Models that contain the Model Concept : Synchronization

(If two or more cells repetitively fire spikes within a few (or less) milliseconds they are considered synchronized.)
Re-display model names without descriptions
    Models   Description
1. 3D model of the olfactory bulb (Migliore et al. 2014)
This entry contains a link to a full HD version of movie 1 and the NEURON code of the paper: "Distributed organization of a brain microcircuit analysed by three-dimensional modeling: the olfactory bulb" by M Migliore, F Cavarretta, ML Hines, and GM Shepherd.
2. A model for how correlation depends on the neuronal excitability type (Hong et al. 2012)
“ … Using simulations and experiments in rat hippocampal neurons, we show here that pairs of neurons receiving correlated input also exhibit correlations arising from precise spike-time synchronization. Contrary to rate comodulation, spike-time synchronization is unaffected by firing rate, thus enabling synchrony- and rate-based coding to operate independently. The type of output correlation depends on whether intrinsic neuron properties promote integration or coincidence detection: “ideal” integrators (with spike generation sensitive to stimulus mean) exhibit rate comodulation, whereas ideal coincidence detectors (with spike generation sensitive to stimulus variance) exhibit precise spike-time synchronization. … Our results explain how different types of correlations arise based on how individual neurons generate spikes, and why spike-time synchronization and rate comodulation can encode different stimulus properties. Our results also highlight the importance of neuronal properties for population-level coding insofar as neural networks can employ different coding schemes depending on the dominant operating mode of their constituent neurons. “
3. A network of AOB mitral cells that produces infra-slow bursting (Zylbertal et al. 2017)
Infra-slow rhythmic neuronal activity with very long (> 10 s) period duration was described in many brain areas but little is known about the role of this activity and the mechanisms that produce it. Here we combine experimental and computational methods to show that synchronous infra-slow bursting activity in mitral cells of the mouse accessory olfactory bulb (AOB) emerges from interplay between intracellular dynamics and network connectivity. In this novel mechanism, slow intracellular Na+ dynamics endow AOB mitral cells with a weak tendency to burst, which is further enhanced and stabilized by chemical and electrical synapses between them. Combined with the unique topology of the AOB network, infra-slow bursting enables integration and binding of multiple chemosensory stimuli over prolonged time scale. The example protocol simulates a two-glomeruli network with a single shared cell. Although each glomerulus is stimulated at a different time point, the activity of the entire population becomes synchronous (see paper Fig. 8)
4. ACh modulation in olfactory bulb and piriform cortex (de Almeida et al. 2013;Devore S, et al. 2014)
This matlab code was used in the papers de Almeida, Idiart and Linster, (2013), Devore S, de Almeida L, Linster C (2014) . This work uses a computational model of the OB and PC and their common cholinergic inputs to investigate how bulbar cholinergic modulation affects cortical odor processing.
5. CA1 pyramidal neuron: effects of Ih on distal inputs (Migliore et al 2004)
NEURON mod files from the paper: M. Migliore, L. Messineo, M. Ferrante Dendritic Ih selectively blocks temporal summation of unsynchronized distal inputs in CA1 pyramidal neurons, J.Comput. Neurosci. 16:5-13 (2004). The model demonstrates how the dendritic Ih in pyramidal neurons could selectively suppress AP generation for a volley of excitatory afferents when they are asynchronously and distally activated.
6. Cerebellar cortex oscil. robustness from Golgi cell gap jncs (Simoes de Souza and De Schutter 2011)
" ... Previous one-dimensional network modeling of the cerebellar granular layer has been successfully linked with a range of cerebellar cortex oscillations observed in vivo. However, the recent discovery of gap junctions between Golgi cells (GoCs), which may cause oscillations by themselves, has raised the question of how gap-junction coupling affects GoC and granular-layer oscillations. To investigate this question, we developed a novel two-dimensional computational model of the GoC-granule cell (GC) circuit with and without gap junctions between GoCs. ..."
7. Cerebellar granular layer (Maex and De Schutter 1998)
Circuit model of the granular layer representing a one-dimensional array of single-compartmental granule cells (grcs) and Golgi cells (Gocs). This paper examines the effects of feedback inhibition (grc -> Goc -> grc) versus feedforward inhibition (mossy fibre -> Goc -> grc) on synchronization and oscillatory behaviour.
8. Changes of ionic concentrations during seizure transitions (Gentiletti et al. 2016)
"... In order to investigate the respective roles of synaptic interactions and nonsynaptic mechanisms in seizure transitions, we developed a computational model of hippocampal cells, involving the extracellular space, realistic dynamics of Na+, K+, Ca2+ and Cl - ions, glial uptake and extracellular diffusion mechanisms. We show that the network behavior with fixed ionic concentrations may be quite different from the neurons’ behavior when more detailed modeling of ionic dynamics is included. In particular, we show that in the extended model strong discharge of inhibitory interneurons may result in long lasting accumulation of extracellular K+, which sustains the depolarization of the principal cells and causes their pathological discharges. ..."
9. Computing with neural synchrony (Brette 2012)
"... In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. ..."
10. Control of vibrissa motoneuron firing (Harish and Golomb 2010)
We construct and analyze a single-compartment, conductance-based model of vibrissa motoneurons. Low firing rates are supported in extended regimes by adaptation currents and the minimal firing rate decreases with the persistent sodium conductance gNaP and increases with M-potassium and h-cation conductances. Suprathreshold resonance results from the locking properties of vMN firing to stimuli and from reduction of firing rates at low frequencies by slow M and afterhyperpolarization potassium conductances. h conductance only slightly affects the suprathreshold resonance. When a vMN is subjected to a small periodic CPG input, serotonergically induced gNaP elevation may transfer the system from quiescence to a firing state that is highly locked to the CPG input.
11. Dentate Gyrus Feed-forward inhibition (Ferrante et al. 2009)
In this paper, the model was used to show how that FFI can change a steeply sigmoidal input-output (I/O) curve into a double-sigmoid typical of buffer systems.
12. Different roles for inhibition in the rhythm-generating respiratory network (Harris et al 2017)
"Unraveling the interplay of excitation and inhibition within rhythm-generating networks remains a fundamental issue in neuroscience. We use a biophysical model to investigate the different roles of local and long-range inhibition in the respiratory network, a key component of which is the pre-Bötzinger complex inspiratory microcircuit. ..."
13. Dynamical model of olfactory bulb mitral cell (Rubin, Cleland 2006)
This four-compartment mitral cell exhibits endogenous subthreshold oscillations, phase resetting, and evoked spike phasing properties as described in electrophysiological studies of mitral cells. It is derived from the prior work of Davison et al (2000) and Bhalla and Bower (1993). See readme.txt for details.
14. Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011)
"Coordination of neocortical oscillations has been hypothesized to underlie the "binding" essential to cognitive function. However, the mechanisms that generate neocortical oscillations in physiological frequency bands remain unknown. We hypothesized that interlaminar relations in neocortex would provide multiple intermediate loops that would play particular roles in generating oscillations, adding different dynamics to the network. We simulated networks from sensory neocortex using 9 columns of event-driven rule-based neurons wired according to anatomical data and driven with random white-noise synaptic inputs. ..."
15. Failure of Deep Brain Stimulation in a basal ganglia neuronal network model (Dovzhenok et al. 2013)
"… Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS). ... This study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients. …" Implemented by Andrey Dovzhenok, to whom questions should be addressed.
16. Fast oscillations in inhibitory networks (Maex, De Schutter 2003)
We observed a new phenomenon of resonant synchronization in computer-simulated networks of inhibitory neurons in which the synaptic current has a delayed onset, reflecting finite spike propagation and synaptic transmission times. At the resonant level of network excitation, all neurons fire synchronously and rhythmically with a period approximately four times the mean delay of the onset of the inhibitory synaptic current. ... By varying the axonal delay of the inhibitory connections, networks with a realistic synaptic kinetics can be tuned to frequencies from 40 to >200 Hz. ... We conclude that the delay of the synaptic current is the primary parameter controlling the oscillation frequency of inhibitory networks and propose that delay-induced synchronization is a mechanism for fast brain rhythms that depend on intact inhibitory synaptic transmission.
17. Gamma oscillations in hippocampal interneuron networks (Bartos et al 2002)
To examine whether an interneuron network with fast inhibitory synapses can act as a gamma frequency oscillator, we developed an interneuron network model based on experimentally determined properties. In comparison to previous interneuron network models, our model was able to generate oscillatory activity with higher coherence over a broad range of frequencies (20-110 Hz). In this model, high coherence and flexibility in frequency control emerge from the combination of synaptic properties, network structure, and electrical coupling.
18. Gamma oscillations in hippocampal interneuron networks (Wang, Buzsaki 1996)
The authors investigated the hypothesis that 20-80Hz neuronal (gamma) oscillations can emerge in sparsely connected network models of GABAergic fast-spiking interneurons. They explore model NN synchronization and compare their results to anatomical and electrophysiological data from hippocampal fast spiking interneurons.
19. Gap junction coupled network of striatal fast spiking interneurons (Hjorth et al. 2009)
Gap junctions between striatal FS neurons has very weak ability to synchronise spiking. Input uncorrelated between neighbouring neurons is shunted, while correlated input is not.
20. Gating of steering signals through phasic modulation of reticulospinal neurons (Kozlov et al. 2014)
" ... 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. ..."
21. GP Neuron, somatic and dendritic phase response curves (Schultheiss et al. 2011)
Phase response analysis of a GP neuron model showing type I PRCs for somatic inputs and type II PRCs for dendritic excitation. Analysis of intrinsic currents underlying type II dendritic PRCs.
22. Grid cell oscillatory interference with noisy network oscillators (Zilli and Hasselmo 2010)
To examine whether an oscillatory interference model of grid cell activity could work if the oscillators were noisy neurons, we implemented these simulations. Here the oscillators are networks (either synaptically- or gap-junction--coupled) of one or more noisy neurons (either Izhikevich's simple model or a Hodgkin-Huxley--type biophysical model) which drive a postsynaptic cell (which may be integrate-and-fire, resonate-and-fire, or the simple model) which should fire spatially as a grid cell if the simulation is successful.
23. Homosynaptic plasticity in the tail withdrawal circuit (TWC) of Aplysia (Baxter and Byrne 2006)
The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit respond preferentially to long-duration, low-frequency inputs.
24. Hopfield and Brody model (Hopfield, Brody 2000)
NEURON implementation of the Hopfield and Brody model from the papers: JJ Hopfield and CD Brody (2000) JJ Hopfield and CD Brody (2001). Instructions are provided in the below readme.txt file.
25. Hopfield and Brody model (Hopfield, Brody 2000) (NEURON+python)
Demonstration of Hopfield-Brody snychronization using artificial cells in NEURON+python.
26. Ketamine disrupts theta modulation of gamma in a computer model of hippocampus (Neymotin et al 2011)
"Abnormalities in oscillations have been suggested to play a role in schizophrenia. We studied theta-modulated gamma oscillations in a computer model of hippocampal CA3 in vivo with and without simulated application of ketamine, an NMDA receptor antagonist and psychotomimetic. Networks of 1200 multi-compartment neurons (pyramidal, basket and oriens-lacunosum moleculare, OLM, cells) generated theta and gamma oscillations from intrinsic network dynamics: basket cells primarily generated gamma and amplified theta, while OLM cells strongly contributed to theta. ..."
27. Large scale model of the olfactory bulb (Yu et al., 2013)
The readme file currently contains links to the results for all the 72 odors investigated in the paper, and the movie showing the network activity during learning of odor k3-3 (an aliphatic ketone).
28. Lateral dendrodenditic inhibition in the Olfactory Bulb (David et al. 2008)
Mitral cells, the principal output neurons of the olfactory bulb, receive direct synaptic activation from primary sensory neurons. Shunting inhibitory inputs delivered by granule cell interneurons onto mitral cell lateral dendrites are believed to influence spike timing and underlie coordinated field potential oscillations. Lateral dendritic shunt conductances delayed spiking to a degree dependent on both their electrotonic distance and phase of onset. Recurrent inhibition significantly narrowed the distribution of mitral cell spike times, illustrating a tendency towards coordinated synchronous activity. This result suggests an essential role for early mechanisms of temporal coordination in olfaction. The model was adapted from Davison et al, 2003, but include additional noise mechanisms, long lateral dendrite, and specific synaptic point processes.
29. Loss of phase-locking in non-weakly coupled inhib. networks of type-I neurons (Oh and Matveev 2009)
... Here we examine the loss of synchrony caused by an increase in inhibitory coupling in networks of type-I Morris–Lecar model oscillators, which is characterized by a period-doubling cascade and leads to mode-locked states with alternation in the firing order of the two cells, as reported recently by Maran and Canavier (J Comput Nerosci, 2008) for a network of Wang-Buzsáki model neurons. Although alternating-order firing has been previously reported as a near-synchronous state, we show that the stable phase difference between the spikes of the two Morris–Lecar cells can constitute as much as 70% of the unperturbed oscillation period. Further, we examine the generality of this phenomenon for a class of type-I oscillators that are close to their excitation thresholds, and provide an intuitive geometric description of such “leap-frog” dynamics. ..."
30. Microsaccades and synchrony coding in the retina (Masquelier et al. 2016)
We show that microsaccades (MS) enable efficient synchrony-based coding among the primate retinal ganglion cells (RGC). We find that each MS causes certain RGCs to fire synchronously, namely those whose receptive fields contain contrast edges after the MS. The emitted synchronous spike volley thus rapidly transmits the most salient edges of the stimulus. We demonstrate that the readout could be done rapidly by simple coincidence-detector neurons, and that the required connectivity could emerge spontaneously with spike timing-dependent plasticity.
31. Model of neural responses to amplitude-modulated tones (Nelson and Carney 2004)
"A phenomenological model with time-varying excitation and inhibition was developed to study possible neural mechanisms underlying changes in the representation of temporal envelopes along the auditory pathway. A modified version of an existing auditory-nerve model (Zhang et al., J. Acoust. Soc. Am. 109, 648–670 (2001) was used to provide inputs to higher hypothetical processing centers. Model responses were compared directly to published physiological data at three levels: the auditory nerve, ventral cochlear nucleus, and inferior colliculus. ..."
32. Model of the cerebellar granular network (Sudhakar et al 2017)
33. Modeling the effects of dopamine on network synchronization (Komek et al. 2012)
Dopamine modulates cortical circuit activity in part through its actions on GABAergic interneurons, including increasing the excitability of fast-spiking interneurons. Though such effects have been demonstrated in single cells, there are no studies that examine how such mechanisms may lead to the effects of dopamine at a neural network level. In this study, we investigated the effects of dopamine on synchronization in two simulated neural networks; one biophysical model composed of Wang-Buzsaki neurons and a reduced model with theta neurons. In both models, we show that parametrically varying the levels of dopamine, modeled through the changes in the excitability of interneurons, reveals an inverted-U shaped relationship, with low gamma band power at both low and high dopamine levels and optimal synchronization at intermediate levels. Moreover, such a relationship holds when the external input is both tonic and periodic at gamma band range. Together, our results indicate that dopamine can modulate cortical gamma band synchrony in an inverted-U fashion and that the physiologic effects of dopamine on single fast-spiking interneurons can give rise to such non-monotonic effects at the network level.
34. Network bursts in cultured NN result from different adaptive mechanisms (Masquelier & Deco 2013)
It is now well established that cultured neuron networks are spontaneously active, and tend to synchronize. Synchronous events typically involve the whole network, and have thus been termed “network spikes” (NS). Using experimental recordings and numerical simulations, we show here that the inter-NS interval statistics are complex, and allow inferring the neural mechanisms at work, in particular the adaptive ones, and estimating a number of parameters to which we cannot access experimentally.
35. Network model of the granular layer of the cerebellar cortex (Maex, De Schutter 1998)
We computed the steady-state activity of a large-scale model of the granular layer of the rat cerebellum. Within a few tens of milliseconds after the start of random mossy fiber input, the populations of Golgi and granule cells became entrained in a single synchronous oscillation, the basic frequency of which ranged from 10 to 40 Hz depending on the average rate of firing in the mossy fiber population. ... The synchronous, rhythmic firing pattern was robust over a broad range of biologically realistic parameter values and to parameter randomization. Three conditions, however, made the oscillations more transient and could desynchronize the entire network in the end: a very low mossy fiber activity, a very dominant excitation of Golgi cells through mossy fiber synapses (rather than through parallel fiber synapses), and a tonic activation of granule cell GABAA receptors (with an almost complete absence of synaptically induced inhibitory postsynaptic currents). The model predicts that, under conditions of strong mossy fiber input to the cerebellum, Golgi cells do not only control the strength of parallel fiber activity but also the timing of the individual spikes. Provided that their parallel fiber synapses constitute an important source of excitation, Golgi cells fire rhythmically and synchronized with granule cells over large distances along the parallel fiber axis. See paper for more and details.
36. Neural model of frog ventilatory rhythmogenesis (Horcholle-Bossavit and Quenet 2009)
"In the adult frog respiratory system, periods of rhythmic movements of the buccal floor are interspersed by lung ventilation episodes. The ventilatory activity results from the interaction of two hypothesized oscillators in the brainstem. Here, we model these oscillators with two coupled neural networks, whose co-activation results in the emergence of new dynamics. .. The biological interest of this formal model is illustrated by the persistence of the relevant dynamical features when perturbations are introduced in the model, i.e. dynamic noises and architecture modifications. The implementation of the networks with clock-driven continuous time neurones provides simulations with physiological time scales."
37. Olfactory bulb mitral cell gap junction NN model: burst firing and synchrony (O`Connor et al. 2012)
In a network of 6 mitral cells connected by gap junction in the apical dendrite tuft, continuous current injections of 0.06 nA are injected into 20 locations in the apical tufts of two of the mitral cells. The current injections into one of the cells starts 10 ms after the other to generate asynchronous firing in the cells (Migliore et al. 2005 protocol). Firing of the cells is asynchronous for the first 120 ms. However after the burst firing phase is completed the firing in all cells becomes synchronous.
38. Olfactory bulb mitral cell: synchronization by gap junctions (Migliore et al 2005)
In a realistic model of two electrically connected mitral cells, the paper shows that the somatically-measured experimental properties of Gap Junctions (GJs) may correspond to a variety of different local coupling strengths and dendritic distributions of GJs in the tuft. The model suggests that the propagation of the GJ-induced local tuft depolarization is a major mechanim for intraglomerular synchronization of mitral cells.
39. Olfactory Bulb Network (Davison et al 2003)
A biologically-detailed model of the mammalian olfactory bulb, incorporating the mitral and granule cells and the dendrodendritic synapses between them. The results of simulation experiments with electrical stimulation agree closely in most details with published experimental data. The model predicts that the time course of dendrodendritic inhibition is dependent on the network connectivity as well as on the intrinsic parameters of the synapses. In response to simulated odor stimulation, strongly activated mitral cells tend to suppress neighboring cells, the mitral cells readily synchronize their firing, and increasing the stimulus intensity increases the degree of synchronization. For more details, see the reference below.
40. Orientation selectivity in inhibition-dominated recurrent networks (Sadeh and Rotter, 2015)
Emergence of contrast-invariant orientation selectivity in large-scale networks of excitatory and inhibitory neurons using integrate-and-fire neuron models.
41. Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)
The model demonstrates how a common oscillatory drive for a group of neurons formats and reliabilizes their spike times - through an activation-to-phase conversion - so that repeating activation patterns can be easily detected and learned by a downstream neuron equipped with STDP, and then recognized in just one oscillation cycle.
42. Pallidostriatal projections promote beta oscillations (Corbit, Whalen, et al 2016)
This model consists of an inhibitory loop combining the projections from GPe neurons back to the striatum (shown experimentally to predominantly affect fast spiking interneurons, FSIs), together with the coupling from FSIs to medium spiny neurons (MSNs) in the striatum, along with the projections from MSNs to GPe. All models are in the Hodgkin-Huxley formalism, adapted from previously published models for each cell type. The connected circuit produces irregular activity under control conditions, but increasing FSI-to-MSN connectivity as observed experimentally under dopamine depletion yields exaggerated beta oscillations and synchrony. Additional mechanistic aspects are also explored.
43. Persistent synchronized bursting activity in cortical tissues (Golomb et al 2005)
The program simulates a one-dimensional model of a cortical tissue with excitatory and inhibitory populations.
44. Phase locking in leaky integrate-and-fire model (Brette 2004)
"This shows the phase-locking structure of a LIF driven by a sinusoidal current. When the current crosses the threshold (a<3), the model almost always phase locks (in a measure-theoretical sense)."
45. Phase precession through acceleration of local theta rhythm (Castro & Aguiar 2011)
"... Here we present a biophysical spiking model for phase precession in hippocampal CA1 which focuses on the interaction between place cells and local inhibitory interneurons. The model’s functional block is composed of a place cell (PC) connected with a local inhibitory cell (IC) which is modulated by the population theta rhythm. Both cells receive excitatory inputs from the entorhinal cortex (EC). ..."
46. Phase response curve of a globus pallidal neuron (Fujita et al. 2011)
We investigated how changes in ionic conductances alter the phase response curve (PRC) of a globus pallidal (GP) neuron and stability of a synchronous activity of a GP network, using a single-compartmental conductance-based neuron model. The results showed the PRC and the stability were influenced by changes in the persistent sodium current, the Kv3 potassium, the M-type potassium and the calcium-dependent potassium current.
47. Phase-locking analysis with transcranial magneto-acoustical stimulation (Yuan et al 2017)
"Transcranial magneto-acoustical stimulation (TMAS) uses ultrasonic waves and a static magnetic field to generate electric current in nerve tissues for the purpose of modulating neuronal activities. It has the advantage of high spatial resolution and penetration depth. Neuronal firing rhythms carry and transmit nerve information in neural systems. In this study, we investigated the phase-locking characteristics of neuronal firing rhythms with TMAS based on the Hodgkin-Huxley neuron model. The simulation results indicate that the modulation frequency of ultrasound can affect the phase-locking behaviors. The results of this study may help us to explain the potential firing mechanism of TMAS."
48. Pyramidal neuron, fast, regular, and irregular spiking interneurons (Konstantoudaki et al 2014)
This is a model network of prefrontal cortical microcircuit based primarily on rodent data. It includes 16 pyramidal model neurons, 2 fast spiking interneuron models, 1 regular spiking interneuron model and 1 irregular spiking interneuron model. The goal of the paper was to use this model network to determine the role of specific interneuron subtypes in persistent activity
49. Rapid desynchronization of an electrically coupled Golgi cell network (Vervaeke et al. 2010)
Electrical synapses between interneurons contribute to synchronized firing and network oscillations in the brain. However, little is known about how such networks respond to excitatory synaptic input. In addition to detailed electrophysiological recordings and histological investigations of electrically coupled Golgi cells in the cerebellum, a detailed network model of these cells was created. The cell models are based on reconstructed Golgi cell morphologies and the active conductances are taken from an earlier abstract Golgi cell model (Solinas et al 2007, accession no. 112685). Our results show that gap junction coupling can sometimes be inhibitory and either promote network synchronization or trigger rapid network desynchronization depending on the synaptic input. The model is available as a neuroConstruct project and can executable scripts can be generated for the NEURON simulator.
50. Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
Phenomenological spiking model of the cat early visual system. We show how natural vision can drive spike time correlations on sufficiently fast time scales to lead to the acquisition of orientation-selective V1 neurons through STDP. This is possible without reference times such as stimulus onsets, or saccade landing times. But even when such reference times are available, we demonstrate that the relative spike times encode the images more robustly than the absolute ones.
51. Reverberatory bursts propagation and synchronization in developing cultured NNs (Huang et al 2016)
"Developing networks of neural systems can exhibit spontaneous, synchronous activities called neural bursts, which can be important in the organization of functional neural circuits. ... Using a propagation model we infer the spreading speed of the spiking activity, which increases as the culture ages. We perform computer simulations of the system using a physiological model of spiking networks in two spatial dimensions and find the parameters that reproduce the observed resynchronization of spiking in the bursts. An analysis of the simulated dynamics suggests that the depletion of synaptic resources causes the resynchronization. The spatial propagation dynamics of the simulations match well with observations over the course of a burst and point to an interplay of the synaptic efficacy and the noisy neural self-activation in producing the morphology of the bursts."
52. Role of active dendrites in rhythmically-firing neurons (Goldberg et al 2006)
"The responsiveness of rhythmically-firing neurons to synaptic inputs is characterized by their phase response curve (PRC), which relates how weak somatic perturbations affect the timing of the next action potential. The shape of the somatic PRC is an important determinant of collective network dynamics. Here we study theoretically and experimentally the impact of distally-located synapses and dendritic nonlinearities on the synchronization properties of rhythmically firing neurons. Combining the theories of quasi-active cables and phase-coupled oscillators we derive an approximation for the dendritic responsiveness, captured by the neuron's dendritic PRC (dPRC). This closed-form expression indicates that the dPRCs are linearly-filtered versions of the somatic PRC, and that the filter characteristics are determined by the passive and active properties of the dendrite. ... collective dynamics can be qualitatively different depending on the location of the synapse, the neuronal firing rates and the dendritic nonlinearities." See paper for more and details.
53. Sensory feedback in an oscillatory interference model of place cell activity (Monaco et al. 2011)
Many animals use a form of dead reckoning known as 'path integration' to maintain a sense of their location as they explore the world. However, internal motion signals and the neural activity that integrates them can be noisy, leading inevitably to inaccurate position estimates. The rat hippocampus and entorhinal cortex support a flexible system of spatial representation that is critical to spatial learning and memory. The position signal encoded by this system is thought to rely on path integration, but it must be recalibrated by familiar landmarks to maintain accuracy. To explore the interaction between path integration and external landmarks, we present a model of hippocampal activity based on the interference of theta-frequency oscillations that are modulated by realistic animal movements around a track. We show that spatial activity degrades with noise, but introducing external cues based on direct sensory feedback can prevent this degradation. When these cues are put into conflict with each other, their interaction produces a diverse array of response changes that resembles experimental observations. Feedback driven by attending to landmarks may be critical to navigation and spatial memory in mammals.
54. Sleep-wake transitions in corticothalamic system (Bazhenov et al 2002)
The authors investigate the transition between sleep and awake states with intracellular recordings in cats and computational models. The model describes many essential features of slow wave sleep and activated states as well as the transition between them.
55. Small world networks of Type I and Type II Excitable Neurons (Bogaard et al. 2009)
Implemented with NEURON 5.9, four model neurons with varying excitability properties affect the spatiotemporal patterning of small world networks of homogeneous and heterogeneous cell population.
56. Spike-Timing-Based Computation in Sound Localization (Goodman and Brette 2010)
" ... In neuron models consisting of spectro-temporal filtering and spiking nonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that depend on source location rather than on source signal. Location-specific synchrony patterns would then result in the activation of location-specific assemblies of postsynaptic neurons. We designed a spiking neuron model which exploited this principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related transfer functions. ..."
57. Spikes,synchrony,and attentive learning by laminar thalamocort. circuits (Grossberg & Versace 2007)
"... The model hereby clarifies, for the first time, how the following levels of brain organization coexist to realize cognitive processing properties that regulate fast learning and stable memory of brain representations: single cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and acetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions; aggregate cell recordings, such as current-source densities and local field potentials; and single cell and large-scale inter-areal oscillations in the gamma and beta frequency domains. ..."
58. Striatal GABAergic microcircuit, dopamine-modulated cell assemblies (Humphries et al. 2009)
To begin identifying potential dynamically-defined computational elements within the striatum, we constructed a new three-dimensional model of the striatal microcircuit's connectivity, and instantiated this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs was introduced and tuned to experimental data. We introduced a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We found that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appeared, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation was strongly dependent on the simulated concentration of dopamine. We also showed that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs.
59. Striatal NN model of MSNs and FSIs investigated effects of dopamine depletion (Damodaran et al 2015)
This study investigates the mechanisms that are affected in the striatal network after dopamine depletion and identifies potential therapeutic targets to restore normal activity.
60. Structure-dynamics relationships in bursting neuronal networks revealed (Mäki-Marttunen et al. 2013)
This entry includes tools for generating and analyzing network structure, and for running the neuronal network simulations on them.
61. Synaptic gating at axonal branches, and sharp-wave ripples with replay (Vladimirov et al. 2013)
The computational model of in vivo sharp-wave ripples with place cell replay. Excitatory post-synaptic potentials at dendrites gate antidromic spikes arriving from the axonal collateral, and thus determine when the soma and the main axon fire. The model allows synchronous replay of pyramidal cells during sharp-wave ripple event, and the replay is possible in both forward and reverse directions.
62. Synchronization by D4 dopamine receptor-mediated phospholipid methylation (Kuznetsova, Deth 2008)
"We describe a new molecular mechanism of dopamine-induced membrane protein modulation that can tune neuronal oscillation frequency to attention related gamma rhythm. This mechanism is based on the unique ability of D4 dopamine receptors (D4R) to carry out phospholipid methylation (PLM) that may affect the kinetics of ion channels. We show that by deceasing the inertia of the delayed rectifier potassium channel, a transition to 40 Hz oscillations can be achieved. ..."
63. Synchrony by synapse location (McTavish et al. 2012)
This model considers synchrony between mitral cells induced via shared granule cell interneurons while taking into account the spatial constraints of the system. In particular, since inhibitory inputs decay passively along the lateral dendrites, this model demonstrates that an optimal arrangement of the inhibitory synapses will be near the cell bodies of the relevant mitral cells.
64. Synthesis of spatial tuning functions from theta cell spike trains (Welday et al., 2011)
A single compartment model reproduces the firing rate maps of place, grid, and boundary cells by receiving inhibitory inputs from theta cells. The theta cell spike trains are modulated by the rat's movement velocity in such a way that phase interference among their burst pattern creates spatial envelope function which simulate the firing rate maps.
65. Thalamocortical and Thalamic Reticular Network (Destexhe et al 1996)
NEURON model of oscillations in networks of thalamocortical and thalamic reticular neurons in the ferret. (more applications for a model quantitatively identical to previous DLGN model; updated for NEURON v4 and above)
66. Thalamocortical augmenting response (Bazhenov et al 1998)
In the cortical model, augmenting responses were more powerful in the "input" layer compared with those in the "output" layer. Cortical stimulation of the network model produced augmenting responses in cortical neurons in distant cortical areas through corticothalamocortical loops and low-threshold intrathalamic augmentation. ... The predictions of the model were compared with in vivo recordings from neurons in cortical area 4 and thalamic ventrolateral nucleus of anesthetized cats. The known intrinsic properties of thalamic cells and thalamocortical interconnections can account for the basic properties of cortical augmenting responses. See reference for details. NEURON implementation note: cortical SU cells are getting slightly too little stimulation - reason unknown.
67. Wang-Buzsaki Interneuron (Talathi et al., 2010)
The submitted code provides the relevant C++ files, matlabfiles and the data files essential to reproduce the figures in the JCNS paper titled Control of neural synchrony using channelrhodopsin-2: A computational study.

Re-display model names without descriptions