35 research outputs found
Spatiotemporal symmetries in the disynaptic canal-neck projection
Get PDFThe vestibular system in almost all vertebrates, and in particular in humans, controls
balance by employing a set of six semicircular canals, three in each inner ear, to detect angular
accelerations of the head in three mutually orthogonal coordinate planes. Signals from the canals are
transmitted to eight (groups of) neck motoneurons, which activate the eight corresponding muscle
groups. These signals may be either excitatory or inhibitory, depending on the direction of head
acceleration. McCollum and Boyle have observed that in the cat the relevant network of neurons
possesses octahedral symmetry, a structure that they deduce from the known innervation patterns
(connections) from canals to muscles. We rederive the octahedral symmetry from mathematical
features of the probable network architecture, and model the movement of the head in response to
the activation patterns of the muscles concerned. We assume that connections between neck muscles
can be modeled by a âcoupled cell network,â a system of coupled ODEs whose variables correspond
to the eight muscles, and that this network also has octahedral symmetry. The network and its
symmetries imply that these ODEs must be equivariant under a suitable action of the octahedral
group. It is observed that muscle motoneurons form natural âpush-pull pairsâ in which, for given
movements of the head, one neuron produces an excitatory signal, whereas the other produces an
inhibitory signal. By incorporating this feature into the mathematics in a natural way, we are led
to a model in which the octahedral group acts by signed permutations on muscle motoneurons.
We show that with the appropriate group actions, there are six possible spatiotemporal patterns of
time-periodic states that can arise by Hopf bifurcation from an equilibrium representing an immobile
head. Here we use results of Ashwin and Podvigina. Counting conjugate states, whose physiological
interpretations can have significantly different features, there are 15 patterns of periodic oscillation,
not counting left-right reflections or time-reversals as being different. We interpret these patterns
as motions of the head, and note that all six types of pattern appear to correspond to natural head
motions
Interneuronal gamma oscillations in hippocampus via adaptive exponential integrate-and-fire neurons
Get PDFInternational audienceFast neuronal oscillations in gamma frequencies are observed in neocortex and hippocampus during essential arousal behaviors. Through a four-variable Hodgkin-Huxley type model, Wang and BuzsĂĄki have numerically demonstrated that such rhythmic activity can emerge from a random network of GABAergic interneurons via minimum synaptic inputs. In this case, the intrinsic neuronal characteristics and network structure act as the main drive of the rhythm. We investigate inhibitory network synchrony with a low complexity, two-variable adap-tive exponential integrate-and-fire (AdEx) model, whose parameters possess strong physiological relevances, and provide a comparison with the two-variable Izhike-vich model and Morris-Lecar model. Despite the simplicity of these three models, AdEx model shares two important results with the previous biophysically detailed Hodgkin-Huxley type model: the minimum number of synaptic input necessary to initiate network gamma-band rhythms remains the same, and this number is weakly dependent on the network size. Meanwhile, Izhikevich and Morris-Lecar neurons demonstrate different results in this study. We further investigate the necessary neuronal, synaptic and connectivity properties, including gap junctions and shunting inhibitions, for AdEx model leading to sparse and random network synchrony in gamma rhythms and nested theta gamma rhythms. These findings suggest a computationally more tractable framework for studying synchronized networks in inducing cerebral gamma band activities
Effects of adaptation and synaptic plasticity on synchronization of coupled oscillating neurons
Get PDFModeling electrocortical activity through improved local approximations of integral neural field equations
Get PDFNeural field models of firing rate activity typically take the form of integral equations with space-dependent axonal delays. Under natural assumptions on the synaptic connectivity we show how one can derive an equivalent partial differential equation (PDE) model that properly treats the axonal delay terms of the integral formulation. Our analysis avoids the so-called long-wavelength approximation that has previously been used to formulate PDE models for neural activity in two spatial dimensions. Direct numerical simulations of this PDE model show instabilities of the homogeneous steady state that are in full agreement with a Turing instability analysis of the original integral model. We discuss the benefits of such a local model and its usefulness in modeling electrocortical activity. In particular we are able to treat "patchy'" connections, whereby a homogeneous and isotropic system is modulated in a spatially periodic fashion. In this case the emergence of a "lattice-directed" traveling wave predicted by a linear instability analysis is confirmed by the numerical simulation of an appropriate set of coupled PDEs.
Article published and (c) American Physical Society 200
Stochastic Delay Accelerates Signaling in Gene Networks
Get PDFThe creation of protein from DNA is a dynamic process consisting of numerous reactions, such as transcription, translation and protein folding. Each of these reactions is further comprised of hundreds or thousands of sub-steps that must be completed before a protein is fully mature. Consequently, the time it takes to create a single protein depends on the number of steps in the reaction chain and the nature of each step. One way to account for these reactions in models of gene regulatory networks is to incorporate dynamical delay. However, the stochastic nature of the reactions necessary to produce protein leads to a waiting time that is randomly distributed. Here, we use queueing theory to examine the effects of such distributed delay on the propagation of information through transcriptionally regulated genetic networks. In an analytically tractable model we find that increasing the randomness in protein production delay can increase signaling speed in transcriptional networks. The effect is confirmed in stochastic simulations, and we demonstrate its impact in several common transcriptional motifs. In particular, we show that in feedforward loops signaling time and magnitude are significantly affected by distributed delay. In addition, delay has previously been shown to cause stable oscillations in circuits with negative feedback. We show that the period and the amplitude of the oscillations monotonically decrease as the variability of the delay time increases
Impact of Adaptation Currents on Synchronization of Coupled Exponential Integrate-and-Fire Neurons
Get PDFThe ability of spiking neurons to synchronize their activity in a network depends on the response behavior of these neurons as quantified by the phase response curve (PRC) and on coupling properties. The PRC characterizes the effects of transient inputs on spike timing and can be measured experimentally. Here we use the adaptive exponential integrate-and-fire (aEIF) neuron model to determine how subthreshold and spike-triggered slow adaptation currents shape the PRC. Based on that, we predict how synchrony and phase locked states of coupled neurons change in presence of synaptic delays and unequal coupling strengths. We find that increased subthreshold adaptation currents cause a transition of the PRC from only phase advances to phase advances and delays in response to excitatory perturbations. Increased spike-triggered adaptation currents on the other hand predominantly skew the PRC to the right. Both adaptation induced changes of the PRC are modulated by spike frequency, being more prominent at lower frequencies. Applying phase reduction theory, we show that subthreshold adaptation stabilizes synchrony for pairs of coupled excitatory neurons, while spike-triggered adaptation causes locking with a small phase difference, as long as synaptic heterogeneities are negligible. For inhibitory pairs synchrony is stable and robust against conduction delays, and adaptation can mediate bistability of in-phase and anti-phase locking. We further demonstrate that stable synchrony and bistable in/anti-phase locking of pairs carry over to synchronization and clustering of larger networks. The effects of adaptation in aEIF neurons on PRCs and network dynamics qualitatively reflect those of biophysical adaptation currents in detailed Hodgkin-Huxley-based neurons, which underscores the utility of the aEIF model for investigating the dynamical behavior of networks. Our results suggest neuronal spike frequency adaptation as a mechanism synchronizing low frequency oscillations in local excitatory networks, but indicate that inhibition rather than excitation generates coherent rhythms at higher frequencies
25th annual computational neuroscience meeting: CNS-2016
Get PDFThe same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong
