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K Pawelzik

Publications and source records attributed to K Pawelzik.

17 recordsLinked to original sources
PubMed2004

Dynamics of population rate codes in ensembles of neocortical neurons.

Information processing in neocortex can be very fast, indicating that neuronal ensembles faithfully transmit rapidly changing signals to each other. Apart from signal-to-noise issues, population codes are fundamentally constrained by the neuronal dynamics. In particular, the biophysical properties of individual neurons and collective phenomena may substantially limit the speed at which a graded signal can be represented by the activity of an ensemble. These implications of the neuronal dynamics are rarely studied experimentally. Here, we combine theoretical analysis and whole cell recordings to show that encoding signals in the variance of uncorrelated synaptic inputs to a neocortical ensemble enables faithful transmission of graded signals with high temporal resolution. In contrast, the encoding of signals in the mean current is subject to low-pass filtering.

Models, Neurological↗
PubMed2003

Optimal neural rate coding leads to bimodal firing rate distributions.

Many experimental studies concerning the neuronal code are based on graded responses of neurons, given by the emitted number of spikes measured in a certain time window. Correspondingly, a large body of neural network theory deals with analogue neuron models and discusses their potential use for computation or function approximation. All physical signals, however, are of limited precision, and neuronal firing rates in cortex are relatively low. Here, we investigate the relevance of analogue signal processing with spikes in terms of optimal stimulus reconstruction and information theory. In particular, we derive optimal tuning functions taking the biological constraint of limited firing rates into account. It turns out that depending on the available decoding time T, optimal encoding undergoes a phase transition from discrete binary coding for small T towards analogue or quasi-analogue encoding for large T. The corresponding firing rate distributions are bimodal for all relevant T, in particular in the case of population coding.

Action Potentials↗
PubMed2002

Optimal short-term population coding: when Fisher information fails.

Efficient coding has been proposed as a first principle explaining neuronal response properties in the central nervous system. The shape of optimal codes, however, strongly depends on the natural limitations of the particular physical system. Here we investigate how optimal neuronal encoding strategies are influenced by the finite number of neurons N (place constraint), the limited decoding time window length T (time constraint), the maximum neuronal firing rate f(max) (power constraint), and the maximal average rate (f)(max) (energy constraint). While Fisher information provides a general lower bound for the mean squared error of unbiased signal reconstruction, its use to characterize the coding precision is limited. Analyzing simple examples, we illustrate some typical pitfalls and thereby show that Fisher information provides a valid measure for the precision of a code only if the dynamic range (f(min)T, f(max)T) is sufficiently large. In particular, we demonstrate that the optimal width of gaussian tuning curves depends on the available decoding time T. Within the broader class of unimodal tuning functions, it turns out that the shape of a Fisher-optimal coding scheme is not unique. We solve this ambiguity by taking the minimum mean square error into account, which leads to flat tuning curves. The tuning width, however, remains to be determined by energy constraints rather than by the principle of efficient coding.

Artifacts↗
PubMed2000

Identification of nonstationary dynamics in physiological recordings.

We present a novel framework for the analysis of time series from dynamical systems that alternate between different operating modes. The method simultaneously segments and identifies the dynamical modes by using predictive models. In extension to previous approaches, it allows an identification of smooth transition between successive modes. The method can be used for analysis, diagnosis, prediction, and control. In an application to EEG and respiratory data recorded from humans during afternoon naps, the obtained segmentations of the data agree with the sleep stage segmentation of a medical expert to a large extent. However, in contrast to the manual segmentation, our method does not require a priori knowledge about physiology. Moreover, it has a high temporal resolution and reveals previously unclassified details of the transitions. In particular, a parameter is found that is potentially helpful for vigilance monitoring. We expect that the method will generally be useful for the analysis of nonstationary dynamical systems, which are abundant in medicine, chemistry, biology and engineering.

Electroencephalography↗
PubMed1999

Relation between retinotopical and orientation maps in visual cortex.

A recent study of cat visual cortex reported abrupt changes in the positions of the receptive fields of adjacent neurons whose preferred orientations strongly differed (Das & Gilbert, 1997). Using a simple cortical model, we show that this covariation of discontinuities in maps of orientation preference and local distortions in maps of visual space reflects collective effects of the lateral cortical feedback.

Animals↗
PubMed1999

Theory of ocular dominance pattern formation.

We investigate a general and analytically tractable model for the activity-dependent formation of neuronal connectivity patterns. Previous models are contained as limiting cases. As an important example we analyze the formation of ocular dominance patterns in the visual cortex. A linear stability analysis reveals that the model undergoes a Turing-type instability as a function of interaction range and receptive field size. The phase transitions is of second order. After the linear instability the patterns may reorganize which we analyze in terms of a potential for the dynamics. Our analysis demonstrates that the experimentally observed dependency of ocular dominance patterns on interocular correlations of visual experience during development can emerge according to two generic scenarios: either the system is driven through the phase transition during development thereby selecting and stabilizing the first unstable mode or a primary pattern reorganizes towards larger wavelength according their lower energy. Experimentally observing the time course of ocular dominance pattern formation will decide which scenario is realized in the brain.

Algorithms↗
PubMed1998

Neural networks with dynamic synapses.

Transmission across neocortical synapses depends on the frequency of presynaptic activity (Thomson & Deuchars, 1994). Interpyramidal synapses in layer V exhibit fast depression of synaptic transmission, while other types of synapses exhibit facilitation of transmission. To study the role of dynamic synapses in network computation, we propose a unified phenomenological model that allows computation of the postsynaptic current generated by both types of synapses when driven by an arbitrary pattern of action potential (AP) activity in a presynaptic population. Using this formalism, we analyze different regimes of synaptic transmission and demonstrate that dynamic synapses transmit different aspects of the presynaptic activity depending on the average presynaptic frequency. The model also allows for derivation of mean-field equations, which govern the activity of large, interconnected networks. We show that the dynamics of synaptic transmission results in complex sets of regular and irregular regimes of network activity.

Action Potentials↗
PubMed1998

Irregular synchronous activity in stochastically-coupled networks of integrate-and-fire neurons.

We investigate the spatial and temporal aspects of firing patterns in a network of integrate-and-fire neurons arranged in a one-dimensional ring topology. The coupling is stochastic and shaped like a Mexican hat with local excitation and lateral inhibition. With perfect precision in the couplings, the attractors of activity in the network occur at every position in the ring. Inhomogeneities in the coupling break the translational invariance of localized attractors and lead to synchronization within highly active as well as weakly active clusters. The interspike interval variability is high, consistent with recent observations of spike time distributions in visual cortex. The robustness of our results is demonstrated with more realistic simulations on a network of McGregor neurons which model conductance changes and after-hyperpolarization potassium currents.

Nerve Net↗
PubMed1994

Uncovering the synchronization dynamics from correlated neuronal activity quantifies assembly formation.

Synchronous network excitation is believed to play an outstanding role in neuronal information processing. Due to the stochastic nature of the contributing neurons, however, those synchronized states are difficult to detect in electrode recordings. We present a framework and a model for the identification of such network states and of their dynamics in a specific experimental situation. Our approach operationalizes the notion of neuronal groups forming assemblies via synchronization based on experimentally obtained spike trains. The dynamics of such groups is reflected in the sequence of synchronized states, which we describe as a renewal dynamics. We furthermore introduce a rate function which is dependent on the internal network phase that quantifies the activity of neurons contributing to the observed spike train. This constitutes a hidden state model which is formally equivalent to a hidden Markov model, and all its parameters can be accurately determined from the experimental time series using the Baum-Welch algorithm. We apply our method to recordings from the cat visual cortex which exhibit oscillations and synchronizations. The parameters obtained for the hidden state model uncover characteristic properties of the system including synchronization, oscillation, switching, background activity and correlations. In applications involving multielectrode recordings, the extracted models quantify the extent of assembly formation and can be used for a temporally precise localization of system states underlying a specific spike train.

Algorithms↗
PubMedDate not supplied

Extracting the dynamics of perceptual switching from 'noisy' behaviour: an application of hidden Markov modelling to pecking data from pigeons.

When studying animal perception, one normally has the chance of localizing perceptual events in time, that is via behavioural responses time-locked to the stimuli. With multistable stimuli, however, perceptual changes occur despite stationary stimulation. Here, the challenge is to infer these not directly observable perceptual states indirectly from the behavioural data. This estimation is complicated by the fact that an animal's performance is contaminated by errors. We propose a two-step approach to overcome this difficulty: First, one sets up a generative, stochastic model of the behavioural time series based on the relevant parameters, including the probability of errors. Second, one performs a model-based maximum-likelihood estimation on the data in order to extract the non-observable perceptual state transitions. We illustrate this methodology for data from experiments on perception of bistable apparent motion in pigeons. The observed behavioural time series is analysed and explained by a combination of a Markovian perceptual dynamics with a renewal process that governs the motor response. We propose a hidden Markov model in which non-observable states represent both the perceptual states and the states of the renewal process of the motor dynamics, while the observable states account for overt pecking performance. Showing that this constitutes an appropriate phenomenological model of the time series of observable pecking events, we use it subsequently to obtain an estimate of the internal (and thus covert) perceptual reversals. These may directly correspond to changes in the activity of mutually inhibitory populations of motion selective neurones tuned to orthogonal directions.

Animals↗
PubMedDate not supplied

How can squint change the spacing of ocular dominance columns?

The pattern of ocular dominance columns in primary visual cortex of mammals such as cats and macaque monkeys arises during development by the activity-dependent refinement of thalamocortical connections. Manipulating visual experience in kittens by the induction of squint leads to the emergence of ocular dominance columns with a larger size and larger column-to-column spacing than in normally raised animals. The mechanism underlying this phenomenon is presently unknown. Theory suggests that experience cannot influence the spacing of columns if the development proceeds through purely Hebbian mechanisms. Here we study a developmental model in which Hebbian mechanisms are complemented by activity-dependent regulation of the total strength of afferent synapses converging onto a cortical neurone. We show that this model implies an influence of visual experience on the spacing of ocular dominance columns and provides a conceptually simple explanation for the emergence of larger sized columns in squinting animals. Assuming that during development cortical neurones become active in local groups, which we call co-activated cortical domains (CCDs), ocular dominance segregation is controlled by the size of these groups: (1) Size and spacing of ocular dominance columns are proportional to the size sigma of CCDs. (2) There is a critical size sigma* of CCDs such that ocular dominance columns form if sigma sigma*. This critical size of CCDs is determined by the correlation functions of activity patterns in the two eyes and specifies the influence of experience on ocular dominance segregation. We show that sigma* is larger with squint than with normal visual experience. Since experimental evidence indicates that the size of CCDs decreases during development, ocular dominance columns are predicted to form earlier and with a larger spacing in squinters compared to normal animals.

Animals↗