arXiv subjects

Henry D. I. Abarbanel

Publications and source records attributed to Henry D. I. Abarbanel.

32 records · Page 2Linked to original sources

arXiv2009

Effective Actions for Ensemble Data Assimilation

Ensemble data assimilation is a problem in determining the most likely phase space trajectory of a model of an observed dynamical sys- tem as it receives inputs from measurements passing information to the model. Using methods developed in statistical physics, we present effective actions and equations of motion for the mean orbits associ- ated with the temporal development of a dynamical model when it has errors, there is uncertainty in its initial state, and it receives informa- tion from measurements. If there are correlations among errors in the measurements they are naturally included in this approach.

nlin.CD↗

arXiv2009

Parameter and State Estimation of Experimental Chaotic Systems Using Synchronization

We examine the use of synchronization as a mechanism for extracting parameter and state information from experimental systems. We focus on important aspects of this problem that have received little attention previously, and we explore them using experiments and simulations with the chaotic Colpitts oscillator as an example system. We explore the impact of model imperfection on the ability to extract valid information from an experimental system. We compare two optimization methods: an initial value method and a constrained method. Each of these involve coupling the model equations to the experimental data in order to regularize the chaotic motions on the synchronization manifold. We explore both time dependent and time independent coupling. We also examine both optimized and fixed (or manually adjusted) coupling. For the case of an optimized time dependent coupling function u(t) we find a robust structure which includes sharp peaks and intervals where it is zero. This structure shows a strong correlation with the location in phase space and appears to depend on noise, imperfections of the model, and the Lyapunov direction vectors. Comparison of this result with that obtained using simulated data may provide one measure of model imperfection. The constrained method with time dependent coupling appears to have benefits in synchronizing long datasets with minimal impact, while the initial value method with time independent coupling tends to be substantially faster, more flexible and easier to use. We also describe a new method of coupling which is useful for sparse experimental data sets. Our use of the Colpitts oscillator allows us to explore in detail the case of a system with one positive Lyapunov exponent. The methods we explored are easily extended to driven systems such as neurons with time dependent injected current.

nlin.CD↗

arXiv2007

Measuring spike train synchrony

Estimating the degree of synchrony or reliability between two or more spike trains is a frequent task in both experimental and computational neuroscience. In recent years, many different methods have been proposed that typically compare the timing of spikes on a certain time scale to be fixed beforehand. Here, we propose the ISI-distance, a simple complementary approach that extracts information from the interspike intervals by evaluating the ratio of the instantaneous frequencies. The method is parameter free, time scale independent and easy to visualize as illustrated by an application to real neuronal spike trains obtained in vitro from rat slices. In a comparison with existing approaches on spike trains extracted from a simulated Hindemarsh-Rose network, the ISI-distance performs as well as the best time-scale-optimized measure based on spike timing.

physics.bio-ph↗

arXiv2005

Reading Sequences of Interspike Intervals in Biological Neural Circuits

Sensory systems pass information about an animal's environment to higher nervous system units through sequences of action potentials. When these action potentials have essentially equivalent waveforms, all information is contained in the interspike intervals (ISIs) of the spike sequence. We address the question: How do neural circuits recognize and read these ISI sequences? Our answer is given in terms of a biologically inspired neural circuit that we construct using biologically realistic neurons. The essential ingredients of the ISI Reading Unit (IRU) are (i) a tunable time delay circuit modelled after one found in the anterior forebrain pathway of the birdsong system and (ii) a recently observed rule for inhibitory synaptic plasticity. We present a circuit that can both learn the ISIs of a training sequence using inhibitory synaptic plasticity and then recognize the same ISI sequence when it is presented on subsequent occasions. We investigate the ability of this IRU to learn in the presence of two kinds of noise: jitter in the time of each spike and random spikes occurring in the ideal spike sequence. We also discuss how the circuit can be detuned by removing the selected ISI sequence and replacing it by an ISI sequence with ISIs drawn from a probability distribution. We have investigated realizations of the time delay circuit using Hodgkin-Huxley conductance based neurons connected by realistic excitatory and inhibitory synapses. Our models for the time delay circuit are tunable from about 10 ms to 100 ms allowing one to learn and recognize ISI sequences within that range of ISIs. ISIs down to a few ms and longer than 100 ms are possible with other intrinsic and synaptic currents in the component neurons.

q-bio.OT↗

arXiv2003

Spatial representation of temporal information through spike timing dependent plasticity

We suggest a mechanism based on spike time dependent plasticity (STDP) of synapses to store, retrieve and predict temporal sequences. The mechanism is demonstrated in a model system of simplified integrate-and-fire type neurons densely connected by STDP synapses. All synapses are modified according to the so-called normal STDP rule observed in various real biological synapses. After conditioning through repeated input of a limited number of of temporal sequences the system is able to complete the temporal sequence upon receiving the input of a fraction of them. This is an example of effective unsupervised learning in an biologically realistic system. We investigate the dependence of learning success on entrainment time, system size and presence of noise. Possible applications include learning of motor sequences, recognition and prediction of temporal sensory information in the visual as well as the auditory system and late processing in the olfactory system of insects.

nlin.AO↗

arXiv2003

Reading Neural Encodings using Phase Space Methods

Environmental signals sensed by nervous systems are often represented in spike trains carried from sensory neurons to higher neural functions where decisions and functional actions occur. Information about the environmental stimulus is contained (encoded) in the train of spikes. We show how to "read" the encoding using state space methods of nonlinear dynamics. We create a mapping from spike signals which are output from the neural processing system back to an estimate of the analog input signal. This mapping is realized locally in a reconstructed state space embodying both the dynamics of the source of the sensory signal and the dynamics of the neural circuit doing the processing. We explore this idea using a Hodgkin-Huxley conductance based neuron model and input from a low dimensional dynamical system, the Lorenz system. We show that one may accurately learn the dynamical input/output connection and estimate with high precision the details of the input signals from spike timing output alone. This form of "reading the neural code" has a focus on the neural circuitry as a dynamical system and emphasizes how one interprets the dynamical degrees of freedom in the neural circuit as they transform analog environmental information into spike trains.

physics.bio-ph↗

arXiv2002

Robustness and Enhancement of Neural Synchronization by Activity-Dependent Coupling

We study the synchronization of two model neurons coupled through a synapse having an activity-dependent strength. Our synapse follows the rules of Spike-Timing Dependent Plasticity (STDP). We show that this plasticity of the coupling between neurons produces enlarged frequency locking zones and results in synchronization that is more rapid and much more robust against noise than classical synchronization arising from connections with constant strength. We also present a simple discrete map model that demonstrates the generality of the phenomenon.

physics.bio-ph↗

arXiv2000

Distribution of Mutual Information

In the analysis of time series from nonlinear sources, mutual information (MI) is used as a nonlinear statistical criterion for the selection of an appropriate time delay in time delay reconstruction of the state space. MI is a statistic over the sets of sequences associated with the dynamical source, and we examine here the distribution of MI, thus going beyond the familiar analysis of its average alone. We give for the first time the distribution of MI for a standard, classical communications channel with Gaussian, additive white noise. For time series analysis of a dynamical system, we show how to determine the distribution of MI and discuss the implications for the use of average mutual information (AMI) in selecting time delays in phase space reconstruction.

nlin.CD↗

arXiv2000

Synchronous Behavior of Two Coupled Electronic Neurons

We report on experimental studies of synchronization phenomena in a pair of analog electronic neurons (ENs). The ENs were designed to reproduce the observed membrane voltage oscillations of isolated biological neurons from the stomatogastric ganglion of the California spiny lobster Panulirus interruptus. The ENs are simple analog circuits which integrate four dimensional differential equations representing fast and slow subcellular mechanisms that produce the characteristic regular/chaotic spiking-bursting behavior of these cells. In this paper we study their dynamical behavior as we couple them in the same configurations as we have done for their counterpart biological neurons. The interconnections we use for these neural oscillators are both direct electrical connections and excitatory and inhibitory chemical connections: each realized by analog circuitry and suggested by biological examples. We provide here quantitative evidence that the ENs and the biological neurons behave similarly when coupled in the same manner. They each display well defined bifurcations in their mutual synchronization and regularization. We report briefly on an experiment on coupled biological neurons and four dimensional ENs which provides further ground for testing the validity of our numerical and electronic models of individual neural behavior. Our experiments as a whole present interesting new examples of regularization and synchronization in coupled nonlinear oscillators.

nlin.CD↗

arXiv1999

Topology of Central Pattern Generators: Selection by Chaotic Neurons

Central Pattern Generators (CPGs) in invertebrates are comprised of networks of neurons in which every neuron has reciprocal connections to other members of the CPG. This is a ``closed'' network topology. An ``open'' topology, where one or more neurons receives input but does not send output to other member neurons, is not found in these CPGs. In this paper we investigate a possible reason for this topological structure using the ability to perform a biological functional task as a measure of the efficacy of the network. When the CPG is composed of model neurons which exhibit regular membrane voltage oscillations, open topologies are essentially as able to maximize this functionality as closed topologies. When we replace these models by neurons which exhibit chaotic membrane voltage oscillations, the functional criterion selects closed topologies when the demands of the task are increased, and these are the topologies observed in known CPG networks. As isolated neurons from invertebrate CPGs are known in some cases to undergo chaotic oscillations (Abarbanel et al 1996, Hayashi Ishuzuka 1992) this provides a biological basis for understanding the class of closed network topologies we observe.

chao-dyn↗

arXiv1999

Synchronization of chaotic oscillations in doped fiber ring lasers

We investigate synchronization and subsequently communication using chaotic rare-earth-doped fiber ring lasers, represented by a physically realistic model. The lasers are coupled by transmitting a fraction c of the circulating electric field in the transmitter and injecting it into the optical cavity of the receiver. We then analyze a coupling strategy which relies on modulation of the intensity of the light alone. This avoids problems associated with the polarization and phase of the laser light. We study synchronization as a function of the coupling strength and see excellent convergence, even with small coupling constants. We prove that in an open-loop configuration (c=1) synchronization is guaranteed due to the particular structure of our equations and of the injection method we use for these coupled laser systems. We also analyze the generalized synchronization of these model lasers when there is parameter mismatch between the transmitter and the receiver. We then address communicating information between the transmitter and receiver lasers by modulation of the chaotic optical waveform. Finally we comment on the cryptographic setting of our algorithms, especially the open-loop strategy at c=1, and hope this may lead others to perform the cryptographic analyses to determine which, if any, of the communications strategies we investigate are secure.

chao-dyn↗

arXiv1999

Sensory Coding with Dynamically Competitive Networks

Studies of insect olfactory processing indicate that odors are represented by rich spatio-temporal patterns of neural activity. These patterns are very difficult to predict a priori, yet they are stimulus specific and reliable upon repeated stimulation with the same input. We formulate here a theoretical framework in which we can interpret these experimental results. We propose a paradigm of ``dynamic competition'' in which inputs (odors) are represented by internally competing neural assemblies. Each pattern is the result of dynamical motion within the network and does not involve a ``winner'' among competing possibilities. The model produces spatio-temporal patterns with strong resemblance to those observed experimentally and possesses many of the general features one desires for pattern classifiers: large information capacity, reliability, specific responses to specific inputs, and reduced sensitivity to initial conditions or influence of noise. This form of neural processing may thus describe the organizational principles of neural information processing in sensory systems and go well beyond the observations on insect olfactory processing which motivated its development.

q-bio.NC↗

arXiv1998

Synchronous Behavior of Two Coupled Biological Neurons

We report experimental studies of synchronization phenomena in a pair of biological neurons that interact through naturally occurring, electrical coupling. When these neurons generate irregular bursts of spikes, the natural coupling synchronizes slow oscillations of membrane potential, but not the fast spikes. By adding artificial electrical coupling we studied transitions between synchrony and asynchrony in both slow oscillations and fast spikes. We discuss the dynamics of bursting and synchronization in living neurons with distributed functional morphology.

chao-dyn↗

arXiv1994

Prediction Errors and Local Lyapunov Exponents

It is frequently asserted that in a chaotic system two initially close points will separate at an exponential rate governed by the largest global Lyapunov exponent. Local Lyapunov exponents, however, are more directly relevant to predictability. The difference between the local and global Lyapunov exponents, the large variations of local exponents over an attractor, and the saturation of error growth near the size of the attractor---all result in non-exponential scalings in errors at both short and long prediction times, sometimes even obscuring evidence of exponential growth. Failure to observe exponential error scaling cannot rule out deterministic chaos as an explanation. We demonstrate a simple model that quantitatively predicts observed error scaling from the local Lyapunov exponents, for both short and surprisingly long times. We comment on the relevance to atmospheric predictability as studied in the meteorological literature.

chao-dyn↗