The uncoupling limit of identical Hopf bifurcations with an application to perceptual bistability

We study the dynamics arising when two identical oscillators are coupled near a Hopf bifurcation where we assume a parameter $\epsilon$ uncouples the system at $\epsilon=0$. Using a normal form for $N=2$ identical systems undergoing Hopf bifurcation, we explore the dynamical properties. Matching the normal form coefficients to a coupled Wilson-Cowan oscillator network gives an understanding of different types of behaviour that arise in a model of perceptual bistability. Notably, we find bistability between in-phase and anti-phase solutions that demonstrates the feasibility for synchronisation to act as the mechanism by which periodic inputs can be segregated (rather than via strong inhibitory coupling, as in existing models). Using numerical continuation we confirm our theoretical analysis for small coupling strength and explore the bifurcation diagrams for large coupling strength, where the normal form approximation breaks down

On the role of oscillatory dynamics in neural communication

In this Thesis we consider problems concerning brain oscillations generated across the interaction between excitatory (E) and inhibitory (I) cells. We explore how two neuronal groups with underlying oscillatory activity communicate much effectively when they are properly phase-locked as suggested by Communcation Through Coherence Theory. In Chapter 1 we introduce the Wilson-Cowan equations (WC), a mean field model describing the mean activity of a network of a single population of E cells and a single popultation of I cells and review the bifurcations that give rise to oscillatory dynamics. In Chapter 2 we study how the oscillations generated across the E-I interaction are affect by a periodic forcing. We take the WC equations in the oscillatory regime with an external time periodic perturbation. We consider the stroboscopic map for this system and compute the bifurcation diagram for its fixed and periodic points as the amplitude and the frequency of the perturbation are varied. From the bifurcation diagram, we can identify the phase-locked states as well as different areas involving bistablility between two invariant objects. Chapter 3 exploits recent techniques based on phase-amplitude variables to describe the phase dynamics of an oscillator under different perturbations. More precisely, the applications of the parameterization method to compute a change of variables that describes correctly the dynamics near a limit cycle in terms of the phase (a periodic variable) and the amplitude. The computational method uses the Floquet normal form to reduce the computational cost. This change provides two remarkable manifolds used in neuroscience: the sets of constant phase/amplitude (isochrons/isostables). Moreover, we compute the functions describing the phase and amplitude changes caused by a perturbation arriving at different phases of the cycle, known as Phase and Amplitude Response Curves, PRCs and ARCs, respectively. The computed parameterization provides also the extension of these curves outside of the limit cycle, defined as the Phase and Amplitude Response Functions, PRFs and ARFs, respectively. We compute these objects for limits cycles in systems with 2 and 3 dimensions. In Chapter 4 we apply the parameterization method to compute Phase Response Curves (PRCs) for a transient stimulus of arbitrary amplitude and duration. The underlying idea is to construct a particular periodic perturbation consisting of the repetition of the transient stimulus followed by a resting period when no perturbation acts. For this periodic system we consider the corresponding stroboscopic map and we prove that, under certain conditions, it has an invariant curve. We prove that this map has an invariant curve and we provide the relationship between the PRC and the internal dynamics of the curve. Moreover, we link the existence properties of this invariant curve as the amplitude of the perturbation is increased with changes in the PRC waveform and with the geometry of isochrons. Furthermore, we also provide algorithms to obtain numerically the PRC and the ARC. In Chapter 5 we study the dynamics arising when two identical oscillators are coupled near a Hopf bifurcation, where we assume the existence of a parameter uncoupling the system when it is equal to zero. Using a recently derived truncated normal form, we perform a theoretical dynamical analysis and study its bifurcations. Computing the normal form coefficients in the case of 2 coupled Wilson-Cowan oscillators gives an understanding of different types of behaviour that arise in this model of perceptual bistability. Notably, we find bistability between in-phase and anti-phase solutions. Using numerical continuation we confirm our theoretical analysis for small coupling strength and explore the bifurcation diagrams for large coupling strength, where the normal form approximation breaks down. We finally discuss the implications of this dynamical study in models of perceptual bistability.Aquesta Tesi estudia problemes relacionats amb les oscil·lacions de l'activitat cerebral. Explorem com dues poblacions neuronals en activitat oscil·latòria es comuniquen més efectivament quan estan lligades en fase, tal com suggereix la teoria de 'Comunicació a Través de la Coherència'. Al capítol 1 introduïm les equacions de Wilson-Cowan (WC), un model de camp mitjà que descriu l' activitat d'una xarxa de neurones excitatòries (E) i inhibitòries (I) i calculem les bifurcacions que generen cicles límit. Al capítol 2 estudiem com un cicle límit generat a través d'aquesta interacció E-I respon a un forçament periòdic. Considerem el model de WC en règim oscil·latori amb una pertorbació externa periòdica en el temps. Considerem el mapa estroboscòpic d'aquest sistema i calculem el diagrama de bifurcació dels seus punts fixos i òrbites periòdiques en funció de l'amplitud i la freqüència de la pertorbació. El diagrama de bifurcació ens permet identificar les àrees amb lligadura de fase, axí com diferents àrees on tenim coexistència de dos objectes invariants estables. Al capítol 3 utilitzem tècniques recents basades en les variables fase-amplitud per a descriure la dinàmica de fase d'un oscil·lador sota diferents pertorbacions. En particular, utilitzem el mètode de la parametrització per a calcular un canvi de variables que descriu correctament la dinàmica prop del cicle límit en termes de la fase (variable periòdica) i l'amplitud. Aquests càlculs estan basats en la forma normal de Floquet que en redueix el cost computacional. Aquest canvi de variables ens permet calcular dos varietats importants en neurociència: els conjunts de fase/amplitud constant (les isòcrones/isostables). A més a més, calculem les funcions que descriuen els canvis de fase i amplitud causats per una pertorbació que arriba a diferents fases del cicle, les Corbes de Resposta de Fase i Amplitud, (PRCs i ARCs), respectivament. El canvi de variables calculat proporciona també l'extensió d'aquestes corbes fora del cicle límit, definides com les Funcions de Resposta de Fase i Amplitud, (PRFs i ARFs). Calculem tots aquests objectes per a cicles límit en 2 i 3 dimensions. Al capítol 4 ens centrem en les aplicacions del mètode de la parametrització per calcular PRCs per a estímuls de duració i amplitud arbitraria. La idea bàsica del mètode és construir una pertorbació periòdica particular que consisteix en la repetició d'un estímul transitori seguit d'un període de relaxació en el qual no actua cap pertorbació. Per a aquest sistema periòdic considerem el seu corresponent mapa estroboscòpic i demostrem que sota certes condicions, té una corba invariant. Demostrem que aquesta aplicació té una corba invariant i donem la relació entre la PRC i la dinàmica interna d'aquesta corba. A més a més, relacionem les propietats d'existència d'aquesta corba quan l'amplitud de la pertorbació augmenta, amb els canvis a la PRC i a la geometria de les isòcrones. Finalment, presentem algoritmes per obtenir numèricament la PRC i la ARC. Al capítol 5 estudiem la dinàmica emergent quan s'acoblen dos oscil·ladors idèntics prop d'una bifurcació de Hopf, pels quals suposem l'existència d'un paràmetre que desacobla el sistema quan s'anul·la. Utilitzant una forma normal derivada recentment per a 2 sistemes idèntics prop d'una bifurcació de Hopf, fem una anàlisi teòrica i estudiem les seves bifurcacions. Identificant els coeficients de la forma normal per a un model de dos oscil·ladors de tipus WC acoblats, il·lustrem els resultats obtinguts en l'anàlisi teòrica en un model amb moltes aplicacions al camp de la percepció biestable. Un resultat important és la biestabilitat entre solucions en fase i en antifase. Utilitzant mètodes de continuacióPostprint (published version

ON SYNCHRONIZATION AND CONTROL OF COUPLED WILSON–COWAN NEURAL OSCILLATORS

This paper investigates the complex dynamics, synchronization, and control of chaos in a system of strongly connected Wilson-Cowan neural oscillators. Some typical synchronized periodic solutions are analyzed by using the Poincaré mapping method, for which bifurcation diagrams are obtained. It is shown that topological change of the synchronization mode is mainly caused and carried out by the Neimark-Sacker bifurcation. Finally, a simple feedback control method is presented for stabilizing an in-phase synchronizing periodic solution embedded in the chaotic attractor of a higher-dimensional model of such coupled neural oscillators

Bifurcation Analysis and Spatiotemporal Patterns of Nonlinear Oscillations in a Ring Lattice of Identical Neurons with Delayed Coupling

We investigate the dynamics of a delayed neural network model consisting of n identical neurons. We first analyze stability of the zero solution and then study the effect of time delay on the dynamics of the system. We also investigate the steady state bifurcations and their stability. The direction and stability of the Hopf bifurcation and the pitchfork bifurcation are analyzed by using the derived normal forms on center manifolds. Then, the spatiotemporal patterns of bifurcating periodic solutions are investigated by using the symmetric bifurcation theory, Lie group theory and S1-equivariant degree theory. Finally, two neural network models with four or seven neurons are used to verify our theoretical results

Conditions for wave trains in spiking neural networks

Spatiotemporal patterns such as traveling waves are frequently observed in recordings of neural activity. The mechanisms underlying the generation of such patterns are largely unknown. Previous studies have investigated the existence and uniqueness of different types of waves or bumps of activity using neural-field models, phenomenological coarse-grained descriptions of neural-network dynamics. But it remains unclear how these insights can be transferred to more biologically realistic networks of spiking neurons, where individual neurons fire irregularly. Here, we employ mean-field theory to reduce a microscopic model of leaky integrate-and-fire (LIF) neurons with distance-dependent connectivity to an effective neural-field model. In contrast to existing phenomenological descriptions, the dynamics in this neural-field model depends on the mean and the variance in the synaptic input, both determining the amplitude and the temporal structure of the resulting effective coupling kernel. For the neural-field model we employ liner stability analysis to derive conditions for the existence of spatial and temporal oscillations and wave trains, that is, temporally and spatially periodic traveling waves. We first prove that wave trains cannot occur in a single homogeneous population of neurons, irrespective of the form of distance dependence of the connection probability. Compatible with the architecture of cortical neural networks, wave trains emerge in two-population networks of excitatory and inhibitory neurons as a combination of delay-induced temporal oscillations and spatial oscillations due to distance-dependent connectivity profiles. Finally, we demonstrate quantitative agreement between predictions of the analytically tractable neural-field model and numerical simulations of both networks of nonlinear rate-based units and networks of LIF neurons.Comment: 36 pages, 8 figures, 4 table

Intermediate Stable Phase Locked States In Oscillator Networks

The study of nonlinear oscillations is important in a variety of physical and biological contexts (especially in neuroscience). Synchronization of oscillators has been a problem of interest in recent years. In networks of nearest neighbor coupled oscillators it is possible to obtain synchrony between oscillators, but also a variety of constant phase shifts between 0 and pi. We coin these phase shifts intermediate stable phase-locked states. In neuroscience, both individual neurons and populations of neurons can behave as complex nonlinear oscillators. Intermediate stable phase-locked states are shown to be obtainable between individual oscillators and populations of identical oscillators.These intermediate stable phase-locked states may be useful in the construction of central pattern generators: autonomous neural cicuits responsible for motor behavior. In large chains and two-dimenional arrays of oscillators, intermediate stable phase-locked states provide a mechanism to produce waves and patterns that cannot be obtained in traditional network models. A particular pattern of interest is known as an anti-wave. This pattern corresponds to the collision of two waves from opposite ends of an oscillator chain. This wave may be relevant in the spinal central pattern generators of various fish. Anti-wave solutions in both conductance based neuron models and phase oscillator models are analyzed. It is shown that such solutions arise in phase oscillator models in which the nonlinearity (interaction function) contains both higher order odd and even Fourier modes. These modes are prominent in pairs of synchronous oscillators which lose stability in a supercritical pitchfork bifurcation

Dimension Reduction of Neural Models Across Multiple Spatio-temporal Scales

In general, reducing the dimensionality of a complex model is a natural first step to gaining insight into the system. In this dissertation, we reduce the dimensions of models at three different scales: first at the scale of microscopic single-neurons, second at the scale of macroscopic infinite neurons, and third at an in-between spatial scale of finite neural populations. Each model also exhibits a separation of timescales, making them amenable to the method of multiple timescales, which is the primary dimension-reduction tool of this dissertation. In the first case, the method of multiple timescales reduces the dynamics of two coupled n-dimensional neurons into one scalar differential equation representing the slow timescale phase-locking properties of the oscillators as a function of an exogenous slowly varying parameter. This result extends the classic theory of weakly coupled oscillators. In the second case, the method reduces the many spatio-temporal \yp{dynamics of} ``bump'' solutions of a neural field model into its scalar coordinates, which are much easier to analyze analytically. This result generalizes existing studies on neural field spatio-temporal dynamics to the case of a smooth firing rate function and general even kernel. In the third case, we reduce the dimension of the oscillators at the spiking level -- similar to the first case -- but with additional slowly varying synaptic variables. This result generalizes existing studies that use scalar oscillators and the Ott-Antonsen ansatz to reduce the dimensionality and determine the synchronization properties of large neural populations