influence of intrinsic properties in single-cell processing as a function of input statistics, and show that intrinsic oscillations enhance discrimination sensitivity at high input rates. Finally, we discuss how the recognition of these cell-specific discrimination properties might further our understanding of neuronal network computations and their relationships to the distribution and functional connectivity of different neuronal types.

Encoding of spatio-temporal input characteristics by a CA1 pyramidal neuron model

The in vivo activity of CA1 pyramidal neurons alternates between regular spiking and bursting, but how these changes affect information processing remains unclear. Using a detailed CA1 pyramidal neuron model, we investigate how timing and spatial arrangement variations in synaptic inputs to the distal and proximal dendritic layers influence the information content of model responses. We find that the temporal delay between activation of the two layers acts as a switch between excitability modes: short delays induce bursting while long delays decrease firing. For long delays, the average firing frequency of the model response discriminates spatially clustered from diffused inputs to the distal dendritic tree. For short delays, the onset latency and inter-spike-interval succession of model responses can accurately classify input signals as temporally close or distant and spatially clustered or diffused across different stimulation protocols. These findings suggest that a CA1 pyramidal neuron may be capable of encoding and transmitting presynaptic spatiotemporal information about the activity of the entorhinal cortex-hippocampal network to higher brain regions via the selective use of either a temporal or a rate code.

Pyramidal neurons in the hippocampus are crucially involved in learning and memory functions, but the ways in which they contribute to the processing of sensory inputs and their internal representation remain mostly unclear. The principal neurons of the CA1 region of the hippocampus are surrounded by at least 21 different types of interneurons. This feature, together with the fact that CA1 pyramidal dendrites associate two major glutamatergic inputs arriving from the entorhinal cortex, makes it laborious to track the ‘how’ and ‘what’ of synaptic integration. The present study tries to shed light on the ‘what’, that is, the information content of the CA1 discharge pattern. Using a detailed biophysical CA1 neuron model, we show that the output of the model neuron contains spatial and temporal features of the incoming synaptic input. This information lies in the temporal pattern of the inter-spike intervals produced during the bursting activity which is induced by the temporal coincidence of the two activated synaptic streams. Our findings suggest that CA1 pyramidal neurons may be capable of capturing features of the ongoing network activity via the use of a temporal code for information transfer.

A method for decoding the neurophysiological spike-response transform

Many physiological responses elicited by neuronal spikes-intracellular calcium transients, synaptic potentials, muscle contractions-are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions-the elementary response kernel and additional kernels or functions that describe the dependence on previous history-that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the "synaptic decoding" approach of Sen et al. (1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms.

The patterns of spontaneous Ca2+ signals generated by ventral spinal neurons in vitro show time-dependent refinement

Embryonic spinal neurons maintained in organotypic slice culture are known to mimic certain maturation-dependent signalling changes. With such a model we investigated, in embryonic mouse spinal segments, the age-dependent spatio-temporal control of intracellular Ca(2+) signalling generated by neuronal populations in ventral circuits and its relation with electrical activity. We used Ca(2+) imaging to monitor areas located within the ventral spinal horn at 1 and 2 weeks of in vitro growth. Primitive patterns of spontaneous neuronal Ca(2+) transients (detected at 1 week) were typically synchronous. Remarkably, such transients originated from widespread propagating waves that became organized into large-scale rhythmic bursts. These activities were associated with the generation of synaptically mediated inward currents under whole-cell patch-clamp. Such patterns disappeared during longer culture of spinal segments: at 2 weeks in culture, only a subset of ventral neurons displayed spontaneous, asynchronous and repetitive Ca(2+) oscillations dissociated from background synaptic activity. We observed that the emergence of oscillations was a restricted phenomenon arising together with the transformation of ventral network electrophysiological bursting into asynchronous synaptic discharges. This change was accompanied by the appearance of discrete calbindin immunoreactivity against an unchanged background of calretinin-positive cells. It is attractive to assume that periodic oscillations of Ca(2+) confer a summative ability to these cells to shape the plasticity of local circuits through different changes (phasic or tonic) in intracellular Ca(2+).

GABAergic and glycinergic interneuron expression during spinal cord development: dynamic interplay between inhibition and excitation in the control of ventral network outputs

A key objective of neuroscience research is to understand the processes leading to mature neural circuitries in the central nervous system (CNS) that enable the control of different behaviours. During development, network-constitutive neurons undergo dramatic rearrangements, involving their intrinsic properties, such as the blend of ion channels governing their firing activity, and their synaptic interactions. The spinal cord is no exception to this rule; in fact, in the ventral horn the maturation of motor networks into functional circuits is a complex process where several mechanisms cooperate to achieve the development of motor control. Elucidating such a process is crucial in identifying neurons more vulnerable to degenerative or traumatic diseases or in developing new strategies aimed at rebuilding damaged tissue. The focus of this review is on recent advances in understanding the spatio-temporal expression of the glycinergic/GABAergic system and on the contribution of this system to early network function and to motor pattern transformation along with spinal maturation. During antenatal development, the operation of mammalian spinal networks strongly depends on the activity of glycinergic/GABAergic neurons, whose action is often excitatory until shortly before birth when locomotor networks acquire the ability to generate alternating motor commands between flexor and extensor motor neurons. At this late stage of prenatal development, GABA-mediated excitation is replaced by synaptic inhibition mediated by glycine and/or GABA. At this stage of spinal maturation, the large majority of GABAergic neurons are located in the dorsal horn. We propose that elucidating the role of inhibitory systems in development will improve our knowledge on the processes regulating spinal cord maturation.

Complex intrinsic membrane properties and dopamine shape spiking activity in a motor axon

We studied the peripheral motor axons of the two pyloric dilator (PD) neurons of the stomatogastric ganglion in the lobster, Homarus americanus. Intracellular recordings from the motor nerve showed both fast and slow voltage- and activity-dependent dynamics. During rhythmic bursts, the PD axons displayed changes in spike amplitude and duration. Pharmacological experiments and the voltage dependence of these phenomena suggest that inactivation of sodium and A-type potassium channels are responsible. In addition, the "resting" membrane potential was dependent on ongoing spike or burst activity, with more hyperpolarized values when activity was strong. Nerve stimulations, pharmacological block and current clamp experiments suggest that this is due to a functional antagonism between a slow after-hyperpolarization (sAHP) and inward rectification through hyperpolarization-activated current (IH). Dopamine application resulted in modest depolarization and "ectopic" peripheral spike initiation in the absence of centrally generated activity. This effect was blocked by CsCl and ZD7288, consistent with a role of IH. High frequency nerve stimulation inhibited peripheral spike initiation for several seconds, presumably due to the sAHP. Both during normal bursting activity and antidromic nerve stimulation, the conduction delay over the length of the peripheral nerve changed in a complex manner. This suggests that axonal membrane dynamics can have a substantial effect on the temporal fidelity of spike patterns propagated from a spike initiation site to a synaptic target, and that neuromodulators can influence the extent to which spike patterns are modified.

Determining Burst Firing Time Distributions from Multiple Spike Trains

Recent experimental findings have shown the presence of robust and cell-type-specific intraburst firing patterns in bursting neurons. We address the problem of characterizing these patterns under the assumption that the bursts exhibit well-defined firing time distributions. We propose a method for estimating these distributions based on a burst alignment algorithm that minimizes the overlap among the firing time distributions of the different spikes within the burst. This method provides a good approximation to the burst's intrinsic temporal structure as a set of firing time distributions. In addition, the method allows labeling the spikes in any particular burst, establishing a correspondence between each spike and the distribution that best explains it, and identifying missing spikes. Our results on both simulated and experimental data from the lobster stomatogastric ganglion show that the proposed method provides a reliable characterization of the intraburst firing patterns and avoids the errors derived from missing spikes. This method can also be applied to nonbursting neurons as a general tool for the study and the interpretation of firing time distributions as part of a temporal neural code.

Robust microcircuit synchronization by inhibitory connections

Microcircuits in different brain areas share similar architectural and biophysical properties with compact motor networks known as central pattern generators (CPGs). Consequently, CPGs have been suggested as valuable biological models for understanding of microcircuit dynamics and particularly, their synchronization. We use a well known compact motor network, the lobster pyloric CPG to study principles of intercircuit synchronization. We couple separate pyloric circuits obtained from two animals via artificial synapses and observe how their synchronization depends on the topology and kinetic parameters of the computer-generated synapses. Stable in-phase synchronization appears when electrically coupling the pacemaker groups of the two networks, but reciprocal inhibitory connections produce more robust and regular cooperative activity. Contralateral inhibitory connections offer effective synchronization and flexible setting of the burst phases of the interacting networks. We also show that a conductance-based mathematical model of the coupled circuits correctly reproduces the observed dynamics illustrating the generality of the phenomena.

Messages of Oscillatory Correlograms: A Spike Train Model

Oscillatory correlograms are widely used to study neuronal activity that shows a joint periodic rhythm. In most cases, the statistical analysis of cross-correlation histograms (CCH) features is based on the null model of independent processes, and the resulting conclusions about the underlying processes remain qualitative. Therefore, we propose a spike train model for synchronous oscillatory firing activity that directly links characteristics of the CCH to parameters of the underlying processes. The model focuses particularly on asymmetric central peaks, which differ in slope and width on the two sides. Asymmetric peaks can be associated with phase offsets in the (sub-) millisecond range. These spatiotemporal firing patterns can be highly consistent across units yet invisible in the underlying processes. The proposed model includes a single temporal parameter that accounts for this peak asymmetry.

The model provides approaches for the analysis of oscillatory correlograms, taking into account dependencies and nonstationarities in the underlying processes. In particular, the auto- and the cross-correlogram can be investigated in a joint analysis because they depend on the same spike train parameters. Particular temporal interactions such as the degree to which different units synchronize in a common oscillatory rhythm can also be investigated. The analysis is demonstrated by application to a simulated data set.

Versatile biologically inspired electronic neuron

We present a biologically inspired electronic neuron based on a conductance model. The channels are constructed using linearly voltage controlled field effect transistors. A two channel and a three channel circuit is developed. The dynamical behavior of this system is studied, showing for the two channel circuit either class-I or class-II excitability and for the three channel circuit bursting and spike frequency adaptation. Voltage-clamp-type measurements, similar to the ones frequently used in neuroscience, are employed in order to determine the conductance characteristics of the electronic channels. We develop an empirical model based on these measurements that reproduces the different dynamical behaviors of the electronic neuron. We found that post-inhibitory rebound is present in the two channel circuit. Reliability and precision of spike timing is induced in the three channel circuit by injecting noise in the control variable of the slow channel that provides a negative feedback. The circuit is appropriate for the design of large scale electronic neural devices that can be used in mixed electronic-biological systems.

Models wagging the dog: are circuits constructed with disparate parameters?

In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken.

FG 7142 specifically reduces meal size and the rate and regularity of sustained feeding in female rats: evidence that benzodiazepine inverse agonists reduce food palatability

Benzodiazepine receptor inverse agonists reduce food intake in males, but their actions in females, in whom stress-related eating disorders are more common, as well as their behavioral mode of action remain unclear. The consummatory effects of benzodiazepine receptor ligands have alternately been hypothesized to reflect changes in the hedonic evaluation of food or secondary effects of anxiety-related or cognitive properties. To test the anorectic mode of action of benzodiazepine inverse agonists, the effects of FG 7142 on feeding microstructure were studied in nondeprived female Wistar rats (n=32). Microstructure analysis used a novel meal definition that recognizes prandial drinking. On pharmacologically synchronized diestrus I, rats were pretreated (-30 min dark onset) with the benzodiazepine partial inverse agonist FG 7142 (i.p. 0, 3.75, 7.5, 15 mg/kg) in a between-subjects design. FG 7142 delayed the onset of (16-541%), decreased the amount eaten (36-52%) and drunk (63-87%), and reduced the time spent drinking (59-87%) within the first nocturnal meal. Dose-dependent incremental anorexia continued 6 h into the dark cycle, whereas FG 7142 did not suppress the quantity, duration or rate of drinking past the first meal. Treated rats ate smaller meals (17-42%) of normal duration. This reflected that FG 7142 slowed feeding within meals (9-38%) by decreasing the regularity and maintenance of feeding from pellet-to-pellet. FG 7142 did not influence postprandial satiety; meal frequency and inter-meal intervals were unaffected. FG 7142 anorexia was blocked by the benzodiazepine receptor antagonist flumazenil in a 2:1 molar ratio (n=17 rats). The very early, nonspecific (+10 min), but not subsequent (2.5, 4.5 h) feeding-specific phase, of FG 7142 anorexia was mirrored by anxiogenic-like behavior in FG 7142-treated (7.5 mg/kg) female rats (n=48) in the elevated plus-maze. Thus, benzodiazepine receptor inverse agonists preferentially lessen the maintenance of feeding in female rats, effects opposite to those of palatable food.

Delayed satiety-like actions and altered feeding microstructure by a selective type 2 corticotropin-releasing factor agonist in rats: intra-hypothalamic urocortin 3 administration reduces food intake by prolonging the post-meal interval

Brain corticotropin-releasing factor/urocortin (CRF/Ucn) systems are hypothesized to control feeding, with central administration of 'type 2' urocortins producing delayed anorexia. The present study sought to identify the receptor subtype, brain site, and behavioral mode of action through which Ucn 3 reduces nocturnal food intake in rats. Non-food-deprived male Wistar rats (n=176) were administered Ucn 3 into the lateral (LV) or fourth ventricle, or into the ventromedial or paraventricular nuclei of the hypothalamus (VMN, PVN) or the medial amygdala (MeA), regions in which Ucn 3 is expressed in proximity to CRF(2) receptors. LV Ucn 3 suppressed ingestion during the third-fourth post-injection hours. LV Ucn 3 anorexia was reversed by cotreatment with astressin(2)-B, a selective CRF(2) antagonist and not observed following equimole subcutaneous or fourth ventricle administration. Bilateral intra-VMN and intra-PVN infusion, more potently than LV infusion, reduced the quantity (57-73%) and duration of ingestion (32-68%) during the third-fourth post-infusion hours. LV, intra-PVN and intra-VMN infusion of Ucn 3 slowed the eating rate and reduced intake by prolonging the post-meal interval. Intra-VMN Ucn 3 reduced feeding bout size, and intra-PVN Ucn 3 reduced the regularity of eating from pellet to pellet. Ucn 3 effects were behaviorally specific, because minimal effective anorectic Ucn 3 doses did not alter drinking rate or promote a conditioned taste aversion, and site-specific, because intra-MeA Ucn 3 produced a nibbling pattern of more, but smaller meals without altering total intake. The results implicate the VMN and PVN of the hypothalamus as sites for Ucn 3-CRF(2) control of food intake.

Understanding Circuit Dynamics Using the Stomatogastric Nervous System of Lobsters and Crabs

Studies of the stomatogastric nervous systems of lobsters and crabs have led to numerous insights into the cellular and circuit mechanisms that generate rhythmic motor patterns. The small number of easily identifiable neurons allowed the establishment of connectivity diagrams among the neurons of the stomatogastric ganglion. We now know that (a) neuromodulatory substances reconfigure circuit dynamics by altering synaptic strength and voltage-dependent conductances and (b) individual neurons can switch among different functional circuits. Computational and experimental studies of single-neuron and network homeostatic regulation have provided insight into compensatory mechanisms that can underlie stable network performance. Many of the observations first made using the stomatogastric nervous system can be generalized to other invertebrate and vertebrate circuits.

Consistent dynamics suggests tight regulation of biophysical parameters in a small network of bursting neurons

The neuronal firing patterns in the pyloric network of crustaceans are remarkably consistent among animals. Although this characteristic of the pyloric network is well-known, the biophysical mechanisms underlying the regulation of the systems output are receiving renewed attention. Computer simulations of the pyloric network recently demonstrated that consistent motor output can be achieved from neurons with disparate biophysical parameters among animals. Here we address this hypothesis by pharmacologically manipulating the pyloric network and analyzing the emerging voltage oscillations and firing patterns. Our results show that the pyloric network of the lobster stomatogastric ganglion maintains consistent and regular firing patterns even when entire populations of specific voltage-gated channels and synaptic receptors are blocked. The variations of temporal parameters used to characterize the burst patterns of the neurons as well as their intraburst spike dynamics do not display statistically significant increase after blocking the transient K-currents (with 4-aminopyridine), the glutamatergic inhibitory synapses (with picrotoxin), or the cholinergic synapses (with atropine) in pyloric networks from different animals. These data suggest that in this very compact circuit, the biophysical parameters are cell-specific and tightly regulated.

Different motor neuron spike patterns produce contractions with very similar rises in graded slow muscles

Graded muscles produce small twitches in response to individual motor neuron spikes. During the early part of their contractions, contraction amplitude in many such muscles depends primarily on the number of spikes the muscle has received, not the frequency or pattern with which they were delivered. Stick insect (Carausius morosus) extensor muscles are graded and thus would likely show spike-number dependency early in their contractions. Tonic stimulations of the extensor motor nerve showed that the response of the muscles differed from the simplest form of spike-number dependency. However, these differences actually increased the spike-number range over which spike-number dependency was present. When the motor nerve was stimulated with patterns mimicking the motor neuron activity present during walking, amplitude during contraction rises also depended much more on spike number than on spike frequency. A consequence of spike-number dependency is that brief changes in spike frequency do not alter contraction slope and we show here that extensor motor neuron bursts with different spike patterns give rise to contractions with very similar contraction rises. We also examined in detail the early portions of a large number of extensor motor neuron bursts recorded during single-leg walking and show that these portions of the bursts do not appear to have any common spike pattern. Although alternative explanations are possible, the simplest interpretation of these data is that extensor motor neuron firing during leg swing is not tightly controlled.

Neural signatures: multiple coding in spiking-bursting cells

Recent experiments have revealed the existence of neural signatures in the activity of individual cells of the pyloric central pattern generator (CPG) of crustacean. The neural signatures consist of cell-specific spike timings in the bursting activity of the neurons. The role of these intraburst neural fingerprints is still unclear. It has been reported previously that some muscles can reflect small changes in the spike timings of the neurons that innervate them. However, it is unclear to what extent neural signatures contribute to the command message that the muscles receive from the motoneurons. It is also unknown whether the signatures have any functional meaning for the neurons that belong to the same CPG or to other interconnected CPGs. In this paper, we use realistic neural models to study the ability of single cells and small circuits to recognize individual neural signatures. We show that model cells and circuits can respond distinctly to the incoming neural fingerprints in addition to the properties of the slow depolarizing waves. Our results suggest that neural signatures can be a general mechanism of spiking-bursting cells to implement multicoding.

StdpC: a modern dynamic clamp

With the advancement of computer technology many novel uses of dynamic clamp have become possible. We have added new features to our dynamic clamp software StdpC ("Spike timing-dependent plasticity Clamp") allowing such new applications while conserving the ease of use and installation of the popular earlier Dynclamp 2/4 package. Here, we introduce the new features of a waveform generator, freely programmable Hodgkin-Huxley conductances, learning synapses, graphic data displays, and a powerful scripting mechanism and discuss examples of experiments using these features. In the first example we built and 'voltage clamped' a conductance based model cell from a passive resistor-capacitor (RC) circuit using the dynamic clamp software to generate the voltage-dependent currents. In the second example we coupled our new spike generator through a burst detection/burst generation mechanism in a phase-dependent way to a neuron in a central pattern generator and dissected the subtle interaction between neurons, which seems to implement an information transfer through intraburst spike patterns. In the third example, making use of the new plasticity mechanism for simulated synapses, we analyzed the effect of spike timing-dependent plasticity (STDP) on synchronization revealing considerable enhancement of the entrainment of a post-synaptic neuron by a periodic spike train. These examples illustrate that with modern dynamic clamp software like StdpC, the dynamic clamp has developed beyond the mere introduction of artificial synapses or ionic conductances into neurons to a universal research tool, which might well become a standard instrument of modern electrophysiology.

Synchronization and propagation of bursts in networks of coupled map neurons

The present paper studies regular and complex spatiotemporal behaviors in networks of coupled map-based bursting oscillators. In-phase and antiphase synchronization of bursts are studied, explaining their underlying mechanisms in order to determine how network parameters separate them. Conditions for emergent bursting in the coupled system are derived from our analysis. In the region of emergence, patterns of chaotic transitions between synchronization and propagation of bursts are found. We show that they consist of transient standing and rotating waves induced by symmetry-breaking bifurcations, and can be viewed as a manifestation of the phenomenon of chaotic itinerancy.

Determinants of inspiratory activity

In vitro and in vivo studies have identified the pre-Bötzinger complex as an important kernel for the generation of inspiratory activity. The mechanisms underlying inspiratory rhythm generation involve pacemaker as well as synaptic mechanisms. In slice preparations, blockade of pacemaker properties with blockers for the persistent Na+ current, and the Ca2+-activated inward cationic current, abolishes respiratory activity. Here we show that blockade of the persistent Na+ current alone is sufficient to abolish respiratory activity in the in situ preparation. Although pacemaker neurons may be critical for establishing the basic respiratory rhythm, their rhythmic output is modulated by many elements of the respiratory network. For example, levels of synaptic inhibition control whether they burst or not, and endogenously released neuromodulators, such as serotonin and substance P modulate their intrinsic membrane currents. We hypothesize that the balance between synaptic and intrinsic pacemaker properties in the respiratory network is plastic, and that alterations of this balance may lead to dynamic reconfigurations of the respiratory network, which ultimately give rise to different activity patterns.

Single neurons in the dentate gyrus and CA1 of the hippocampus exhibit inverse patterns of encoding during trace fear conditioning

Trace fear conditioning is a hippocampus-dependent learning task that requires the association of an auditory conditioned stimulus (CS) and a shock unconditioned stimulus (US) that are separated by a 20-s trace interval. Single-neuron activity was recorded simultaneously from the dentate gyrus (DG) and CA1 of rats during unpaired pseudoconditioning and subsequent trace fear conditioning. Single neurons in DG showed a progressive increase in learning-related activity to the CS and US across trace fear conditioning. Single neurons in CA1 showed an early increase in responding to the CS, which developed into a decrease in firing later in trace conditioning. Correlation analyses showed that DG and CA1 units exhibit inverse patterns of responding to the CS during trace fear conditioning.

Mechanisms of fast ripples in the hippocampus

Hippocampal fast ripples (FRs) have been associated with seizure onset in both human and experimental epilepsy. To characterize the mechanisms underlying FR oscillations (200-600 Hz), we studied activity of single neurons and neuronal networks in rat hippocampal slices in vitro. The correlation between the action potentials of bursting pyramidal cells and local field potential oscillations suggests that synchronous onset of action potential bursts and similar intrinsic firing patterns among local neurons are both necessary conditions for FR oscillations. Increasing the fidelity of individual pyramidal cell spike train timing by blocking accommodation dramatically increased FR amplitude, whereas blockade of potassium conductances decreased the fidelity of action potential timing in individual pyramidal cell action potential bursts and decreased FR amplitude. Blockade of ionotropic glutamate receptors desynchronized onset of action potential bursts in individual pyramidal cells and abolished fast ripples. Thus, synchronous burst onset mediated by recurrent excitatory synaptic transmission and similar intrinsic spike timing mechanisms in neighboring pyramidal cells are necessary conditions for FR oscillations within the hippocampal network.

Whole cell stochastic model reproduces the irregularities found in the membrane potential of bursting neurons

Irregular intrinsic behavior of neurons seems ubiquitous in the nervous system. Even in circuits specialized to provide periodic and reliable patterns to control the repetitive activity of muscles, such as the pyloric central pattern generator (CPG) of the crustacean stomatogastric ganglion (STG), many bursting motor neurons present irregular activity when deprived from synaptic inputs. Moreover, many authors attribute to these irregularities the role of providing flexibility and adaptation capabilities to oscillatory neural networks such as CPGs. These irregular behaviors, related to nonlinear and chaotic properties of the cells, pose serious challenges to developing deterministic Hodgkin-Huxley-type (HH-type) conductance models. Only a few deterministic HH-type models based on experimental conductance values were able to show such nonlinear properties, but most of these models are based on slow oscillatory dynamics of the cytosolic calcium concentration that were never found experimentally in STG neurons. Based on an up-to-date single-compartment deterministic HH-type model of a STG neuron, we developed a stochastic HH-type model based on the microscopic Markovian states that an ion channel can achieve. We used tools from nonlinear analysis to show that the stochastic model is able to express the same kind of irregularities, sensitivity to initial conditions, and low dimensional dynamics found in the neurons isolated from the STG. Without including any nonrealistic dynamics in our whole cell stochastic model, we show that the nontrivial dynamics of the membrane potential naturally emerge from the interplay between the microscopic probabilistic character of the ion channels and the nonlinear interactions among these elements. Moreover, the experimental irregular behavior is reproduced by the stochastic model for the same parameters for which the membrane potential of the original deterministic model exhibits periodic oscillations.

Dopamine modulation of spike dynamics in bursting neurons

The pyloric network of the lobster stomatogastric ganglion is a prime example of an oscillatory neural circuit. In our previous study on the firing patterns of pyloric neurons we observed characteristic temporal structures termed 'interspike interval (ISI) signatures' which were found to depend on the synaptic connectivity of the network. Dopamine, a well-known modulator of the pyloric network, is known to affect inhibitory synapses so it might also tune the fine temporal structure of intraburst spikes, a phenomenon not previously investigated. In the recent work we study the DA modulation of ISI patterns of two identified pyloric neurons in normal conditions and after blocking their glutamatergic synaptic connections. Dopamine (10-50 microM) strongly regularizes the firing of the lateral pyloric (LP) and pyloric dilator (PD) neurons by increasing the reliability of recurrent spike patterns. The most dramatic effect is observed in the LP, where precisely replicated spike multiplets appear in a normally 'noisy' neuron. The DA-induced regularization of intraburst spike patterns requires functional glutamatergic inputs to the LP neuron and this effect cannot be mimicked by simple intracellular depolarization. Inhibitory synaptic inputs arriving before the bursts are important factors in shaping the intraburst spike dynamics of both the PD and the LP neurons. Our data reveal a novel aspect of chemical neuromodulation in oscillatory neural networks. This effect sets in at concentrations lower than those affecting the overall burst pattern of the network. The sensitivity of intraburst spike dynamics to preceding synaptic inputs also suggests a novel method of temporal coding in neural bursters.

The dynamic clamp comes of age

The dynamic clamp uses computer simulation to introduce artificial membrane or synaptic conductances into biological neurons and to create hybrid circuits of real and model neurons. In the ten years since it was first developed, the dynamic clamp has become a widely used tool for the study of neural systems at the cellular and circuit levels. This review describes recent state-of-the-art implementations of the dynamic clamp and summarizes insights gained through its use, ranging from the role of voltage-dependent conductances in shaping neuronal activity to the effects of synaptic dynamics on network behavior and the impact of in vivo-like input on neuronal information processing.