From biophysics to models of network function

Degenerate neuronal and circuit mechanisms important for generating rhythmic motor patterns

In 1996, we published a review article (Marder E, Calabrese RL. Physiol Rev 76: 687-717, 1996) describing the state of knowledge about the structure and function of the central pattern-generating circuits important for producing rhythmic behaviors. Although many of the core questions persist, much has changed since 1996. Here, we focus on newer studies that reveal ambiguities that complicate understanding circuit dynamics, despite the enormous technical advances of the recent past. In particular, we highlight recent studies of animal-to-animal variability and our understanding that circuit rhythmicity may be supported by multiple state-dependent mechanisms within the same animal and that robustness and resilience in the face of perturbation may depend critically on the presence of modulators and degenerate circuit mechanisms. Additionally, we highlight the use of computational models to ask whether there are generalizable principles about circuit motifs that can be found across rhythmic motor systems in different animal species.

Synergies between synaptic and HCN channel plasticity dictates firing rate homeostasis and mutual information transfer in hippocampal model neuron

Homeostasis is a precondition for any physiological system of any living organism. Nonetheless, models of learning and memory that are based on processes of synaptic plasticity are unstable by nature according to Hebbian rules, and it is not fully clear how homeostasis is maintained during these processes. This is where theoretical and computational frameworks can help in gaining a deeper understanding of the various cellular processes that enable homeostasis in the face of plasticity. A previous simplistic single compartmental model with a single synapse showed that maintaining input/output response homeostasis and stable synaptic learning could be enabled by introducing a linear relationship between synaptic plasticity and HCN conductance plasticity. In this study, we aimed to examine whether this approach could be extended to a more morphologically realistic model that entails multiple synapses and gradients of various VGICs. In doing so, we found that a linear relationship between synaptic plasticity and HCN conductance plasticity was able to maintain input/output response homeostasis in our morphologically realistic model, where the slope of the linear relationship was dependent on baseline HCN conductance and synaptic permeability values. An increase in either baseline HCN conductance or synaptic permeability value led to a decrease in the slope of the linear relationship. We further show that in striking contrast to the single compartment model, here linear relationship was insufficient in maintaining stable synaptic learning despite maintaining input/output response homeostasis. Additionally, we showed that homeostasis of input/output response profiles was at the expense of decreasing the mutual information transfer due to the increase in noise entropy, which could not be fully rescued by optimizing the linear relationship between synaptic and HCN conductance plasticity. Finally, we generated a place cell model based on theta oscillations and show that synaptic plasticity disrupts place cell activity. Whereas synaptic plasticity accompanied by HCN conductance plasticity through linear relationship maintains the stability of place cell activity. Our study establishes potential differences between a single compartmental model and a morphologically realistic model.

Connectomic features underlying diverse synaptic connection strengths and subcellular computation

Connectomes generated from electron microscopy images of neural tissue unveil the complex morphology of every neuron and the locations of every synapse interconnecting them. These wiring diagrams may also enable inference of synaptic and neuronal biophysics, such as the functional weights of synaptic connections, but this requires integration with physiological data to properly parameterize. Working with a stereotyped olfactory network in the Drosophila brain, we make direct comparisons of the anatomy and physiology of diverse neurons and synapses with subcellular and subthreshold resolution. We find that synapse density and location jointly predict the amplitude of the somatic postsynaptic potential evoked by a single presynaptic spike. Biophysical models fit to data predict that electrical compartmentalization allows axon and dendrite arbors to balance independent and interacting computations. These findings begin to fill the gap between connectivity maps and activity maps, which should enable new hypotheses about how network structure constrains network function.

Respiratory neuroplasticity: Mechanisms and translational implications of phrenic motor plasticity

Widespread appreciation that neuroplasticity is an essential feature of the neural system controlling breathing has emerged only in recent years. In this chapter, we focus on respiratory motor plasticity, with emphasis on the phrenic motor system. First, we define related but distinct concepts: neuromodulation and neuroplasticity. We then focus on mechanisms underlying two well-studied models of phrenic motor plasticity: (1) phrenic long-term facilitation following brief exposure to acute intermittent hypoxia; and (2) phrenic motor facilitation after prolonged or recurrent bouts of diminished respiratory neural activity. Advances in our understanding of these novel and important forms of plasticity have been rapid and have already inspired translation in multiple respects: (1) development of novel therapeutic strategies to preserve/restore breathing function in humans with severe neurological disorders, such as spinal cord injury and amyotrophic lateral sclerosis; and (2) the discovery that similar plasticity also occurs in nonrespiratory motor systems. Indeed, the realization that similar plasticity occurs in respiratory and nonrespiratory motor neurons inspired clinical trials to restore leg/walking and hand/arm function in people living with chronic, incomplete spinal cord injury. Similar application may be possible to other clinical disorders that compromise respiratory and non-respiratory movements.

Behavioral control by depolarized and hyperpolarized states of an integrating neuron

Coordinated transitions between mutually exclusive motor states are central to behavioral decisions. During locomotion, the nematode Caenorhabditis elegans spontaneously cycles between forward runs, reversals, and turns with complex but predictable dynamics. Here, we provide insight into these dynamics by demonstrating how RIM interneurons, which are active during reversals, act in two modes to stabilize both forward runs and reversals. By systematically quantifying the roles of RIM outputs during spontaneous behavior, we show that RIM lengthens reversals when depolarized through glutamate and tyramine neurotransmitters and lengthens forward runs when hyperpolarized through its gap junctions. RIM is not merely silent upon hyperpolarization: RIM gap junctions actively reinforce a hyperpolarized state of the reversal circuit. Additionally, the combined outputs of chemical synapses and gap junctions from RIM regulate forward-to-reversal transitions. Our results indicate that multiple classes of RIM synapses create behavioral inertia during spontaneous locomotion.

Integration, coincidence detection and resonance in networks of spiking neurons expressing Gamma oscillations and asynchronous states

Gamma oscillations are widely seen in the awake and sleeping cerebral cortex, but the exact role of these oscillations is still debated. Here, we used biophysical models to examine how Gamma oscillations may participate to the processing of afferent stimuli. We constructed conductance-based network models of Gamma oscillations, based on different cell types found in cerebral cortex. The models were adjusted to extracellular unit recordings in humans, where Gamma oscillations always coexist with the asynchronous firing mode. We considered three different mechanisms to generate Gamma, first a mechanism based on the interaction between pyramidal neurons and interneurons (PING), second a mechanism in which Gamma is generated by interneuron networks (ING) and third, a mechanism which relies on Gamma oscillations generated by pacemaker chattering neurons (CHING). We find that all three mechanisms generate features consistent with human recordings, but that the ING mechanism is most consistent with the firing rate change inside Gamma bursts seen in the human data. We next evaluated the responsiveness and resonant properties of these networks, contrasting Gamma oscillations with the asynchronous mode. We find that for both slowly-varying stimuli and precisely-timed stimuli, the responsiveness is generally lower during Gamma compared to asynchronous states, while resonant properties are similar around the Gamma band. We could not find conditions where Gamma oscillations were more responsive. We therefore predict that asynchronous states provide the highest responsiveness to external stimuli, while Gamma oscillations tend to overall diminish responsiveness.

In the awake and attentive brain, the activity of neurons is typically asynchronous and irregular. It also occasionally displays oscillations in the Gamma frequency range (30–90 Hz), which are believed to be involved in information processing. Here, we use computational models to investigate how brain circuits generate oscillations in a manner consistent with microelectrode recordings in humans. We then study how these networks respond to external input, comparing asynchronous and oscillatory states. This is tested according to several paradigms, an integrative mode, where slowly varying inputs are progressively integrated, a coincidence detection mode, where brief inputs are processed according to the phase of the oscillations, and a resonance mode where the network is probed with oscillatory inputs. Surprisingly, we find that in all cases, the presence of Gamma oscillations tends to diminish the responsiveness to external inputs. We discuss possible implications of this responsiveness decrease on information processing and propose new directions for further exploration.

Interrogating theoretical models of neural computation with emergent property inference

A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon -- whether behavioral or a pattern of neural activity -- and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters. A key step is then to identify the model parameters consistent with observed phenomena: to solve the inverse problem. In this work, we present a novel technique, emergent property inference (EPI), that brings the modern probabilistic modeling toolkit to theoretical neuroscience. When theorizing circuit models, theoreticians predominantly focus on reproducing computational properties rather than a particular dataset. Our method uses deep neural networks to learn parameter distributions with these computational properties. This methodology is introduced through a motivational example of parameter inference in the stomatogastric ganglion. EPI is then shown to allow precise control over the behavior of inferred parameters and to scale in parameter dimension better than alternative techniques. In the remainder of this work, we present novel theoretical findings in models of primary visual cortex and superior colliculus, which were gained through the examination of complex parametric structure captured by EPI. Beyond its scientific contribution, this work illustrates the variety of analyses possible once deep learning is harnessed towards solving theoretical inverse problems.

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.

Intrinsic neuronal properties represent song and error in zebra finch vocal learning

Neurons regulate their intrinsic physiological properties, which could influence network properties and contribute to behavioral plasticity. Recording from adult zebra finch brain slices we show that within each bird basal ganglia Area X-projecting (HVCX) neurons share similar spike waveform morphology and timing of spike trains, with modeling indicating similar magnitudes of five principal ion currents. These properties vary among birds in lawful relation to acoustic similarity of the birds' songs, with adult sibling pairs (same songs) sharing similar waveforms and spiking characteristics. The properties are maintained dynamically: HVCX within juveniles learning to sing show variable properties, whereas the uniformity rapidly degrades within hours in adults singing while exposed to abnormal (delayed) auditory feedback. Thus, within individual birds the population of current magnitudes covary over the arc of development, while rapidly responding to changes in feedback (in adults). This identifies network interactions with intrinsic properties that affect information storage and processing of learned vocalizations.

Degeneracy in hippocampal physiology and plasticity

Degeneracy, defined as the ability of structurally disparate elements to perform analogous function, has largely been assessed from the perspective of maintaining robustness of physiology or plasticity. How does the framework of degeneracy assimilate into an encoding system where the ability to change is an essential ingredient for storing new incoming information? Could degeneracy maintain the balance between the apparently contradictory goals of the need to change for encoding and the need to resist change towards maintaining homeostasis? In this review, we explore these fundamental questions with the mammalian hippocampus as an example encoding system. We systematically catalog lines of evidence, spanning multiple scales of analysis that point to the expression of degeneracy in hippocampal physiology and plasticity. We assess the potential of degeneracy as a framework to achieve the conjoint goals of encoding and homeostasis without cross-interferences. We postulate that biological complexity, involving interactions among the numerous parameters spanning different scales of analysis, could establish disparate routes towards accomplishing these conjoint goals. These disparate routes then provide several degrees of freedom to the encoding-homeostasis system in accomplishing its tasks in an input- and state-dependent manner. Finally, the expression of degeneracy spanning multiple scales offers an ideal reconciliation to several outstanding controversies, through the recognition that the seemingly contradictory disparate observations are merely alternate routes that the system might recruit towards accomplishment of its goals.

Dopaminergic Modulation of Synaptic Plasticity, Its Role in Neuropsychiatric Disorders, and Its Computational Modeling

Neuromodulators modify intrinsic characteristics of the nervous system in order to reconfigure the functional properties of neural circuits. This reconfiguration is crucial for the flexibility of the nervous system to respond on an input-modulated basis. Such a functional rearrangement is realized by modification of intrinsic properties of the neural circuits including synaptic interactions. Dopamine is an important neuromodulator involved in motivation and stimulus-reward learning process, and adjusts synaptic dynamics in multiple time scales through different pathways. The modification of synaptic plasticity by dopamine underlies the change in synaptic transmission and integration mechanisms, which affects intrinsic properties of the neural system including membrane excitability, probability of neurotransmitters release, receptors’ response to neurotransmitters, protein trafficking, and gene transcription. Dopamine also plays a central role in behavioral control, whereas its malfunction can cause cognitive disorders. Impaired dopamine signaling is implicated in several neuropsychiatric disorders such as Parkinson’s disease, drug addiction, schizophrenia, attention-deficit/hyperactivity disorder, obsessive-compulsive disorder and Tourette’s syndrome. Therefore, dopamine plays a crucial role in the nervous system, where its proper modulation of neural circuits may enhance plasticity-related procedures, but disturbances in dopamine signaling might be involved in numerous neuropsychiatric disorders. In recent years, several computational models are proposed to formulate the involvement of dopamine in synaptic plasticity or neuropsychiatric disorders and address their connection based on the experimental findings.

Long-lasting changes in neural networks to compensate for altered nicotinic input

The nervous system must balance excitatory and inhibitory input to constrain network activity levels within a proper dynamic range. This is a demanding requirement during development, when networks form and throughout adulthood as networks respond to constantly changing environments. Defects in the ability to sustain a proper balance of excitatory and inhibitory activity are characteristic of numerous neurological disorders such as schizophrenia, Alzheimer's disease, and autism. A variety of homeostatic mechanisms appear to be critical for balancing excitatory and inhibitory activity in a network. These are operative at the level of individual neurons, regulating their excitability by adjusting the numbers and types of ion channels, and at the level of synaptic connections, determining the relative numbers of excitatory versus inhibitory connections a neuron receives. Nicotinic cholinergic signaling is well positioned to contribute at both levels because it appears early in development, extends across much of the nervous system, and modulates transmission at many kinds of synapses. Further, it is known to influence the ratio of excitatory-to-inhibitory synapses formed on neurons during development. GABAergic inhibitory neurons are likely to be key for maintaining network homeostasis (limiting excitatory output), and nicotinic signaling is known to prominently regulate the activity of several GABAergic neuronal subtypes. But how nicotinic signaling achieves this and how networks may compensate for the loss of such input are important questions remaining unanswered. These issues are reviewed.

Coordinated forms of noradrenergic plasticity in the locus coeruleus and primary auditory cortex

The cerebral cortex is plastic and represents the world according to the significance of sensory stimuli. However, cortical networks are embodied in complex circuits, including neuromodulatory systems such as the noradrenergic locus coeruleus, providing information about internal state and behavioral relevance. Although norepinephrine is important for cortical plasticity, it is unknown how modulatory neurons themselves respond to changes of sensory input. We examined how locus coeruleus neurons are modified by experience and the consequences of locus coeruleus plasticity for cortical representations and sensory perception. We made whole-cell recordings from rat locus coeruleus and primary auditory cortex (A1), pairing sounds with locus coeruleus activation. Although initially unresponsive, locus coeruleus neurons developed and maintained auditory responses afterwards. Locus coeruleus plasticity induced changes in A1 responses lasting at least hours and improved auditory perception for days to weeks. Our results demonstrate that locus coeruleus is highly plastic, leading to substantial changes in regulation of brain state by norepinephrine.

Computation in dynamically bounded asymmetric systems

Previous explanations of computations performed by recurrent networks have focused on symmetrically connected saturating neurons and their convergence toward attractors. Here we analyze the behavior of asymmetrical connected networks of linear threshold neurons, whose positive response is unbounded. We show that, for a wide range of parameters, this asymmetry brings interesting and computationally useful dynamical properties. When driven by input, the network explores potential solutions through highly unstable 'expansion' dynamics. This expansion is steered and constrained by negative divergence of the dynamics, which ensures that the dimensionality of the solution space continues to reduce until an acceptable solution manifold is reached. Then the system contracts stably on this manifold towards its final solution trajectory. The unstable positive feedback and cross inhibition that underlie expansion and divergence are common motifs in molecular and neuronal networks. Therefore we propose that very simple organizational constraints that combine these motifs can lead to spontaneous computation and so to the spontaneous modification of entropy that is characteristic of living systems.

Gating of steering signals through phasic modulation of reticulospinal neurons during locomotion

The neural control of movements in vertebrates is based on a set of modules, like the central pattern generator networks (CPGs) in the spinal cord coordinating locomotion. Sensory feedback is not required for the CPGs to generate the appropriate motor pattern and neither a detailed control from higher brain centers. Reticulospinal neurons in the brainstem activate the locomotor network, and the same neurons also convey signals from higher brain regions, such as turning/steering commands from the optic tectum (superior colliculus). A tonic increase in the background excitatory drive of the reticulospinal neurons would be sufficient to produce coordinated locomotor activity. However, in both vertebrates and invertebrates, descending systems are in addition phasically modulated because of feedback from the ongoing CPG activity. We use the lamprey as a model for investigating the role of this phasic modulation of the reticulospinal activity, because the brainstem-spinal cord networks are known down to the cellular level in this phylogenetically oldest extant vertebrate. We describe how the phasic modulation of reticulospinal activity from the spinal CPG ensures reliable steering/turning commands without the need for a very precise timing of on- or offset, by using a biophysically detailed large-scale (19,600 model neurons and 646,800 synapses) computational model of the lamprey brainstem-spinal cord network. To verify that the simulated neural network can control body movements, including turning, the spinal activity is fed to a mechanical model of lamprey swimming. The simulations also predict that, in contrast to reticulospinal neurons, tectal steering/turning command neurons should have minimal frequency adaptive properties, which has been confirmed experimentally.

Removing sensory input disrupts spinal locomotor activity in the early postnatal period

Motor patterns driving rhythmic movements of our lower limbs during walking are generated by groups of neurons within the spinal cord, called central pattern generators (CPGs). After suffering a spinal cord injury (SCI), many descending fibers from our brain are severed or become nonfunctional, leaving the spinal CPG network without its initiating drive. Recent studies have focused on the importance of maintaining sensory stimulation to the limbs of SCI patients as a way to initiate and control the CPG locomotor network. We began assessing the role of sensory feedback to the locomotor CPG network using a neonatal mouse spinal cord preparation where the hindlimbs are still attached. Removing sensory feedback coming from the hindlimbs by way of a lower lumbar transection or by ventral root denervation revealed a positive correlation in the ability of sensory input deprivation to disrupt ongoing locomotor activity on older versus younger animals. The differences in the motor responses as a function of age could be correlated with the loss of excitatory activity from sensory afferents. Continued studies on this field could eventually provide key information that translates into the design of novel therapeutic strategies to treat patients who have suffered a SCI.

Influence of asymmetric attenuation of single and paired dendritic inputs on summation of synaptic potentials and initiation of action potentials

Previous studies revealed mechanisms of dendritic inputs leading to action potential initiation at the axon initial segment and backpropagation into the dendritic tree. This interest has recently expanded toward the communication between different parts of the dendritic tree which could preprocess information before reaching the soma. This study tested for effects of asymmetric voltage attenuation between different sites in the dendritic tree on summation of synaptic inputs and action potential initiation using the NEURON simulation environment. Passive responses due to the electrical equivalent circuit of the three-dimensional neuron architecture with leak channels were examined first, followed by the responses after adding voltage-gated channels and finally synaptic noise. Asymmetric attenuation of voltage, which is a function of asymmetric input resistance, was seen between all pairs of dendritic sites but the transfer voltages (voltage recorded at the opposite site from stimulation among a pair of dendritic sites) were equal and also summed linearly with local voltage responses during simultaneous stimulation of both sites. In neurons with voltage-gated channels, we reproduced the observations where a brief stimulus to the proximal ascending dendritic branch of a pyramidal cell triggers a local action potential but a long stimulus triggers a somal action potential. Combined stimulation of a pair of sites in this proximal dendrite did not alter this pattern. The attraction of the action potential onset toward the soma with a long stimulus in the absence of noise was due to the higher density of voltage-gated sodium channels at the axon initial segment. This attraction was, however, negligible at the most remote distal dendritic sites and was replaced by an effect due to high input resistance. Action potential onset occurred at the dendritic site of higher input resistance among a pair of remote dendritic sites, irrespective of which of these two sites received the synaptic input. Exploration of the parameter space showed how the gradient of voltage-gated channel densities and input resistances along a dendrite could draw the action potential onset away from the stimulation site. The attraction of action potential onset toward the higher density of voltage-gated channels in the soma during stimulation of the proximal dendrite was, however, reduced after the addition of synaptic noise.

Three independent techniques localize expression of transcript afp-11 and its bioactive peptide products to the paired AVK neurons in Ascaris suum: in situ hybridization, immunocytochemistry, and single cell mass spectrometry

We utilized three independent techniques, immunocytochemistry (ICC), single cell mass spectrometry (MS), and in situ hybridization (ISH), to localize neuropeptides and their transcripts in the nervous system of the nematode Ascaris suum . AF11 (SDIGISEPNFLRFa) is an endogenous peptide with potent paralytic effects on A. suum locomotory behavior. A highly specific antibody to AF11 showed robust immunostaining for AF11 in the paired AVK neurons in the ventral ganglion. We traced the processes from the AVK neurons into the ventral nerve cord and identified them as ventral cord interneurons. MS and MS/MS of single dissected AVKs detected AF11, two previously characterized peptides (AF25 and AF26), seven novel sequence-related peptides, including several sharing a PNFLRFamide C-terminus, and peptide NY, a peptide with an unrelated sequence. Also present in a subset of AVKs was AF2, a peptide encoded by the afp-4 transcript. By sequencing the afp-11 transcript, we discovered that it encodes AF11, all the AF11-related peptides detected by MS in AVK, and peptide NY. ISH detected the afp-11 transcript in AVK neurons, consistent with other techniques. ISH did not detect afp-11 in the ALA neuron, although both ICC and MS found AF11 in ca. 30% of ALAs. All 10 AF11-related peptides reduced acetylcholine-induced muscle contraction, but they differed in their rate of reversal of inhibition after removal of the peptide.

Unusual increase in lumbar network excitability of the rat spinal cord evoked by the PARP-1 inhibitor PJ-34 through inhibition of glutamate uptake

Overactivity of poly(ADP-ribose) polymerase enzyme 1 (PARP-1) is suggested to be a major contributor to neuronal damage following brain or spinal cord injury, and has led to study the PARP-1 inhibitor 2-(dimethylamino)-N-(5,6-dihydro-6-oxophenanthridin-2yl)acetamide (PJ-34) as a neuroprotective agent. Unexpectedly, electrophysiological recording from the neonatal rat spinal cord in vitro showed that, under control conditions, 1-60 μM PJ-34 per se strongly increased spontaneous network discharges occurring synchronously on ventral roots, persisting for 24 h even after PJ-34 washout. The PARP-1 inhibitor PHE had no similar effect. The action by PJ-34 was reversibly suppressed by glutamate ionotropic receptor blockers and remained after applying strychnine and bicuculline. Fictive locomotion evoked by neurochemicals or by dorsal root stimulation was present 24 h after PJ-34 application. In accordance with this observation, lumbar neurons and glia were undamaged. Neurochemical experiments showed that PJ-34 produced up to 33% inhibition of synaptosomal glutamate uptake with no effect on GABA uptake. In keeping with this result, the glutamate uptake blocker TBOA (5 μM) induced long-lasting synchronous discharges without suppressing the ability to produce fictive locomotion after 24 h. The novel inhibition of glutamate uptake by PJ-34 suggested that this effect may compound tests for its neuroprotective activity which cannot be merely attributed to PARP-1 block. Furthermore, the current data indicate that the neonatal rat spinal cord could withstand a strong, long-lasting rise in network excitability without compromising locomotor pattern generation or circuit structure in contrast with the damage to brain circuits known to be readily produced by persistent seizures.

Functional connectivity in a rhythmic inhibitory circuit using Granger causality

Background

Understanding circuit function would be greatly facilitated by methods that allow the simultaneous estimation of the functional strengths of all of the synapses in the network during ongoing network activity. Towards that end, we used Granger causality analysis on electrical recordings from the pyloric network of the crab Cancer borealis, a small rhythmic circuit with known connectivity, and known neuronal intrinsic properties.

Results

Granger causality analysis reported a causal relationship where there is no anatomical correlate because of the strong oscillatory behavior of the pyloric circuit. Additionally, we failed to find a direct relationship between synaptic strength and Granger causality in a set of pyloric circuit models.

Conclusions

We conclude that the lack of a relationship between synaptic strength and functional connectivity occurs because Granger causality essentially collapses the direct contribution of the synapse with the intrinsic properties of the postsynaptic neuron. We suggest that the richness of the dynamical properties of most biological neurons complicates the simple interpretation of the results of functional connectivity analyses using Granger causality.

Invertebrate central pattern generator circuits

There are now a reasonable number of invertebrate central pattern generator (CPG) circuits described in sufficient detail that a mechanistic explanation of how they work is possible. These small circuits represent the best-understood neural circuits with which to investigate how cell-to-cell synaptic connections and individual channel conductances combine to generate rhythmic and patterned output. In this review, some of the main lessons that have appeared from this analysis are discussed and concrete examples of circuits ranging from single phase to multiple phase patterns are described. While it is clear that the cellular components of any CPG are basically the same, the topology of the circuits have evolved independently to meet the particular motor requirements of each individual organism and only a few general principles of circuit operation have emerged. The principal usefulness of small systems in relation to the brain is to demonstrate in detail how cellular infrastructure can be used to generate rhythmicity and form specialized patterns in a way that may suggest how similar processes might occur in more complex systems. But some of the problems and challenges associated with applying data from invertebrate preparations to the brain are also discussed. Finally, I discuss why it is useful to have well-defined circuits with which to examine various computational models that can be validated experimentally and possibly applied to brain circuits when the details of such circuits become available.

How multiple conductances determine electrophysiological properties in a multicompartment model

Most neurons have large numbers of voltage- and time-dependent currents that contribute to their electrical firing patterns. Because these currents are nonlinear, it can be difficult to determine the role each current plays in determining how a neuron fires. The lateral pyloric (LP) neuron of the stomatogastric ganglion of decapod crustaceans has been studied extensively biophysically. We constructed approximately 600,000 versions of a four-compartment model of the LP neuron and distributed 11 different currents into the compartments. From these, we selected approximately 1300 models that match well the electrophysiological properties of the biological neuron. Interestingly, correlations that were seen in the expression of channel mRNA in biological studies were not found across the approximately 1300 admissible LP neuron models, suggesting that the electrical phenotype does not require these correlations. We used cubic fits of the function from maximal conductances to a series of electrophysiological properties to ask which conductances predominantly influence input conductance, resting membrane potential, resting spike rate, phasing of activity in response to rhythmic inhibition, and several other properties. In all cases, multiple conductances contribute to the measured property, and the combinations of currents that strongly influence each property differ. These methods can be used to understand how multiple currents in any candidate neuron interact to determine the cell's electrophysiological behavior.

Multiple forms of activity-dependent intrinsic plasticity in layer V cortical neurones in vivo

Synaptic plasticity is classically considered as the neuronal substrate for learning and memory. However, activity-dependent changes in neuronal intrinsic excitability have been reported in several learning-related brain regions, suggesting that intrinsic plasticity could also participate to information storage. Compared to synaptic plasticity, there has been little exploration of the properties of induction and expression of intrinsic plasticity in an intact brain. Here, by the means of in vivo intracellular recordings in the rat we have examined how the intrinsic excitability of layer V motor cortex pyramidal neurones is altered following brief periods of repeated firing. Changes in membrane excitability were assessed by modifications in the discharge frequency versus injected current (F-I) curves. Most (approximately 64%) conditioned neurones exhibited a long-lasting intrinsic plasticity, which was expressed either by selective changes in the current threshold or in the slope of the F-I curve, or by concomitant changes in both parameters. These modifications in the neuronal input-output relationship led to a global increase or decrease in intrinsic excitability. Passive electrical membrane properties were unaffected by the intracellular conditioning, indicating that intrinsic plasticity resulted from modifications of voltage-gated ion channels. These results demonstrate that neocortical pyramidal neurones can express in vivo a bidirectional use-dependent intrinsic plasticity, modifying their sensitivity to weak inputs and/or the gain of their input-output function. These multiple forms of experience-dependent intrinsic changes, which expand the computational abilities of individual neurones, could shape new network dynamics and thus might participate in the formation of mnemonic motor engrams.

The contribution of synaptic inputs to sustained depolarizations in reticulospinal neurons

Sensory stimulation elicits sustained depolarizations in lamprey reticulospinal (RS) cells for which intrinsic properties were shown to play a crucial role. The depolarizations last up to minutes, and we tested whether the intrinsic properties required the cooperation of synaptic inputs to maintain RS cells depolarized for such long periods of time. Ascending spinal inputs to RS cells were reversibly blocked by applying xylocaine over the rostral spinal cord segments. The duration of the sustained depolarizations was markedly reduced. The membrane potential oscillations in tune with locomotor activity that were present under control condition were also abolished. The contribution of excitatory glutamatergic inputs was then assessed by applying CNQX and AP-5 over one of two simultaneously recorded homologous RS cells on each side of the brainstem. The level of sensory-evoked depolarization decreased significantly in the cell exposed to the antagonists compared with the other RS cell monitored as a control. In contrast, local application of glycine only produced a transient membrane potential hyperpolarization with a marked reduction in the amplitude of membrane potential oscillations. Locally applied strychnine did not change the duration of the sustained depolarizations, suggesting that mechanisms other than glycinergic inhibition are involved in ending the sustained depolarizations in RS cells. It is concluded that excitatory glutamatergic inputs, including ascending spinal feedback, cooperate with intrinsic properties of RS cells to maintain the cells depolarized for prolonged periods, sustaining long bouts of escape swimming.

Characterization of a descending pathway: activation and effects on motor patterns in the brachyuran crustacean stomatogastric nervous system

The regulation of motor patterns by higher-order neuronal centers ensures appropriate motor function and behavior, but only a few studies have characterized this regulation at the cellular level. Here, we address motor pattern regulation in the stomatogastric nervous system (STNS) of the crab Cancer pagurus. This easily accessible model system is an extension of the central nervous system and contains the motor circuits that generate the rhythmic motor patterns for ingestion (esophageal rhythm) and processing of food (gastric mill and pyloric rhythms).

We have documented the actions of two identified neurons located in the brain on the STNS motor circuits. We show that these neurons provide exteroceptive chemosensory information to the motor circuits and we outline their axonal projection patterns, their firing activity and their effects on three motor patterns. Backfill stainings and activity measurements in vivo and in vitro show that two neurons located in cluster 17 of the brain project via the inferior ventricular (IV) nerve to the STNS. These IV neurons started to burst rhythmically when chemosensory stimuli were applied to the first antennae. When rhythmically activated in vitro, gastric mill rhythms were elicited or, if already active,entrained by the IV neuron activity. In addition, IV neuron stimulation excited the esophageal motor neuron and inhibited several pyloric neurons such that the timing of the IV neuron activity was imposed on all motor rhythms. The IV neurons were thus capable of synchronizing the activities of different motor circuits, which demonstrates the regulation of motor patterns by higher-order neuronal centers.

The nervous system might 'orthogonalize' to discriminate

It is still unclear how information is actually stored in biological neural networks. We propose here that information could be first orthogonalized and then stored. This could happen in a manner similar to how a set of vectors is transformed into a set of orthogonalized (i.e. mutually perpendicular) vectors. Orthogonalization may overcome the limits of conventional artificial networks, particularly the catastrophic interference caused by interference between stored inputs. The features needed to allow orthogonalization are common to biological networks, suggesting that it may be a common network mechanism. To illustrate this hypothesis, we characterize the underlying features that an archetypal biological network must have in order to perform orthogonalization, and point out that a number of actual networks show this archetypal network organization.

Maintaining phase of the crustacean tri-phasic pyloric rhythm

We construct and analyze a model network of the pyloric rhythm of the crustacean stomatogastric ganglion consisting of an oscillator neuron that inhibits two reciprocally inhibitory follower neurons. We derive analytic expressions that determine the phase of firing of the follower neurons with respect to the oscillator. An important aspect of the model is the inclusion of synapses that exhibit short-term synaptic depression. We show that these type of synapses allow there to be a complicated relationship between the intrinsic properties of the neurons and the synapses between them in determining phase relationships. Our analysis reveals the circumstances and ranges of cycle periods under which these properties work in concert with or independently from one another. In particular, we show that phase maintenance over a range of oscillator periods can be enhanced through the interplay of the two follower neurons if the synapses between these neurons are depressing. Since our model represents the core of the oscillatory pyloric network, the results of our analysis can be compared to experimental data and used to make predictions about the biological network.

How tightly tuned are network parameters? Insight from computational and experimental studies in small rhythmic motor networks

We describe theoretical and experimental studies that demonstrate that a given pattern of neuronal activity can be produced by variable sets of underlying conductances. Experimental work demonstrates that individual identified neurons in different animals may show variations as large as 2-5 fold in the conductance densities of specific ion channels. Theoretical work shows that models with this range of variation in many of their maximal conductances can produce similar activity. Together, these observations suggest that neurons and networks may be less tightly tuned than previously thought. Consequently, we argue that instead of attempting to construct single canonical models of neuronal function, it might be more useful to construct and analyze large families of models that give similar behavior.

Target-specific regulation of synaptic efficacy in the feeding central pattern generator of Aplysia: potential substrates for behavioral plasticity?

The contributions to this symposium are unified by their focus on the role of synaptic plasticity in sensorimotor learning. Synaptic plasticities are also known to operate within the central pattern generator (CPG) circuits that produce repetitive motor programs, where their relation to adaptive behavior is less well understood. This study examined divergent synaptic plasticity in the signaling of an influential interneuron, B20, located within the CPG that controls consummatory feeding-related behaviors in Aplysia. Previously, B20 was shown to contain markers for catecholamines and GABA (Díaz-Ríos et al., 2002), and its rapid synaptic signaling to two follower motor neurons, B16 and B8, was found to be mediated by dopamine (Díaz-Ríos and Miller, 2005). In this investigation, two incremental forms of increased synaptic efficacy, facilitation and summation, were both greater in the signaling from B20 to B8 than in the signaling from B20 to B16. Manipulation of the membrane potentials of the two postsynaptic motor neurons did not affect facilitation of excitatory postsynaptic potentials (EPSPs) to either follower cell. Striking levels of summation in B8, however, were eliminated at hyperpolarized membrane potentials and could be attributed to distinctive membrane properties of this postsynaptic cell. GABA and the GABAB agonist baclofen increased facilitation and summation of EPSPs from B20 to B8, but not to B16. The enhanced facilitation was not affected when the membrane potential of B8 was pre-set to hyperpolarized levels, but GABAergic effects on summation were eliminated by this manipulation. These observations demonstrate a target-specific amplification of synaptic efficacy that can contribute to channeling the flow of divergent information from an intrinsic interneuron within the buccal CPG. They further suggest that GABA, acting as a cotransmitter in B20, could induce coordinated and target-specific pre- and postsynaptic modulation of these signals. Finally, we speculate that target-specific plasticity and its modulation could be efficient, specific, and flexible substrates for learning-related modifications of CPG function.

Contributions of intrinsic membrane dynamics to fast network oscillations with irregular neuronal discharges

During fast oscillations in the local field potential (40-100 Hz gamma, 100-200 Hz sharp-wave ripples) single cortical neurons typically fire irregularly at rates that are much lower than the oscillation frequency. Recent computational studies have provided a mathematical description of such fast oscillations, using the leaky integrate-and-fire (LIF) neuron model. Here, we extend this theoretical framework to populations of more realistic Hodgkin-Huxley-type conductance-based neurons. In a noisy network of GABAergic neurons that are connected randomly and sparsely by chemical synapses, coherent oscillations emerge with a frequency that depends sensitively on the single cell's membrane dynamics. The population frequency can be predicted analytically from the synaptic time constants and the preferred phase of discharge during the oscillatory cycle of a single cell subjected to noisy sinusoidal input. The latter depends significantly on the single cell's membrane properties and can be understood in the context of the simplified exponential integrate-and-fire (EIF) neuron. We find that 200-Hz oscillations can be generated, provided the effective input conductance of single cells is large, so that the single neuron's phase shift is sufficiently small. In a two-population network of excitatory pyramidal cells and inhibitory neurons, recurrent excitation can either decrease or increase the population rhythmic frequency, depending on whether in a neuron the excitatory synaptic current follows or precedes the inhibitory synaptic current in an oscillatory cycle. Detailed single-cell properties have a substantial impact on population oscillations, even though rhythmicity does not originate from pacemaker neurons and is an emergent network phenomenon.

Prestimulus oscillations enhance psychophysical performance in humans

The presence of various ongoing oscillations in the brain is correlated with behavioral states such as restful wakefulness or drowsiness. However, even when subjects aim to maintain a high level of vigilance, ongoing oscillations exhibit large amplitude variability on time scales of hundreds of milliseconds to seconds, suggesting that the functional state of local cortical networks is continuously changing. How this volatility of ongoing oscillations influences the perception of sensory stimuli has remained essentially unknown. We investigated the relationship between prestimulus neuronal oscillations and the subjects' ability to consciously perceive and react to somatosensory stimuli near the threshold of detection. We show that, for prestimulus oscillations at approximately 10, 20, and 40 Hz detected over the sensorimotor cortex, intermediate amplitudes were associated with the highest probability of conscious detection and the shortest reaction times. In contrast, for 10 and 20 Hz prestimulus oscillations detected over the parietal region, the largest amplitudes were associated with the best performance. Our data indicate that the prestimulus oscillatory activity detected over sensorimotor and parietal cortices has a profound effect on the processing of weak stimuli. Furthermore, the results suggest that ongoing oscillations in sensory cortices may optimize the processing of sensory stimuli with the same mechanism as noise sources in intrinsic stochastic resonance.

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.

Phase resetting and phase locking in hybrid circuits of one model and one biological neuron

To determine why elements of central pattern generators phase lock in a particular pattern under some conditions but not others, we tested a theoretical pattern prediction method. The method is based on the tabulated open loop pulsatile interactions of bursting neurons on a cycle-by-cycle basis and was tested in closed loop hybrid circuits composed of one bursting biological neuron and one bursting model neuron coupled using the dynamic clamp. A total of 164 hybrid networks were formed by varying the synaptic conductances. The prediction of 1:1 phase locking agreed qualitatively with the experimental observations, except in three hybrid circuits in which 1:1 locking was predicted but not observed. Correct predictions sometimes required consideration of the second order phase resetting, which measures the change in the timing of the second burst after the perturbation. The method was robust to offsets between the initiation of bursting in the presynaptic neuron and the activation of the synaptic coupling with the postsynaptic neuron. The quantitative accuracy of the predictions fell within the variability (10%) in the experimentally observed intrinsic period and phase resetting curve (PRC), despite changes in the burst duration of the neurons between open and closed loop conditions.

An integrated spinal cord–hindlimbs preparation for studying the role of intrinsic properties in somatosensory information processing

Several studies performed using the slice in vitro technique have shown that spinal cord neurons display specialized intrinsic electrophysiological properties. However, the actual role of intrinsic properties in somatosensory processing remains unclear, mainly due to the impossibility to generate natural sensory inputs in spinal cord slices. Here, we show an integrated spinal cord-hindlimbs preparation of juvenile turtles that has the advantages of in vitro approaches and still enables natural stimulation. By making patch-clamp whole-cell recordings of both superficial and deep dorsal horn neurons in the integrated preparation, we found similar electrophysiological phenotypes as those observed in slices. Most of the cells responded to natural stimuli, had large receptive fields and were classified as wide-dynamic range neurons. Both low-threshold spikes and plateau potentials interacted with naturally evoked sensory inputs, generating complex dynamics. Furthermore, we found that the activity of the spinal cord network induced by natural stimulation modulated the excitability of plateau-generating cells. This effect was mimicked by bath application of cis-(+/-)-1-aminocyclopentane-1,3-dicarboxylic acid (ACPD), a group I glutamate metabotropic receptor agonist. Our results show that the spinal cord-hindlimbs preparation represents a valuable model to study the contribution of intrinsic properties to early stages of somatosensory information processing.

Electrophysiological classes of neocortical neurons

Neocortical network behavior and neocortical function emerge from synaptic interactions among neurons with specific electrophysiological and morphological characteristics. The intrinsic electrophysiological properties of neurons define their firing patterns and their input-output functions with critical consequences for their functional properties within the network. Understanding the role played by the active non-linear properties caused by ionic conductances distributed in the soma and the dendrites is a critical step towards understanding cortical function. Here I present a brief description of electrophysiological and morphological characteristics of neocortical cells that allow their classification in categories. I review some examples of differences in functional properties among different electrophysiological cell classes in the visual cortex, as well as the role played by specific ionic conductances in defining firing and accommodation properties of neocortical neurons.

Long-Term Potentiation of Intrinsic Excitability in LV Visual Cortical Neurons

Neuronal excitability has a large impact on network behavior, and plasticity in intrinsic excitability could serve as an important information storage mechanism. Here we ask whether postsynaptic excitability of layer V pyramidal neurons from primary visual cortex can be rapidly regulated by activity. Whole cell current-clamp recordings were obtained from visual cortical slices, and intrinsic excitability was measured by recording the firing response to small depolarizing test pulses. Inducing neurons to fire at high-frequency (30–40 Hz) in bursts for 5 min in the presence of synaptic blockers increased the firing rate evoked by the test pulse. This long-term potentiation of intrinsic excitability (LTP-IE) lasted for as long as we held the recording (>60 min). LTP-IE was accompanied by a leftward shift in the entire frequency versus current ( F-I) curve and a decrease in threshold current and voltage. Passive neuronal properties were unaffected by the induction protocol, indicating that LTP-IE occurred through modification in voltage-gated conductances. Reducing extracellular calcium during the induction protocol, or buffering intracellular calcium with bis-( o-aminophenoxy)- N,N,N′,N′-tetraacetic acid, prevented LTP-IE. Finally, blocking protein kinase A (PKA) activation prevented, whereas pharmacological activation of PKA both mimicked and occluded, LTP-IE. This suggests that LTP-IE occurs through postsynaptic calcium influx and subsequent activation of PKA. Activity-dependent plasticity in intrinsic excitability could greatly expand the computational power of individual neurons.

Stimulus-induced change in long-range temporal correlations and scaling behaviour of sensorimotor oscillations

The human brain spontaneously generates large-scale network oscillations at around 10 and 20 Hz. The amplitude envelope of these oscillations fluctuates intermittently and was recently reported to exhibit power-law decay of the autocorrelation for hundreds of seconds. This indicates that the underlying networks are in a dynamic state resembling the self-organized critical state known to exist in many complex systems. Based on the mechanism of how correlations emerge in these systems, we hypothesized that the physiological basis of long-range power-law correlations is the buildup of a memory of past activity by a continuous modification of the network's functional connectivity by the ongoing oscillations. In this framework, exogenous perturbations of ongoing oscillations would degrade or abolish this dynamic network memory. We investigated the sensitivity of the temporal correlations in sensorimotor 10- and 20-Hz oscillations to median nerve stimulation that is known to have immediate effects on ongoing oscillations. Our results show that the amplitude fluctuations of these oscillations were effectively modulated by the somatosensory stimuli but still exhibited long-range temporal correlations and power-law scaling behaviour. The magnitude of the temporal correlations was, however, attenuated and the power-law exponents were decreased. This implies that the stimuli indeed degraded the network's memory of its past.

A computer model of field potential responses for the study of short-term plasticity in hippocampus

Activity-dependent synaptic plasticity has important implications for network function. The previously developed model of the hippocampal CA1 area, which contained pyramidal cells (PC) and two types of interneurons involved in feed-forward and recurrent inhibition, respectively, and received synaptic inputs from CA3 neurons via the Schaffer collaterals, was enhanced by incorporating dynamic synaptic connections capable of changing their weights depending on presynaptic activation history. The model output was presented as field potentials, which were compared with those derived experimentally. The parameters of Schaffer collateral-PC excitatory model synapse were determined, with which the model successfully reproduced the complicated dynamics of train-stimulation sequential potentiation/depression observed in experimentally recorded field responses. It was found that the model better reproduces the time course of experimental field potentials if the inhibitory synapses on PC are also made dynamic, with expressed properties of frequency-dependent depression. This finding supports experimental evidence that these synapses are subject to activity-dependent depression. The model field potentials in response to various randomly generated and real (derived from recorded CA3 unit activity) long stimulating trains were calculated, illustrating that short-term plasticity with the observed characteristics could play specific roles in frequency processing in hippocampus and thus providing a new tool for the theoretical study of activity-dependent synaptic plasticity.

Electrophysiological and Morphological Characterization of Identified Motor Neurons in theDrosophilaThird Instar Larva Central Nervous System

We have used dye fills and electrophysiological recordings to identify and characterize a cluster of motor neurons in the third instar larval ventral ganglion. This cluster of neurons is similar in position to the well-studied embryonic RP neurons. Dye fills of larval dorsomedial neurons demonstrate that individual neurons within the cluster can be reproducibly identified by observing their muscle targets and bouton morphology. The terminal targets of these five neurons are body wall muscles 6/7, 1, 14, and 30 and the intersegmental nerve (ISN) terminal muscles (1, 2, 3, 4, 9, 10, 19, 20). All cells except the ISN neuron, which has a type Is ending, display type Ib boutons. Two of these neurons appear to be identical to the embryonic RP3 and aCC cells, which define the most proximal and distal innervations within a hemisegment. The targets of the other neurons in the larval dorsomedial cluster do not correspond to embryonic targets of the neurons in the RP cluster, suggesting rewiring of this circuit during early larval stages. Electrophysiological studies of the five neurons in current clamp revealed that type Is neurons have a longer delay in the appearance of the first spike compared with type Ib neurons. Genetic, biophysical, and pharmacological studies in current and voltage clamp show this delay is controlled by the kinetics and voltage sensitivity of inactivation of a current whose properties suggest that it may be the Shal IAcurrent. The combination of genetic identification and whole cell recording allows us to directly explore the cellular substrates of neural and locomotor behavior in an intact system.

Locomotor activity in spinal cord-injured persons

After a spinal cord injury (SCI) of the cat or rat, neuronal centers below the level of lesion exhibit plasticity that can be exploited by specific training paradigms. In individuals with complete or incomplete SCI, human spinal locomotor centers can be activated and modulated by locomotor training (facilitating stepping movements of the legs using body weight support on a treadmill to provide appropriate sensory cues). Individuals with incomplete SCI benefit from locomotor training such that they improve their ability to walk over ground. Load- or hip joint-related afferent input seems to be of crucial importance for both the generation of a locomotor pattern and the effectiveness of the training. However, it may be a critical combination of afferent signals that is needed to generate a locomotor pattern after severe SCI. Mobility of individuals after a SCI can be improved by taking advantage of the plasticity of the central nervous system and can be maintained with persistent locomotor activity. In the future, if regeneration approaches can successfully be applied in human SCI, even individuals with complete SCI may recover walking ability with locomotor training.

Synaptic Interactions Between Thalamic and Cortical Inputs Onto Cortical Neurons In Vivo

To study the interactions between thalamic and cortical inputs onto neocortical neurons, we used paired-pulse stimulation (PPS) of thalamic and cortical inputs as well as PPS of two cortical or two thalamic inputs that converged, at different time intervals, onto intracellularly recorded cortical and thalamocortical neurons in anesthetized cats. PPS of homosynaptic cortico-cortical pathways produced facilitation, depression, or no significant effects in cortical pathways, whereas cortical responses to thalamocortical inputs were mostly facilitated at both short and long intervals. By contrast, heterosynaptic interactions between either cortical and thalamic, or thalamic and cortical, inputs generally produced decreases in the peak amplitudes and depolarization area of evoked excitatory postsynaptic potentials (EPSPs), with maximal effect at ∼10 ms and lasting from 60 to 100 ms. All neurons tested with thalamic followed by cortical stimuli showed a decrease in the apparent input resistance ( Rin), the time course of which paralleled that of decreased responses, suggesting that shunting is the factor accounting for EPSP's decrease. Only half of neurons tested with cortical followed by thalamic stimuli displayed changes in Rin. Spike shunting in the thalamus may account for those cases in which decreased synaptic responsiveness of cortical neurons was not associated with decreased Rin because thalamocortical neurons showed decreased firing probability during cortical stimulation. These results suggest a short-lasting but strong shunting between thalamocortical and cortical inputs onto cortical neurons.

Cellular localization of Shab and Shaw potassium channels in the lobster stomatogastric ganglion

The motor pattern generated by the 14 neurons composing the pyloric circuit in the stomatogastric ganglion (STG) of the spiny lobster, Panulirus interruptus, is organized not only by the synaptic connections between neurons, but also by the characteristic intrinsic electrophysiological properties of the individual cells. These cellular properties result from the unique complement of ion channels that each cell expresses, and the distribution of those channels in the cell membranes. We have mapped the STG expression of shab and shaw, two genes in the Shaker superfamily of potassium channel genes that encode voltage-dependent, non-inactivating channels. Using antibodies developed against peptide sequences from the two channel proteins, we explored the localization and cell-specific expression of the channels. Anti-Shab and anti-Shaw antibodies both stain all the pyloric neurons in the somata, as well as their primary neurites and branch points of large neurites, but to varying degrees between cell types. Staining was weak and irregular (Shaw) or absent (Shab) in the fine neuropil of pyloric neurons, where most synaptic interactions occur. There is a high degree of variability in the staining intensity among neurons of a single cell class. This supports Golowasch et al.'s [J Neurosci 19 (1999) RC33; Neural Comput 11 (1999) 1079] hypothesis that individual cells can have similar firing properties with varying compositions of ionic currents. Both antibodies stain the axons of the peripheral nerves as they enter foregut muscles. We conclude that both Shab and Shaw channels are appropriately localized to contribute to the noninactivating potassium current in the stomatogastric nervous system.

A recurrent network model of somatosensory parametric working memory in the prefrontal cortex

A parametric working memory network stores the information of an analog stimulus in the form of persistent neural activity that is monotonically tuned to the stimulus. The family of persistent firing patterns with a continuous range of firing rates must all be realizable under exactly the same external conditions (during the delay when the transient stimulus is withdrawn). How this can be accomplished by neural mechanisms remains an unresolved question. Here we present a recurrent cortical network model of irregularly spiking neurons that was designed to simulate a somatosensory working memory experiment with behaving monkeys. Our model reproduces the observed positively and negatively monotonic persistent activity, and heterogeneous tuning curves of memory activity. We show that fine-tuning mathematically corresponds to a precise alignment of cusps in the bifurcation diagram of the network. Moreover, we show that the fine-tuned network can integrate stimulus inputs over several seconds. Assuming that such time integration occurs in neural populations downstream from a tonically persistent neural population, our model is able to account for the slow ramping-up and ramping-down behaviors of neurons observed in prefrontal cortex.

Acute changes in short-term plasticity at synapses with elevated levels of neuronal calcium sensor-1

Short-term synaptic plasticity is a defining feature of neuronal activity, but the underlying molecular mechanisms are poorly understood. Depression of synaptic activity might be due to limited vesicle availability, whereas facilitation is thought to result from elevated calcium levels. However, it is unclear whether the strength and direction (facilitation versus depression) of plasticity at a given synapse result from preexisting synaptic strength or whether they are regulated by separate mechanisms. Here we show, in rat hippocampal cell cultures, that increases in the calcium binding protein neuronal calcium sensor-1 (NCS-1) can switch paired-pulse depression to facilitation without altering basal synaptic transmission or initial neurotransmitter release probability. Facilitation persisted during high-frequency trains of stimulation, indicating that NCS-1 can recruit 'dormant' vesicles. Our results suggest that NCS-1 acts as a calcium sensor for short-term plasticity by facilitating neurotransmitter output independent of initial release. We conclude that separate mechanisms are responsible for determining basal synaptic strength and short-term plasticity.

A model synapse that incorporates the properties of short- and long-term synaptic plasticity

We propose a general computer model of a synapse, which incorporates mechanisms responsible for the realization of both short- and long-term synaptic plasticity-the two forms of experimentally observed plasticity that seem to be very significant for the performance of neuronal networks. The model consists of a presynaptic part based on the earlier 'double barrier synapse' model, and a postsynaptic compartment which is connected to the presynaptic terminal via a feedback, the sign and magnitude of which depend on postsynaptic Ca(2+) concentration. The feedback increases or decreases the amount of neurotransmitter which is in a ready for release state. The model adequately reproduced the phenomena of short- and long-term plasticity observed experimentally in hippocampal slices for CA3-CA1 synapses. The proposed model may be used in the investigation of certain real synapses to estimate their physiological parameters, and in the construction of realistic neuronal networks.