The Influences of I h on Temporal Summation in Hippocampal CA1 Pyramidal Neurons: A Modeling Study

Recent experimental and theoretical studies have found that active dendritic ionic currents can compensate for the effects of electrotonic attenuation. In particular, temporal summation, the percentage increase in peak somatic voltage responses invoked by a synaptic input train, is independent of location of the synaptic input in hippocampal CA1 pyramidal neurons under normal conditions. This independence, known as normalization of temporal summation, is destroyed when the hyperpolarization-activated current, I _h, is blocked [Magee JC (1999a), Nature Neurosci. 2: 508–514]. Using a compartmental model derived from morphological recordings of hippocampal CA1 pyramidal neurons, we examined the hypothesis that I _h was primarily responsible for normalization of temporal summation. We concluded that this hypothesis was incomplete. With a model that included I _h, the persistent Na⁺ current (I _NaP), and the transient A-type K⁺ current (I _A), however, we observed normalization of temporal summation across a wide range of synaptic input frequencies, in keeping with experimental observations.     

Differential regulation of glycinergic and GABAergic nanocolumns at mixed inhibitory synapses

Super‐resolution imaging has revealed that key synaptic proteins are dynamically organized within sub‐synaptic domains (SSDs). To examine how different inhibitory receptors are regulated, we carried out dual‐color direct stochastic optical reconstruction microscopy (dSTORM) of GlyRs and GABA_ARs at mixed inhibitory synapses in spinal cord neurons. We show that endogenous GlyRs and GABA_ARs as well as their common scaffold protein gephyrin form SSDs that align with pre‐synaptic RIM1/2, thus creating trans‐synaptic nanocolumns. Strikingly, GlyRs and GABA_ARs occupy different sub‐synaptic spaces, exhibiting only a partial overlap at mixed inhibitory synapses. When network activity is increased by 4‐aminopyridine treatment, the GABA_AR copy numbers and the number of GABA_AR SSDs are reduced, while GlyRs remain largely unchanged. This differential regulation is likely the result of changes in gephyrin phosphorylation that preferentially occurs outside of SSDs. The activity‐dependent regulation of GABA_ARs versus GlyRs suggests that different signaling pathways control the receptors'' sub‐synaptic clustering. Taken together, our data reinforce the notion that the precise sub‐synaptic organization of GlyRs, GABA_ARs, and gephyrin has functional consequences for the plasticity of mixed inhibitory synapses.     

Scn1a missense mutation impairs GABAA receptor-mediated synaptic transmission in the rat hippocampus

Mutations of the Na_v1.1 channel subunit SCN1A have been implicated in the pathogenesis of human febrile seizures (FS). We have recently developed hyperthermia-induced seizure-susceptible (Hiss) rat, a novel rat model of FS, which carries a missense mutation (N1417H) in Scn1a[1]. Here, we conducted electrophysiological studies to clarify the influences of the Scn1a mutation on the hippocampal synaptic transmission, specifically focusing on the GABAergic system. Hippocampal slices were prepared from Hiss or F344 (control) rats and maintained in artificial cerebrospinal fluid saturated with 95% O₂ and 5% CO₂in vitro. Single neuron activity was recorded from CA1 pyramidal neurons and their responses to the test (unconditioned) or paired pulse (PP) stimulation of the Schaffer collateral/commissural fibers were evaluated. Hiss rats were first tested for pentylenetetrazole-induced seizures and confirmed to show high seizure susceptibility to the blockade of GAGA_A receptors. The Scn1a mutation in Hiss rats did not directly affect spike generation (i.e., number of evoked spikes and firing threshold) of the CA1 pyramidal neurons elicited by the Schaffer collateral/commissural stimulation. However, GABA_A receptor-mediated inhibition of pyramidal neurons by the PP stimulation was significantly disrupted in Hiss rats, yielding a significant increase in the number of PP-induced firings at PP intervals of 32-256 ms. The present study shows that the Scn1a missense mutation preferentially impairs GABA_A receptor-mediated synaptic transmission without directly altering the excitability of the pyramidal neurons in the hippocampus, which may be linked to the pathogenesis of FS.     

Distinguishing Between GABAA Receptors Responsible for Tonic and Phasic Conductances

Cell-to-cell communication in the mammalian nervous system does not solely involve direct synaptic transmission. There is considerable evidence for a type of communication between neurons through chemical means that lies somewhere between the rapid synaptic information transfer and the relatively non-specific neuroendocrine secretion. Here I review some of the experimental evidence accumulated for the GABA system indicating that GABA_A receptor-gated Cl-channels localized at synapses differ significantly from those found extrasynaptically. These two types of GABA_A receptor are involved in generating distinctly different conductances. Thus, the development and search for pharmacological agents specifically aimed at selectively altering synaptic and extrasynaptic GABA_A conductances is within reach, and is expected to provide novel insights into the regulation of neuronal excitability.     

Population rate coding in recurrent neuronal networks with unreliable synapses

Neuron transmits spikes to postsynaptic neurons through synapses. Experimental observations indicated that the communication between neurons is unreliable. However most modelling and computational studies considered deterministic synaptic interaction model. In this paper, we investigate the population rate coding in an all-to-all coupled recurrent neuronal network consisting of both excitatory and inhibitory neurons connected with unreliable synapses. We use a stochastic on-off process to model the unreliable synaptic transmission. We find that synapses with suitable successful transmission probability can enhance the encoding performance in the case of weak noise; while in the case of strong noise, the synaptic interactions reduce the encoding performance. We also show that several important synaptic parameters, such as the excitatory synaptic strength, the relative strength of inhibitory and excitatory synapses, as well as the synaptic time constant, have significant effects on the performance of the population rate coding. Further simulations indicate that the encoding dynamics of our considered network cannot be simply determined by the average amount of received neurotransmitter for each neuron in a time instant. Moreover, we compare our results with those obtained in the corresponding random neuronal networks. Our numerical results demonstrate that the network randomness has the similar qualitative effect as the synaptic unreliability but not completely equivalent in quantity.     

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

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

Computational modeling of GABA<Subscript>A</Subscript> receptor-mediated paired-pulse inhibition in the dentate gyrus

Paired-pulse inhibition (PPI) of the population spike observed in extracellular field recordings is widely used as a read-out of hippocampal network inhibition. PPI reflects GABA_A receptor-mediated inhibition of principal neurons through local interneurons. However, because of its polysynaptic nature, it is difficult to assign PPI changes to precise synaptic mechanisms. Here we used a detailed network model of the dentate gyrus to simulate PPI of granule cell action potentials and analyze its network properties. Our computational analysis indicates that PPI results mainly from a combination of perisomatic feed-forward and feedback inhibition of granule cells by basket cells. Feed-forward inhibition mediated by basket cells appeared to be the most significant source of PPI. Our simulations suggest that PPI depends more on somatic than on dendritic inhibition of granule cells. Furthermore, PPI was modulated by changes in GABA_A reversal potential (E_GABA) and by alterations in intrinsic excitability of granule cells. In summary, computer modeling provides a useful tool for determining the role of synaptic and intrinsic cellular mechanisms in paired-pulse field potential responses.     

Shaping Neuronal Network Activity by Presynaptic Mechanisms

Neuronal microcircuits generate oscillatory activity, which has been linked to basic functions such as sleep, learning and sensorimotor gating. Although synaptic release processes are well known for their ability to shape the interaction between neurons in microcircuits, most computational models do not simulate the synaptic transmission process directly and hence cannot explain how changes in synaptic parameters alter neuronal network activity. In this paper, we present a novel neuronal network model that incorporates presynaptic release mechanisms, such as vesicle pool dynamics and calcium-dependent release probability, to model the spontaneous activity of neuronal networks. The model, which is based on modified leaky integrate-and-fire neurons, generates spontaneous network activity patterns, which are similar to experimental data and robust under changes in the model''s primary gain parameters such as excitatory postsynaptic potential and connectivity ratio. Furthermore, it reliably recreates experimental findings and provides mechanistic explanations for data obtained from microelectrode array recordings, such as network burst termination and the effects of pharmacological and genetic manipulations. The model demonstrates how elevated asynchronous release, but not spontaneous release, synchronizes neuronal network activity and reveals that asynchronous release enhances utilization of the recycling vesicle pool to induce the network effect. The model further predicts a positive correlation between vesicle priming at the single-neuron level and burst frequency at the network level; this prediction is supported by experimental findings. Thus, the model is utilized to reveal how synaptic release processes at the neuronal level govern activity patterns and synchronization at the network level.     

A neural network model of reliably optimized spike transmission

We studied the detailed structure of a neuronal network model in which the spontaneous spike activity is correctly optimized to match the experimental data and discuss the reliability of the optimized spike transmission. Two stochastic properties of the spontaneous activity were calculated: the spike-count rate and synchrony size. The synchrony size, expected to be an important factor for optimization of spike transmission in the network, represents a percentage of observed coactive neurons within a time bin, whose probability approximately follows a power-law. We systematically investigated how these stochastic properties could matched to those calculated from the experimental data in terms of the log-normally distributed synaptic weights between excitatory and inhibitory neurons and synaptic background activity induced by the input current noise in the network model. To ensure reliably optimized spike transmission, the synchrony size as well as spike-count rate were simultaneously optimized. This required changeably balanced log-normal distributions of synaptic weights between excitatory and inhibitory neurons and appropriately amplified synaptic background activity. Our results suggested that the inhibitory neurons with a hub-like structure driven by intensive feedback from excitatory neurons were a key factor in the simultaneous optimization of the spike-count rate and synchrony size, regardless of different spiking types between excitatory and inhibitory neurons.     

Synaptic Potentiation Facilitates Memory-like Attractor Dynamics in Cultured In Vitro Hippocampal Networks

Collective rhythmic dynamics from neurons is vital for cognitive functions such as memory formation but how neurons self-organize to produce such activity is not well understood. Attractor-based computational models have been successfully implemented as a theoretical framework for memory storage in networks of neurons. Additionally, activity-dependent modification of synaptic transmission is thought to be the physiological basis of learning and memory. The goal of this study is to demonstrate that using a pharmacological treatment that has been shown to increase synaptic strength within in vitro networks of hippocampal neurons follows the dynamical postulates theorized by attractor models. We use a grid of extracellular electrodes to study changes in network activity after this perturbation and show that there is a persistent increase in overall spiking and bursting activity after treatment. This increase in activity appears to recruit more “errant” spikes into bursts. Phase plots indicate a conserved activity pattern suggesting that a synaptic potentiation perturbation to the attractor leaves it unchanged. Lastly, we construct a computational model to demonstrate that these synaptic perturbations can account for the dynamical changes seen within the network.     

Regulation of GABAergic inhibition by serotonin signaling in prefrontal cortex: molecular mechanisms and functional implications

Serotonergic neurotransmission in prefrontal cortex (PFC) plays a key role in regulating emotion and cognition under normal and pathological conditios. Increasing evidence suggests that serotonin receptors are involved in the complex regulation of GABAergic inhibitory transmission in PFC. Activation of postsynaptic 5-HT₂ receptors in PFC pyramidal neurons inhibits GABA_A-receptor currents via phosphorylation of GABA_A receptor γ2 subunits by RACK1-anchored PKC. In contrast, activation of postsynaptic 5-HT₄ receptors produces an activity-dependent bi-directional regulation of GABA-evoked currents in PFC pyramidal neurons, which is mediated through phosphorylation of GABA_A-receptor β subunits by anchored PKA. On the presynaptic side, GABAergic inhibition is regulated by 5-HT through the activation of 5-HT₂, 5-HT₁, and 5-HT₃ receptors on GABAergic intereneurons. These data provide a molecular and cellular mechanism for serotonin to dynamically regulate synaptic transmission and neuronal excitability in the PFC network, which may underlie the actions of many antidepressant and antipsychotic drugs.     

Tonic inhibition in spinal ventral horn interneurons mediated by α5 subunit-containing GABA(A) receptors

GABA_A receptors mediate synaptic and tonic inhibition in many neurons of the central nervous system. These receptors can be constructed from a range of different subunits deriving from seven identified families. Among these subunits, α₅ has been shown to mediate GABAergic tonic inhibitory currents in neurons from supraspinal nuclei. Likewise, immunohistochemical and in situ hybridization studies have shown the presence of the α₅ subunit in spinal cord neurons, though almost nothing is known about its function. In the present report, using slices of the adult turtle spinal cord as a model system we have recorded a tonic inhibitory current in ventral horn interneurons (VHIs) and determined the functional contribution of the α₅ subunit-containing GABA_A receptors to this current. Patch clamp studies show that the GABAergic tonic inhibitory current in VHIs is not affected by the application of antagonists of the α_4/6 subunit-containing GABA_A receptors, but is sensitive to L-655708, an antagonist of the GABA_A receptors containing α₅ subunits. Last, by using RT-PCR and immunohistochemistry we confirmed the expression of the α₅ subunit in the turtle spinal cord. Together, these results suggest that GABA_A receptors containing the α₅ subunit mediate the tonic inhibitory currents observed in VHIs.     

A hemicord locomotor network of excitatory interneurons: a simulation study

Locomotor burst generation is simulated using a full-scale network model of the unilateral excitatory interneuronal population. Earlier small-scale models predicted that a population of excitatory neurons would be sufficient to produce burst activity, and this has recently been experimentally confirmed. Here we simulate the hemicord activity induced under various experimental conditions, including pharmacological activation by NMDA and AMPA as well as electrical stimulation. The model network comprises a realistic number of cells and synaptic connectivity patterns. Using similar distributions of cellular and synaptic parameters, as have been estimated experimentally, a large variation in dynamic characteristics like firing rates, burst, and cycle durations were seen in single cells. On the network level an overall rhythm was generated because the synaptic interactions cause partial synchronization within the population. This network rhythm not only emerged despite the distributed cellular parameters but relied on this variability, in particular, in reproducing variations of the activity during the cycle and showing recruitment in interneuronal populations. A slow rhythm (0.4–2 Hz) can be induced by tonic activation of NMDA-sensitive channels, which are voltage dependent and generate depolarizing plateaus. The rhythm emerges through a synchronization of bursts of the individual neurons. A fast rhythm (4–12 Hz), induced by AMPA, relies on spike synchronization within the population, and each burst is composed of single spikes produced by different neurons. The dynamic range of the fast rhythm is limited by the ability of the network to synchronize oscillations and depends on the strength of synaptic connections and the duration of the slow after hyperpolarization. The model network also produces prolonged bouts of rhythmic activity in response to brief electrical activations, as seen experimentally. The mutual excitation can sustain long-lasting activity for a realistic set of synaptic parameters. The bout duration depends on the strength of excitatory synaptic connections, the level of persistent depolarization, and the influx of Ca²⁺ ions and activation of Ca²⁺-dependent K⁺ current.     

Robust Short-Term Memory without Synaptic Learning

Short-term memory in the brain cannot in general be explained the way long-term memory can – as a gradual modification of synaptic weights – since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds). The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings.     

Dynamical estimation of neuron and network properties III: network analysis using neuron spike times

Estimating the behavior of a network of neurons requires accurate models of the individual neurons along with accurate characterizations of the connections among them. Whereas for a single cell, measurements of the intracellular voltage are technically feasible and sufficient to characterize a useful model of its behavior, making sufficient numbers of simultaneous intracellular measurements to characterize even small networks is infeasible. This paper builds on prior work on single neurons to explore whether knowledge of the time of spiking of neurons in a network, once the nodes (neurons) have been characterized biophysically, can provide enough information to usefully constrain the functional architecture of the network: the existence of synaptic links among neurons and their strength. Using standardized voltage and synaptic gating variable waveforms associated with a spike, we demonstrate that the functional architecture of a small network of model neurons can be established.     

Modelling Feedback Excitation,Pacemaker Properties and Sensory Switching of Electrically Coupled Brainstem Neurons Controlling Rhythmic Activity

What cellular and network properties allow reliable neuronal rhythm generation or firing that can be started and stopped by brief synaptic inputs? We investigate rhythmic activity in an electrically-coupled population of brainstem neurons driving swimming locomotion in young frog tadpoles, and how activity is switched on and off by brief sensory stimulation. We build a computational model of 30 electrically-coupled conditional pacemaker neurons on one side of the tadpole hindbrain and spinal cord. Based on experimental estimates for neuron properties, population sizes, synapse strengths and connections, we show that: long-lasting, mutual, glutamatergic excitation between the neurons allows the network to sustain rhythmic pacemaker firing at swimming frequencies following brief synaptic excitation; activity persists but rhythm breaks down without electrical coupling; NMDA voltage-dependency doubles the range of synaptic feedback strengths generating sustained rhythm. The network can be switched on and off at short latency by brief synaptic excitation and inhibition. We demonstrate that a population of generic Hodgkin-Huxley type neurons coupled by glutamatergic excitatory feedback can generate sustained asynchronous firing switched on and off synaptically. We conclude that networks of neurons with NMDAR mediated feedback excitation can generate self-sustained activity following brief synaptic excitation. The frequency of activity is limited by the kinetics of the neuron membrane channels and can be stopped by brief inhibitory input. Network activity can be rhythmic at lower frequencies if the neurons are electrically coupled. Our key finding is that excitatory synaptic feedback within a population of neurons can produce switchable, stable, sustained firing without synaptic inhibition.     

Differences in biophysical properties of nucleus accumbens medium spiny neurons emerging from inactivation of inward rectifying potassium currents

Inward rectifying potassium (K_IR) currents in medium spiny (MS) neurons of nucleus accumbens inactivate significantly in ~40% of the neurons but not in the rest, which may lead to differences in input processing by these two groups. Using a 189-compartment computational model of the MS neuron, we investigate the influence of this property using injected current as well as spatiotemporally distributed synaptic inputs. Our study demonstrates that K_IR current inactivation facilitates depolarization, firing frequency and firing onset in these neurons. These effects may be attributed to the higher input resistance of the cell as well as a more depolarized resting/down-state potential induced by the inactivation of this current. In view of the reports that dendritic intracellular calcium levels depend closely on burst strength and spike onset time, our findings suggest that inactivation of K_IR currents may offer a means of modulating both excitability and synaptic plasticity in MS neurons.     

Variability v.s. synchronicity of neuronal activity in local cortical network models with different wiring topologies

Dynamical behavior of a biological neuronal network depends significantly on the spatial pattern of synaptic connections among neurons. While neuronal network dynamics has extensively been studied with simple wiring patterns, such as all-to-all or random synaptic connections, not much is known about the activity of networks with more complicated wiring topologies. Here, we examined how different wiring topologies may influence the response properties of neuronal networks, paying attention to irregular spike firing, which is known as a characteristic of in vivo cortical neurons, and spike synchronicity. We constructed a recurrent network model of realistic neurons and systematically rewired the recurrent synapses to change the network topology, from a localized regular and a “small-world” network topology to a distributed random network topology. Regular and small-world wiring patterns greatly increased the irregularity or the coefficient of variation (Cv) of output spike trains, whereas such an increase was small in random connectivity patterns. For given strength of recurrent synapses, the firing irregularity exhibited monotonous decreases from the regular to the random network topology. By contrast, the spike coherence between an arbitrary neuron pair exhibited a non-monotonous dependence on the topological wiring pattern. More precisely, the wiring pattern to maximize the spike coherence varied with the strength of recurrent synapses. In a certain range of the synaptic strength, the spike coherence was maximal in the small-world network topology, and the long-range connections introduced in this wiring changed the dependence of spike synchrony on the synaptic strength moderately. However, the effects of this network topology were not really special in other properties of network activity. Action Editor: Xiao-Jing Wang     

Synchronization without oscillatory neurons

Experimental results suggest that neurons in the cortex synchronize their action potentials on the millisecond time scale. More importantly this binding expresses functional relationships between the neurons. A model of neuronal interactions is proposed in which simultaneous discharges of neurons develop through specialized synaptic circuits. As an important prerequisite for this synchronization it is demonstrated that SynFire chains, generating different levels of excitation, propagate their activity waves at distinct velocities. Two chains were coupled by excitatory synapses and their activity was initiated at different times. Due to synaptic interactions, activity in the earlier-initiated chain accelerates propagation in the other chain until the two activity waves are synchronized. Compared with several neural network models with oscillatory units, physiologically more plausible neurons are simulated. It is still under debate whether neurons in the cortex show oscillatory dischargesper se. In particular, a high rate of noise relative to very weak synaptic gains cannot impair our results in the neural network simulations.     

Mechanisms for oscillation and frequency control in reciprocally inhibitory model neural networks

We describe four different mechanisms that lead to oscillations in a network of two reciprocally inhibitory cells. In two cases (intrinsic release and intrinsic escape) the frequency of the network oscillation is insensitive to the threshold voltage of the synaptic potentials. In the other two cases (synaptic release and synaptic escape) the network frequency is strongly determined by the threshold voltage of the synaptic connections. The distinction between the different mechanisms blurs as the function describing synaptic activation becomes less steep and as the model neurons are removed from the relaxation regime. These mechanisms provide insight into the parameters that control network frequency in motor systems that depend on reciprocal inhibition.