Activity-dependent modification of inhibitory synapses in models of rhythmic neural networks

From Biological Synapses to “Intelligent” Robots

This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems.

Intrinsic plasticity and birdsong learning

Although information processing and storage in the brain is thought to be primarily orchestrated by synaptic plasticity, other neural mechanisms such as intrinsic plasticity are available. While a number of recent studies have described the plasticity of intrinsic excitability in several types of neurons, the significance of non-synaptic mechanisms in memory and learning remains elusive. After reviewing plasticity of intrinsic excitation in relation to learning and homeostatic mechanisms, we focus on the intrinsic properties of a class of basal-ganglia projecting song system neurons in zebra finch, how these related to each bird's unique learned song, how these properties change over development, and how they are maintained dynamically to rapidly change in response to auditory feedback perturbations. We place these results in the broader theme of learning and changes in intrinsic properties, emphasizing the computational implications of this form of plasticity, which are distinct from synaptic plasticity. The results suggest that exploring reciprocal interactions between intrinsic and network properties will be a fruitful avenue for understanding mechanisms of birdsong learning.

Effect of interpopulation spike-timing-dependent plasticity on synchronized rhythms in neuronal networks with inhibitory and excitatory populations

We consider a two-population network consisting of both inhibitory (I) interneurons and excitatory (E) pyramidal cells. This I-E neuronal network has adaptive dynamic I to E and E to I interpopulation synaptic strengths, governed by interpopulation spike-timing-dependent plasticity (STDP). In previous works without STDPs, fast sparsely synchronized rhythms, related to diverse cognitive functions, were found to appear in a range of noise intensity D for static synaptic strengths. Here, by varying D, we investigate the effect of interpopulation STDPs on fast sparsely synchronized rhythms that emerge in both the I- and the E-populations. Depending on values of D, long-term potentiation (LTP) and long-term depression (LTD) for population-averaged values of saturated interpopulation synaptic strengths are found to occur. Then, the degree of fast sparse synchronization varies due to effects of LTP and LTD. In a broad region of intermediate D, the degree of good synchronization (with higher synchronization degree) becomes decreased, while in a region of large D, the degree of bad synchronization (with lower synchronization degree) gets increased. Consequently, in each I- or E-population, the synchronization degree becomes nearly the same in a wide range of D (including both the intermediate and the large D regions). This kind of "equalization effect" is found to occur via cooperative interplay between the average occupation and pacing degrees of spikes (i.e., the average fraction of firing neurons and the average degree of phase coherence between spikes in each synchronized stripe of spikes in the raster plot of spikes) in fast sparsely synchronized rhythms. Finally, emergences of LTP and LTD of interpopulation synaptic strengths (leading to occurrence of equalization effect) are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic spike times.

Formulation of Pruning Maps with Rhythmic Neural Firing

Rhythmic neural firing is thought to underlie the operation of neural function. This triggers the construction of dynamical network models to investigate how the rhythms interact with each other. Recently, an approach concerning neural path pruning has been proposed in a dynamical network system, in which critical neuronal connections are identified and adjusted according to the pruning maps, enabling neurons to produce rhythmic, oscillatory activity in simulation. Here, we construct a sort of homomorphic functions based on different rhythms of neural firing in network dynamics. Armed with the homomorphic functions, the pruning maps can be simply expressed in terms of interactive rhythms of neural firing and allow a concrete analysis of coupling operators to control network dynamics. Such formulation of pruning maps is applied to probe the consolidation of rhythmic patterns between layers of neurons in feedforward neural networks.

Burst synchronization in a scale-free neuronal network with inhibitory spike-timing-dependent plasticity

We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barabási-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without considering iSTDP, BS was found to appear in a range of noise intensities for fixed synaptic inhibition strengths. In contrast, in our present work, we take into consideration iSTDP and investigate its effect on BS by varying the noise intensity. Our new main result is to find occurrence of a Matthew effect in inhibitory synaptic plasticity: good BS gets better via LTD, while bad BS get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). We note that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic plasticity are reversed in comparison with those in excitatory synaptic plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic burst onset times. Finally, in the presence of iSTDP we investigate the effects of network architecture on BS by varying the symmetric attachment degree l ∗ and the asymmetry parameter Δ l in the SFN.

Effect of inhibitory spike-timing-dependent plasticity on fast sparsely synchronized rhythms in a small-world neuronal network

We consider the Watts-Strogatz small-world network (SWN) consisting of inhibitory fast spiking Izhikevich interneurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without iSTDP, fast sparsely synchronized rhythms, associated with diverse cognitive functions, were found to appear in a range of large noise intensities for fixed strong synaptic inhibition strengths. Here, we investigate the effect of iSTDP on fast sparse synchronization (FSS) by varying the noise intensity D. We employ an asymmetric anti-Hebbian time window for the iSTDP update rule [which is in contrast to the Hebbian time window for the excitatory STDP (eSTDP)]. Depending on values of D, population-averaged values of saturated synaptic inhibition strengths are potentiated [long-term potentiation (LTP)] or depressed [long-term depression (LTD)] in comparison with the initial mean value, and dispersions from the mean values of LTP/LTD are much increased when compared with the initial dispersion, independently of D. In most cases of LTD where the effect of mean LTD is dominant in comparison with the effect of dispersion, good synchronization (with higher spiking measure) is found to get better via LTD, while bad synchronization (with lower spiking measure) is found to get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). Emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic spike times. Furthermore, we also investigate the effects of network architecture on FSS by changing the rewiring probability p of the SWN in the presence of iSTDP.

Pharmacological modulation of Kv3.1 mitigates auditory midbrain temporal processing deficits following auditory nerve damage

Higher stages of central auditory processing compensate for a loss of cochlear nerve synapses by increasing the gain on remaining afferent inputs, thereby restoring firing rate codes for rudimentary sound features. The benefits of this compensatory plasticity are limited, as the recovery of precise temporal coding is comparatively modest. We reasoned that persistent temporal coding deficits could be ameliorated through modulation of voltage-gated potassium (Kv) channels that regulate temporal firing patterns. Here, we characterize AUT00063, a pharmacological compound that modulates Kv3.1, a high-threshold channel expressed in fast-spiking neurons throughout the central auditory pathway. Patch clamp recordings from auditory brainstem neurons and in silico modeling revealed that application of AUT00063 reduced action potential timing variability and improved temporal coding precision. Systemic injections of AUT00063 in vivo improved auditory synchronization and supported more accurate decoding of temporal sound features in the inferior colliculus and auditory cortex in adult mice with a near-complete loss of auditory nerve afferent synapses in the contralateral ear. These findings suggest modulating Kv3.1 in central neurons could be a promising therapeutic approach to mitigate temporal processing deficits that commonly accompany aging, tinnitus, ototoxic drug exposure or noise damage.

Synaptic convergence regulates synchronization-dependent spike transfer in feedforward neural networks

Correlated neural activities such as synchronizations can significantly alter the characteristics of spike transfer between neural layers. However, it is not clear how this synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. To address this question, we implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that, the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.

Chloride Regulation: A Dynamic Equilibrium Crucial for Synaptic Inhibition

Fast synaptic inhibition relies on tight regulation of intracellular Cl(-). Chloride dysregulation is implicated in several neurological and psychiatric disorders. Beyond mere disinhibition, the consequences of Cl(-) dysregulation are multifaceted and best understood in terms of a dynamical system involving complex interactions between multiple processes operating on many spatiotemporal scales. This dynamical perspective helps explain many unintuitive manifestations of Cl(-) dysregulation. Here we discuss how taking into account dynamical regulation of intracellular Cl(-) is important for understanding how synaptic inhibition fails, how to best detect that failure, why Cl(-) regulation is energetically so expensive, and the overall consequences for therapeutics.

Mild KCC2 Hypofunction Causes Inconspicuous Chloride Dysregulation that Degrades Neural Coding

Disinhibition caused by Cl⁻ dysregulation is implicated in several neurological disorders. This form of disinhibition, which stems primarily from impaired Cl⁻ extrusion through the co-transporter KCC2, is typically identified by a depolarizing shift in GABA reversal potential (E_GABA). Here we show, using computer simulations, that intracellular [Cl⁻] exhibits exaggerated fluctuations during transient Cl⁻ loads and recovers more slowly to baseline when KCC2 level is even modestly reduced. Using information theory and signal detection theory, we show that increased Cl⁻ lability and settling time degrade neural coding. Importantly, these deleterious effects manifest after less KCC2 reduction than needed to produce the gross changes in E_GABA required for detection by most experiments, which assess KCC2 function under weak Cl⁻ load conditions. By demonstrating the existence and functional consequences of “occult” Cl⁻ dysregulation, these results suggest that modest KCC2 hypofunction plays a greater role in neurological disorders than previously believed.

Consequences of acute and long-term removal of neuromodulatory input on the episodic gastric rhythm of the crab Cancer borealis

For decades, the episodic gastric rhythm of the crustacean stomatogastric nervous system (STNS) has served as an important model system for understanding the generation of rhythmic motor behaviors. Here we quantitatively describe many features of the gastric rhythm of the crab Cancer borealis under several conditions. First, we analyzed spontaneous gastric rhythms produced by freshly dissected preparations of the STNS, including the cycle frequency and phase relationships among gastric units. We find that phase is relatively conserved across frequency, similar to the pyloric rhythm. We also describe relationships between these two rhythms, including a significant gastric/pyloric frequency correlation. We then performed continuous, days-long extracellular recordings of gastric activity from preparations of the STNS in which neuromodulatory inputs to the stomatogastric ganglion were left intact and also from preparations in which these modulatory inputs were cut (decentralization). This allowed us to provide quantitative descriptions of variability and phase conservation within preparations across time. For intact preparations, gastric activity was more variable than pyloric activity but remained relatively stable across 4-6 days, and many significant correlations were found between phase and frequency within animals. Decentralized preparations displayed fewer episodes of gastric activity, with altered phase relationships, lower frequencies, and reduced coordination both among gastric units and between the gastric and pyloric rhythms. Together, these results provide insight into the role of neuromodulation in episodic pattern generation and the extent of animal-to-animal variability in features of spontaneously occurring gastric rhythms.

Homeostatic plasticity for single node delay-coupled reservoir computing

Supplementing a differential equation with delays results in an infinite-dimensional dynamical system. This property provides the basis for a reservoir computing architecture, where the recurrent neural network is replaced by a single nonlinear node, delay-coupled to itself. Instead of the spatial topology of a network, subunits in the delay-coupled reservoir are multiplexed in time along one delay span of the system. The computational power of the reservoir is contingent on this temporal multiplexing. Here, we learn optimal temporal multiplexing by means of a biologically inspired homeostatic plasticity mechanism. Plasticity acts locally and changes the distances between the subunits along the delay, depending on how responsive these subunits are to the input. After analytically deriving the learning mechanism, we illustrate its role in improving the reservoir's computational power. To this end, we investigate, first, the increase of the reservoir's memory capacity. Second, we predict a NARMA-10 time series, showing that plasticity reduces the normalized root-mean-square error by more than 20%. Third, we discuss plasticity's influence on the reservoir's input-information capacity, the coupling strength between subunits, and the distribution of the readout coefficients.

Pulse-coupled BZ oscillators with unequal coupling strengths

A host of asymptotically stable, temporally periodic patterns are found when chemical oscillators are pulse coupled,e.g., the 1 : 2 and 1 : D–N type patterns shown here.

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.

A simple rule for dendritic spine and axonal bouton formation can account for cortical reorganization after focal retinal lesions

Lasting alterations in sensory input trigger massive structural and functional adaptations in cortical networks. The principles governing these experience-dependent changes are, however, poorly understood. Here, we examine whether a simple rule based on the neurons' need for homeostasis in electrical activity may serve as driving force for cortical reorganization. According to this rule, a neuron creates new spines and boutons when its level of electrical activity is below a homeostatic set-point and decreases the number of spines and boutons when its activity exceeds this set-point. In addition, neurons need a minimum level of activity to form spines and boutons. Spine and bouton formation depends solely on the neuron's own activity level, and synapses are formed by merging spines and boutons independently of activity. Using a novel computational model, we show that this simple growth rule produces neuron and network changes as observed in the visual cortex after focal retinal lesions. In the model, as in the cortex, the turnover of dendritic spines was increased strongest in the center of the lesion projection zone, while axonal boutons displayed a marked overshoot followed by pruning. Moreover, the decrease in external input was compensated for by the formation of new horizontal connections, which caused a retinotopic remapping. Homeostatic regulation may provide a unifying framework for understanding cortical reorganization, including network repair in degenerative diseases or following focal stroke.

The adult brain is less hard-wired than traditionally thought. About ten percent of synapses in the mature visual cortex is continually replaced by new ones (structural plasticity). This percentage greatly increases after lasting changes in visual input. Due to the topographically organized nerve connections from the retina in the eye to the primary visual cortex in the brain, a small circumscribed lesion in the retina leads to a defined area in the cortex that is deprived of input. Recent experimental studies have revealed that axonal sprouting and dendritic spine turnover are massively increased in and around the cortical area that is deprived of input. However, the driving forces for this structural plasticity remain unclear. Using a novel computational model, we examine whether the need for activity homeostasis of individual neurons may drive cortical reorganization after lasting changes in input activity. We show that homeostatic growth rules indeed give rise to structural and functional reorganization of neuronal networks similar to the cortical reorganization observed experimentally. Understanding the principles of structural plasticity may eventually lead to novel treatment strategies for stimulating functional reorganization after brain damage and neurodegeneration.

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.

Short-term synaptic plasticity compensates for variability in number of motor neurons at a neuromuscular junction

We studied how similar postsynaptic responses are maintained in the face of interindividual variability in the number of presynaptic neurons. In the stomatogastric ganglion of the lobster, Homarus americanus, the pyloric (PY) neurons exist in variable numbers across animals. We show that each individual fiber of the stomach muscles innervated by PY neurons received synaptic input from all neurons present. We performed intracellular recordings of excitatory junction potentials (EJPs) in the muscle fibers to determine the consequences of differences in the number of motor neurons. Despite the variability in neuron number, the compound electrical response of muscle fibers to natural bursting input was similar across individuals. The similarity of total synaptic activation was not due to differences in the spiking activity of individual motor neurons across animals with different numbers of PY neurons. The amplitude of a unitary EJP in response to a single spike in a single motor neuron also did not depend on the number of PY neurons present. Consequently, the compound EJP in response to a single stimulus that activated all motor axons present was larger in individuals with more PY neurons. However, when axons were stimulated with trains of pulses mimicking bursting activity, EJPs facilitated more in individuals with fewer PY neurons. After a few stimuli, this resulted in depolarizations similar to the ones in individuals with more PY neurons. We interpret our findings as evidence that compensatory or homeostatic regulatory mechanisms can act on short-term synaptic dynamics instead of absolute synaptic strength.

Effect of synaptic plasticity on the structure and dynamics of disordered networks of coupled neurons

In an all-to-all network of integrate-and-fire neurons in which there is a disorder in the intrinsic oscillatory frequencies of the neurons, we show that through spike-timing-dependent plasticity the synapses which have the high-frequency neurons as presynaptic tend to be potentiated while the links originated from the low-frequency neurons are weakened. The emergent effective flow of directed connections introduces the high-frequency neurons as the more influential elements in the network and facilitates synchronization by decreasing the synaptic cost for onset of synchronization.

Endogenous patterns of activity are required for the maturation of a motor network

Many parts of the nervous system become active before development is complete, including the embryonic spinal cord. Remarkably, although the subject has been debated for over a century (Harrison, 1904), it is still unclear whether such activity is required for normal development of motor circuitry. In Drosophila, embryonic motor output is initially poorly organized, and coordinated crawling-like behavior gradually emerges over the subsequent phase of development. We show that reversibly blocking synaptic transmission during this phase severely delays the first appearance of coordinated movements. When we interfere with the pattern of neuronal firing during this period, coordination is also delayed or blocked. We conclude that there is a period during which endogenous patterns of neuronal activity are required for the normal development of motor circuits in Drosophila.

Compensation for variable intrinsic neuronal excitability by circuit-synaptic interactions

Recent theoretical and experimental work indicates that neurons tune themselves to maintain target levels of excitation by modulating ion channel expression and synaptic strengths. As a result, functionally equivalent circuits can produce similar activity despite disparate underlying network and cellular properties. To experimentally test the extent to which synaptic and intrinsic conductances can produce target activity in the presence of variability in neuronal intrinsic properties, we used the dynamic clamp to create hybrid two-cell circuits built from four types of stomatogastric neurons coupled to the same model Morris-Lecar neuron by reciprocal inhibition. We measured six intrinsic properties (input resistance, minimum membrane potential, firing rate in response to +1 nA of injected current, slope of the frequency-current curve, spike height, and spike voltage threshold) of dorsal gastric, gastric mill, lateral pyloric, and pyloric dilator neurons from male crabs of the species Cancer borealis. The intrinsic properties varied twofold to sevenfold in each cell type. We coupled each biological neuron to the Morris-Lecar model with seven different values of inhibitory synaptic conductance and also used the dynamic clamp to add seven different values of an artificial h-conductance, thus creating 49 different circuits for each biological neuron. Despite the variability in intrinsic excitability, networks formed from each neuron produced similar circuit performance at some values of synaptic and h-conductances. This work experimentally confirms results from previous modeling studies; tuning synaptic and intrinsic conductances can yield similar circuit outputs from neurons with variable intrinsic excitability.

Model calcium sensors for network homeostasis: sensor and readout parameter analysis from a database of model neuronal networks

In activity-dependent homeostatic regulation (ADHR) of neuronal and network properties, the intracellular Ca(2+) concentration is a good candidate for sensing activity levels because it is correlated with the electrical activity of the cell. Previous ADHR models, developed with abstract activity sensors for model pyloric neurons and networks of the crustacean stomatogastric ganglion, showed that functional activity can be maintained by a regulation mechanism that senses activity levels solely from Ca(2+). At the same time, several intracellular pathways have been discovered for Ca(2+)-dependent regulation of ion channels. To generate testable predictions for dynamics of these signaling pathways, we undertook a parameter study of model Ca(2+) sensors across thousands of model pyloric networks. We found that an optimal regulation signal can be generated for 86% of model networks with a sensing mechanism that activates with a time constant of 1 ms and that inactivates within 1 s. The sensor performed robustly around this optimal point and did not need to be specific to the role of the cell. When multiple sensors with different time constants were used, coverage extended to 88% of the networks. Without changing the sensors, it extended to 95% of the networks by letting the sensors affect the readout nonlinearly. Specific to this pyloric network model, the sensor of the follower pyloric constrictor cell was more informative than the pacemaker anterior burster cell for producing a regulatory signal. Conversely, a global signal indicating network activity that was generated by summing the sensors in individual cells was less informative for regulation.

Persistent Ca2+ Current Contributes to a Prolonged Depolarization in Aplysia Bag Cell Neurons

Neurons may initiate behavior or store information by translating prior activity into a lengthy change in excitability. For example, brief input to the bag cell neurons of Aplysia results in an approximate 30-min afterdischarge that induces reproduction. Similarly, momentary stimulation of cultured bag cells neurons evokes a prolonged depolarization lasting many minutes. Contributing to this is a voltage-independent cation current activated by Ca2+ entering during the stimulus. However, the cation current is relatively short-lived, and we hypothesized that a second, voltage-dependent persistent current sustains the prolonged depolarization. In bag cell neurons, the inward voltage-dependent current is carried by Ca2+; thus we tested for persistent Ca2+ current in primary culture under voltage clamp. The observed current activated between −40 and −50 mV exhibited a very slow decay, presented a similar magnitude regardless of stimulus duration (10–60 s), and, like the rapid Ca2+ current, was enhanced when Ba2+ was the permeant ion. The rapid and persistent Ca2+ current, but not the cation current, were Ni2+ sensitive. Consistent with the persistent current contributing to the response, Ni2+ reduced the amplitude of a prolonged depolarization evoked under current clamp. Finally, protein kinase C activation enhanced the rapid and persistent Ca2+ current as well as increased the prolonged depolarization when elicited by an action potential-independent stimulus. Thus the prolonged depolarization arises from Ca2+ influx triggering a cation current, followed by voltage-dependent activation of a persistent Ca2+ current and is subject to modulation. Such synergy between currents may represent a common means of achieving activity-dependent changes to excitability.

How does maintenance of network activity depend on endogenous dynamics of isolated neurons?

Robust activity of some networks, such as central pattern generators, suggests the existence of physiological mechanisms that maintain the most important characteristics, for example, the period and spike frequency of the pattern. Whatever these mechanisms are, they change the appropriate model parameters to or along the isomanifolds on which the characteristics of the pattern are constant, while their sensitivities to parameters may be different. Setting synaptic connections to zero at the points of isomanifolds allows for dissecting the maintenance mechanisms into components involving synaptic transmission and components involving intrinsic currents. The physiological meaning of the intrinsic current changes might be revealed by analysis of their impact on endogenous neuronal dynamics. Here, we sought answers to two questions: (1) Do parameter variations in insensitive directions (along isomanifolds) change endogenous dynamics of the network neurons? (2) Do sensitive and insensitive directions for network pattern characteristics depend on endogenous dynamics of the network neurons? We considered a leech heartbeat half-center oscillator model network and analyzed isomanifolds on which the burst period or spike frequency of the model, or both, are constant. Based on our analysis, we hypothesize that the dependence on endogenous dynamics of the isolated neurons is the stronger the more characteristics of the pattern have to be maintained. We also found that in general, the network was more flexible when it consisted of endogenously tonically spiking rather than bursting or silent neurons. Finally, we discuss the physiological implications of our findings.

Homeostasis of brain dynamics in epilepsy: a feedback control systems perspective of seizures

In an effort to understand basic functional mechanisms that can produce epileptic seizures, some key features are introduced in coupled lumped-parameter neural population models that produce "seizure"-like events and dynamics similar to the ones during the route of the epileptic brain towards seizures. In these models, modified from existing ones in the literature, internal feedback mechanisms are incorporated to maintain the normal low level of synchronous behavior in the presence of coupling variations. While the internal feedback is developed using basic feedback systems principles, it is also functionally equivalent to actual neurophysiological mechanisms such as homeostasis that act to maintain normal activity in neural systems that are subject to extrinsic and intrinsic perturbations. Here it is hypothesized that a plausible cause of seizures is a pathology in the internal feedback action; normal internal feedback quickly regulates an abnormally high coupling between the neural populations, whereas pathological internal feedback can lead to "seizure"-like high amplitude oscillations. Several external seizure-control paradigms, that act to achieve the operational objective of maintaining normal levels of synchronous behavior, are also developed and tested in this paper. In particular, closed-loop "modulating" control with predefined stimuli, and closed-loop feedback decoupling control are considered. Among these, feedback decoupling control is the consistently successful and robust seizure-control strategy. The proposed model and remedies are consistent with a variety of recent observations in the human and animal epileptic brain, and with theories from nonlinear systems, adaptive systems, optimization, and neurophysiology. The results from the analysis of these models have two key implications, namely, developing a basic theory for epilepsy and other brain disorders, and the development of a robust seizure-control device through electrical stimulation and/or drug intervention modalities.

Cracking neural circuits in a tiny brain: new approaches for understanding the neural circuitry of Drosophila

Genetic screens in Drosophila have identified many genes involved in neural development and function. However, until recently, it has been impossible to monitor neural signals in Drosophila central neurons, and it has been difficult to make specific perturbations to central neural circuits. This has changed in the past few years with the development of new tools for measuring and manipulating neural activity in the fly. Here we review how these new tools enable novel conceptual approaches to 'cracking circuits' in this important model organism. We discuss recent studies aimed at defining the cognitive demands on the fly brain, identifying the cellular components of specific neural circuits, mapping functional connectivity in those circuits and defining causal relationships between neural activity and behavior.

A model of stimulus-specific neural assemblies in the insect antennal lobe

It has been proposed that synchronized neural assemblies in the antennal lobe of insects encode the identity of olfactory stimuli. In response to an odor, some projection neurons exhibit synchronous firing, phase-locked to the oscillations of the field potential, whereas others do not. Experimental data indicate that neural synchronization and field oscillations are induced by fast GABA(A)-type inhibition, but it remains unclear how desynchronization occurs. We hypothesize that slow inhibition plays a key role in desynchronizing projection neurons. Because synaptic noise is believed to be the dominant factor that limits neuronal reliability, we consider a computational model of the antennal lobe in which a population of oscillatory neurons interact through unreliable GABA(A) and GABA(B) inhibitory synapses. From theoretical analysis and extensive computer simulations, we show that transmission failures at slow GABA(B) synapses make the neural response unpredictable. Depending on the balance between GABA(A) and GABA(B) inputs, particular neurons may either synchronize or desynchronize. These findings suggest a wiring scheme that triggers stimulus-specific synchronized assemblies. Inhibitory connections are set by Hebbian learning and selectively activated by stimulus patterns to form a spiking associative memory whose storage capacity is comparable to that of classical binary-coded models. We conclude that fast inhibition acts in concert with slow inhibition to reformat the glomerular input into odor-specific synchronized neural assemblies.

Rapid homeostatic plasticity in the intact adult visual system

Neurons may possess activity-dependent homeostatic mechanisms that permit them to globally alter synaptic strength as activity varies. We used the retinotectal projection of goldfish to test this idea in the intact adult CNS. We first altered tectal neuron activity by selectively manipulating excitatory input. When excitatory synaptic drive to tectal neurons was eliminated by blocking optic fibers, current evoked at optic synapses increased by 183% within 90 min. With partial activity blockade, the increase in synaptic strength scaled with the magnitude of activity depression. This silence-induced potentiation was also rapidly reversible. Conversely, an increase in optic input was followed by a decrease in evoked synaptic current. When optic drive was not altered and tectal neuronal activity was instead increased or decreased pharmacologically via GABA(A) receptors, synaptic strength again changed inversely with activity, indicating that synaptic strength changed in response to neuronal activity and not excitatory drive. Furthermore, altered synaptic strength tended to return ongoing activity to baseline. Changes in synaptic strength could also be detected in heterosynaptic pathways, indicating a global response. Finally, changes in synaptic strength were associated with corresponding changes in ongoing and evoked firing rates, indicating that the responsivity of tectal neurons was altered. Thus, tectal neurons exhibit archetypical homeostasis, one of the first robust examples in the intact adult CNS.

Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons

The aim of the present paper is to study the effects of Hebbian learning in random recurrent neural networks with biological connectivity, i.e. sparse connections and separate populations of excitatory and inhibitory neurons. We furthermore consider that the neuron dynamics may occur at a (shorter) time scale than synaptic plasticity and consider the possibility of learning rules with passive forgetting. We show that the application of such Hebbian learning leads to drastic changes in the network dynamics and structure. In particular, the learning rule contracts the norm of the weight matrix and yields a rapid decay of the dynamics complexity and entropy. In other words, the network is rewired by Hebbian learning into a new synaptic structure that emerges with learning on the basis of the correlations that progressively build up between neurons. We also observe that, within this emerging structure, the strongest synapses organize as a small-world network. The second effect of the decay of the weight matrix spectral radius consists in a rapid contraction of the spectral radius of the Jacobian matrix. This drives the system through the "edge of chaos" where sensitivity to the input pattern is maximal. Taken together, this scenario is remarkably predicted by theoretical arguments derived from dynamical systems and graph theory.

Synaptic heterogeneity between mouse paracapsular intercalated neurons of the amygdala

GABAergic medial paracapsular intercalated (Imp) neurons of amygdala are thought of as playing a central role in fear learning and extinction. We report here that the synaptic network formed by these neurons exhibits distinct short-term plastic synaptic responses. The success rate of synaptic events evoked at a frequency range of 0.1-10 Hz varied dramatically between different connected cell pairs. Upon enhancing the frequency of stimulation, the success rate increased, decreased or remained constant, in a similar number of cell pairs. Such synaptic heterogeneity resulted in inhibition of the firing of the postsynaptic neurons with different efficacies. Moreover, we found that the different synaptic weights were mainly determined by diversity in presynaptic release probabilities rather than postsynaptic changes. Sequential paired recording experiments demonstrated that the same presynaptic neuron established the same type of synaptic connections with different postsynaptic neurons, suggesting the absence of target-cell specificity. Conversely, the same postsynaptic neuron was contacted by different types of synaptic connections formed by different presynaptic neurons. A detailed anatomical analysis of the recorded neurons revealed discrete and unexpected peculiarities in the dendritic and axonal patterns of different cell pairs. In contrast, several intrinsic electrophysiological responses were homogeneous among neurons, and synaptic failure counts were not affected by presynaptic cannabinoid 1 or GABA B receptors. We propose that the heterogeneous functional connectivity of Imp neurons, demonstrated by this study, is required to maintain the stability of firing patterns which is critical for the computational role of the amygdala in fear learning and extinction.

Intersensory facilitation in rapid single-joint voluntary activation and cancellation of arm movements

The ability to initiate and cancel actions is a basic requirement for motor control in humans. Rapid movements to stationary targets over single joints are characterized by triphasic bursts of electromyographic (EMG) activity. While analysis of reaction time in motor activation tasks, in relation to different modalities of sensory inputs, has studied, its diametrically opposite task of motor cancellation has not been adequately addressed. We studied 9 normal right-handed subjects using biceps (agonist) and triceps (antagonist) EMG recordings. Each underwent 3 motor activation and 3 motor cancellation tasks to light, sound and dual stimuli (6 blocks). The former consisted of ballistic elbow flexion over 45 degrees, while the latter involved dropping of the forearm from a 45-degree elbow flexion angle. For motor activation, onset latencies and duration of agonist (Lat1, Dur1) and antagonist (Lat2, Dur2) muscles were recorded. For motor cancellation, onset latencies and duration of agonist (Lat1 only) and antagonist (Lat2, Dur2) were noted. Motor cancellation showed significantly shorter Dur2 EMG bursts (p < .0005) for all 3 stimuli conditions. Lat1 and Lat2 demonstrated significant correlation (p < 0.0005 for all), with the exception of dual stimulus condition during motor cancellation (p = 0.089). While dual stimulus during motor cancellation resulted in significantly shorter Lat2 (p = .013) in comparison with light and sound stimuli, this was not evident for motor activation tasks. The findings suggest that while a common central program exists for executing motor activation and cancellation, generation of antagonist activity in the latter may involve distinct neural pathways specifically robust to the effects of intersensory facilitation. This is discussed in relation to reciprocal motor oscillatory activity manifestations at the level of single joint movements.

Observation of locomotor functional recovery in adult complete spinal rats with BWSTT using semiquantitative and qualitative methods

STUDY DESIGN

Experimental rat model of spinal cord transection .

SETTING

China rehabilitation research center.

OBJECTIVE

To investigate locomotor functional recovery in spinal rats with BWSTT using semiquantitative and qualitative methods.

METHODS

Five-day postoperative (dpo), adult female complete spinal rats (at T(8) level) received 40 days of body weight-supported treadmill training (BWSTT). Signs of functional recovery were examined with average combined scores (ACOS) and Basso Beattie and Bresnahan (BBB) scales at different time points.

RESULTS

At 1-dpo, none of the spinal rats exhibited hindlimb movements. The spinal rats displayed functional progress with time, but the rare could recover to full weight-bearing hindlimb at 45-dpo. BBB and ACOS scores in the BWSTT group obtained better scores than those in the spinal cord injury (SCI) group at 30- and 45-dpo. Furthermore, all BBB and ACOS scores of spinal rats reached statistical significance between 7- and 30-dpo, and between 15- and 30-dpo. However, only ACOS but not BBB scores in the SCI and BWSTT groups showed statistics differences between 15- and 45-dpo, and between 30- and 45-dpo. The Spearman correlation coefficients of BBB and ACOS scores were 0.913 and 0.972 for the SCI and BWSTT groups, respectively.

CONCLUSIONS

The results confirmed the existence of partial spontaneous hindlimb functional recovery in adult chronically spinal cord-transected rats, and that BWSTT can improve motor performance. In addition, our study suggests that qualitative and semiquantiative methods are strongly correlated with locomotor recovery in spinal rats, and the latter may be more sensitive in reflecting minor variance at different time points.

Oscillations and oscillatory behavior in small neural circuits

In order to determine the dynamical properties of central pattern generators (CPGs), we have examined the lobster stomatogastric ganglion using the tools of nonlinear dynamics. The lobster pyloric and gastric mill central pattern generators can be analyzed at both the cellular and network levels because they are small, i.e., contain only 25 neurons between them and each neuron and synapse are repeatedly identifiable from animal to animal. We discuss how the biophysical properties of each neuron and synapse in the two circuits act cooperatively to generate two different patterns of sequential activity, how these patterns are altered by neuromodulators and perturbed by noise and sensory inputs. Finally, we show how simplified Hindmarsh-Rose models can be made into analog electronic neurons that mimic the lobster neurons and in addition be incorporated into artificial CPGs with robotic applications.

Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex

Actions of inhibitory interneurons organize and modulate many neuronal processes, yet the mechanisms and consequences of plasticity of inhibitory synapses remain poorly understood. We report on spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. After pairing presynaptic stimulations at time t(pre) with evoked postsynaptic spikes at time t(post) under pharmacological blockade of excitation we found, via whole cell recordings, an asymmetrical timing rule for plasticity of the remaining inhibitory responses. Strength of response varied as a function of the time interval Deltat = t(post) - t(pre): for Deltat > 0 inhibitory responses potentiated, peaking at a delay of 10 ms. For Deltat < 0, the synaptic coupling depressed, again with a maximal effect near 10 ms of delay. We also show that changes in synaptic strength depend on changes in intracellular calcium concentrations and demonstrate that the calcium enters the postsynaptic cell through voltage-gated channels. Using network models, we demonstrate how this novel form of plasticity can sculpt network behavior efficiently and with remarkable flexibility.

Age-related changes in transient and oscillatory brain responses to auditory stimulation in healthy adults 19-45 years old

The capacity of the human cerebral cortex to track fast temporal changes in auditory stimuli is related to the development of language in children and to deficits in speech perception in the elderly. Although maturation of temporal processing in children and its deterioration in the elderly has been investigated previously, little is known about naturally occurring changes in auditory temporal processing between these limits. The present study examined age-related (19-45 years) changes in 3 electrophysiological measures of auditory processing: 1) the late transient auditory evoked potentials to tone onset, 2) the auditory steady-state response (ASSR) to a 40-Hz frequency-modulated tone, and 3) the envelope following response (EFR) to sweeps of amplitude-modulated white noise from 10 to 100 Hz. With increasing age, the latency of the auditory P1-N1 complex decreased, the oscillatory (ASSR) response became larger and more stable, and the resonant peak of the EFR increased from 38 Hz at 19 years to 46 Hz at 45 years. Source analysis localized these changes to the auditory regions of the temporal lobe. These results indicate persistent adaptation of cortical auditory processes into middle adulthood. We speculate that experience-driven myelination and/or refinement of inhibitory circuits may underlie these changes.

Variability, compensation and homeostasis in neuron and network function

Neurons in most animals live a very long time relative to the half-lives of all of the proteins that govern excitability and synaptic transmission. Consequently, homeostatic mechanisms are necessary to ensure stable neuronal and network function over an animal's lifetime. To understand how these homeostatic mechanisms might function, it is crucial to understand how tightly regulated synaptic and intrinsic properties must be for adequate network performance, and the extent to which compensatory mechanisms allow for multiple solutions to the production of similar behaviour. Here, we use examples from theoretical and experimental studies of invertebrates and vertebrates to explore several issues relevant to understanding the precision of tuning of synaptic and intrinsic currents for the operation of functional neuronal circuits.

Maintaining your youthful spontaneity: microcircuit homeostasis in the embryonic spinal cord

Many developing networks generate spontaneous network activity (SNA) that plays an important role in setting up functional circuitry, but how the proper level and pattern of SNA is itself maintained has not been clear. In this issue of Neuron, Gonzalez-Islas and Wenner show that SNA in the intact embryo regulates itself through a set of adaptive homeostatic plasticity mechanisms.

Animal-to-animal variability in motor pattern production in adults and during growth

Which features of network output are well preserved during growth of the nervous system and across different preparations of the same size? To address this issue, we characterized the pyloric rhythms generated by the stomatogastric nervous systems of 99 adult and 12 juvenile lobsters (Homarus americanus). Anatomical studies of single pyloric network neurons and of the whole stomatogastric ganglion (STG) showed that the STG and its neurons grow considerably from juvenile to adult. Despite these changes in size, intracellularly recorded membrane potential waveforms of pyloric network neurons and the phase relationships in the pyloric rhythm were very similar between juvenile and adult preparations. Across adult preparations, the cycle period and number of spikes per burst were not tightly maintained, but the mean phase relationships were independent of the period of the rhythm and relatively tightly maintained across preparations. We interpret this as evidence for homeostatic regulation of network activity.

Synaptic depression as a timing device

A depressing synapse transforms a time interval into a voltage amplitude. The effect of that transformation on the output of the neuron and network depends on the kinetics of synaptic depression and properties of the postsynaptic neuron and network. Using as examples neural circuits that incorporate depressing synapses, we show how short-term depression can contribute to a surprising variety of time-dependent computational and behavioral tasks.

A Learning Rule for Local Synaptic Interactions Between Excitation and Shunting Inhibition

The basic requirement for direction selectivity is a nonlinear interaction between two different inputs in space-time. In some models, the interaction is hypothesized to occur between excitation and inhibition of the shunting type in the neuron's dendritic tree. How can the required spatial specificity be acquired in an unsupervised manner? We here propose an activity-based, local learning model that can account for direction selectivity in visual cortex based on such a local veto operation and that depends on synaptically induced changes in intracellular calcium concentration. Our biophysical simulations suggest that a model cell with our learning algorithm can develop direction selectivity organically after unsupervised training. The learning rule is also applicable to a neuron with multiple-direction-selective subunits and to a pair of cells with opposite-direction selectivities and is stable under different starting conditions, delays, and velocities.

Quantitative Investigation of Calcium Signals for Locomotor Pattern Generation in the Lamprey Spinal Cord

Locomotor pattern generation requires the network coordination of spinal ventral horn neurons acting in concert with the oscillatory properties of individual neurons. In the spinal cord, N-methyl-d-aspartate (NMDA) activates neuronal oscillators that are believed to rely on Ca2+entry to the cytosol through voltage-operated Ca2+channels and synaptically activated NMDA receptors. Ca2+signaling in lamprey ventral horn neurons thus plays a determinant role in the regulation of the intrinsic membrane properties and network synaptic interaction generating spinal locomotor neural pattern activity. We have characterized aspects of this signaling quantitatively for the first time. Resting Ca2+concentrations were between 87 and 120 nM. Ca2+concentration measured during fictive locomotion increased from soma to distal dendrites [from 208 ± 27 (SE) nM in the soma to 335 ± 41 nM in the proximal dendrites to 457 ± 68 nM in the distal dendrites]. We sought to determine the temporal and spatial properties of Ca2+oscillations, imaged with Ca2+-sensitive dyes and correlated with fluctuations in membrane potential, during lamprey fictive locomotion. The Ca2+signals recorded in the dendrites showed a great deal of spatial heterogeneity. Rapid changes in Ca2+-induced fluorescence coincided with action potentials, which initiated significant Ca2+transients distributed throughout the neurons. Ca2+entry to the cytosol coincided with the depolarizing phase of the locomotor rhythm. During fictive locomotion, larger Ca2+oscillations were recorded in dendrites compared with somata in motoneurons and premotor interneurons. Ca2+fluctuations were barely detected with dyes of lower affinity providing alternative empirical evidence that Ca2+responses are limited to hundreds of nanomolars during fictive locomotion.

Episodic bouts of activity accompany recovery of rhythmic output by a neuromodulator- and activity-deprived adult neural network

The pyloric rhythm of the stomatogastric ganglion of the crab, Cancer borealis, slows or stops when descending modulatory inputs are acutely removed. However, the rhythm spontaneously resumes after one or more days in the absence of neuromodulatory input. We recorded continuously for days to characterize quantitatively this recovery process. Activity bouts lasting 40-900 s began several hours after removal of neuromodulatory input and were followed by stable rhythm recovery after 1-4 days. Bout duration was not related to the intervals (0.3-800 min) between bouts. During an individual bout, the frequency rapidly increased and then decreased more slowly. Photoablation of back-filled neuromodulatory terminals in the stomatogastric ganglion (STG) neuropil had no effect on activity bouts or recovery, suggesting that these processes are intrinsic to the STG neuronal network. After removal of neuromodulatory input, the phase relationships of the components of the triphasic pyloric rhythm were altered, and then over time the phase relationships moved toward their control values. Although at low pyloric rhythm frequency the phase relationships among pyloric network neurons depended on frequency, the changes in frequency during recovery did not completely account for the change in phase seen after rhythm recovery. We suggest that activity bouts represent underlying mechanisms controlling the restructuring of the pyloric network to allow resumption of an appropriate output after removal of neuromodulatory input.

Neurobiological mechanisms involved in sleep bruxism

Sleep bruxism (SB) is reported by 8% of the adult population and is mainly associated with rhythmic masticatory muscle activity (RMMA) characterized by repetitive jaw muscle contractions (3 bursts or more at a frequency of 1 Hz). The consequences of SB may include tooth destruction, jaw pain, headaches, or the limitation of mandibular movement, as well as tooth-grinding sounds that disrupt the sleep of bed partners. SB is probably an extreme manifestation of a masticatory muscle activity occurring during the sleep of most normal subjects, since RMMA is observed in 60% of normal sleepers in the absence of grinding sounds. The pathophysiology of SB is becoming clearer, and there is an abundance of evidence outlining the neurophysiology and neurochemistry of rhythmic jaw movements (RJM) in relation to chewing, swallowing, and breathing. The sleep literature provides much evidence describing the mechanisms involved in the reduction of muscle tone, from sleep onset to the atonia that characterizes rapid eye movement (REM) sleep. Several brainstem structures (e.g., reticular pontis oralis, pontis caudalis, parvocellularis) and neurochemicals (e.g., serotonin, dopamine, gamma aminobutyric acid [GABA], noradrenaline) are involved in both the genesis of RJM and the modulation of muscle tone during sleep. It remains unknown why a high percentage of normal subjects present RMMA during sleep and why this activity is three times more frequent and higher in amplitude in SB patients. It is also unclear why RMMA during sleep is characterized by co-activation of both jaw-opening and jaw-closing muscles instead of the alternating jaw-opening and jaw-closing muscle activity pattern typical of chewing. The final section of this review proposes that RMMA during sleep has a role in lubricating the upper alimentary tract and increasing airway patency. The review concludes with an outline of questions for future research.

Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks

The concept of bell-shaped persistent neural activity represents a cornerstone of the theory for the internal representation of analog quantities, such as spatial location or head direction. Previous models, however, relied on the unrealistic assumption of network homogeneity. We investigate this issue in a network model where fine tuning of parameters is destroyed by heterogeneities in cellular and synaptic properties. Heterogeneities result in the loss of stored spatial information in a few seconds. Accurate encoding is recovered when a homeostatic mechanism scales the excitatory synapses to each cell to compensate for the heterogeneity in cellular excitability and synaptic inputs. Moreover, the more realistic model produces a wide diversity of tuning curves, as commonly observed in recordings from prefrontal neurons. We conclude that recurrent attractor networks in conjunction with appropriate homeostatic mechanisms provide a robust, biologically plausible theoretical framework for understanding the neural circuit basis of spatial working memory.

Reversible disorganization of the locomotor pattern after neonatal spinal cord transection in the rat

The central pattern generators (CPGs) for locomotion, located in the lumbar spinal cord, are functional at birth in the rat. Their maturation occurs during the last few days preceding birth, a period during which the first projections from the brainstem start to reach the lumbar enlargement of the spinal cord. The goal of the present study was to investigate the effect of suppressing inputs from supraspinal structures on the CPGs, shortly after their formation. The spinal cord was transected at the thoracic level at birth [postnatal day 0 (P0)]. We examined during the first postnatal week the capacity of the CPGs to produce rhythmic motor activity in two complementary experimental conditions. Left and right ankle extensor muscles were recorded in vivo during airstepping, and lumbar ventral roots were recorded in vitro during pharmacologically evoked fictive locomotion. Mechanical stimulation of the tail elicited long-lasting sequences of airstepping in the spinal neonates and only a few steps in sham-operated rats. In vitro experiments made simultaneously on spinal and sham animals confirmed the increased excitability of the CPGs after spinalization. A left-right alternating locomotor pattern was observed at P1-P3. Both types of experiments showed that the pattern was disorganized at P6-P7, and that the left-right alternation was lost. Alternation was restored after the activation of serotonergic 5-HT(2) receptors in vivo. These results suggest that descending pathways, in particular serotonergic projections, control the strength of reciprocal inhibition and therefore shape the locomotor pattern in the neonatal rat.

Activity-dependent modulation of GABAergic synapses in developing rat spinal networks in vitro

The role of activity-dependent plasticity in modulating inhibitory synapses was investigated in embryonic rat spinal cord slice cultures, by chronic exposure to non-NMDA receptor blockers. GABAergic synaptic efficacy in control and chronic-treated cultures was investigated by patch-recordings from visually identified spinal interneurons. In both culture groups proximal stimulation induced the appearance of postsynaptic currents (PSCs), which were fully antagonized by 20 microM bicuculline application and reverse polarity at potential values close to those reported for spontaneous GABAergic PSCs. In chronically treated cells GABAergic evoked PSCs displayed a larger failure rate and a smaller coefficient of variation of mean PSC amplitude, when compared to controls. As opposed to controls, chronic GABAergic evoked PSCs did not facilitate upon paired-pulse stimulation. Facilitation at chronic synapses was observed when extracellular calcium levels were decreased below physiological values (< 2 mM). Kainate was used to disclose any functional differences between control and treated slices. In accordance with the presynaptic action of kainate, the application of this drug along with GYKI, an AMPA receptor selective antagonist, changed, with analogous potency, short-term plasticity of GABAergic synapses from control and treated cultures. Nevertheless, in chronic cultures, the downstream effects of such activation unmasked short-term depression. Ultrastructural analysis of synapses in chronically treated cultures showed a reduction both in symmetric synapses and in the number of vesicles at symmetric terminals. Thus, based on electrophysiological and ultrastructural data, it could be suggested that during the development of spinal circuits, GABAergic synapses are modulated by glutamatergic transmission, and thus implying that excitatory transmission regulates the strength of GABAergic synapses.

Modeling stability in neuron and network function: the role of activity in homeostasis

Individual neurons display characteristic firing patterns determined by the number and kind of ion channels in their membranes. We describe experimental and computational studies that suggest that neurons use activity sensors to regulate the number and kind of ion channels and receptors in their membrane to maintain a stable pattern of activity and to compensate for ongoing processes of degradation, synthesis and insertion of ion channels and receptors. We show that similar neuronal and network outputs can be produced by a number of different combinations of ion channels and synapse strengths. This suggests that individual neurons of the same class may each have found an acceptable solution to a genetically determined pattern of activity, and that networks of neurons in different animals may produce similar output patterns by somewhat variable underlying mechanisms.