Biomedical subjects

Thomas Wennekers

Publications and source records attributed to Thomas Wennekers.

9 recordsLinked to original sources

PubMed2006

High-resolution multiple-unit EEG in cat auditory cortex reveals large spatio-temporal stochastic interactions.

It has been argued that information processing in the cortex is optimised with regard to certain information theoretic principles. We have, for instance, recently shown that spike-timing dependent plasticity can improve an information-theoretic measure called spatio-temporal stochastic interaction which captures how strongly a set of neurons cooperates in space and time. Systems with high stochastic interaction reveal Poisson spike trains but nonetheless occupy only a strongly reduced area in their global phase space, they reveal repetiting but complex global activation patterns, and they can be interpreted as computational systems operating on selected sets of collective patterns or "global states" in a rule-like manner. In the present work we investigate stochastic interaction in high-resolution EEG-data from cat auditory cortex. Using Kohonen maps to reduce the high-dimensional dynamics of the system, we are able to detect repetiting system states and estimate the stochastic interaction in the data, which turns out to be fairly high. This suggests an organised cooperation in the underlying neural networks which cause the data and may reflect generic intrinsic computational capabilities of the cortex.

Animals↗

PubMed2006

Language models based on Hebbian cell assemblies.

This paper demonstrates how associative neural networks as standard models for Hebbian cell assemblies can be extended to implement language processes in large-scale brain simulations. To this end the classical auto- and hetero-associative paradigms of attractor nets and synfire chains (SFCs) are combined and complemented by conditioned associations as a third principle which allows for the implementation of complex graph-like transition structures between assemblies. We show example simulations of a multiple area network for object-naming, which categorises objects in a visual hierarchy and generates different specific syntactic motor sequences ("words") in response. The formation of cell assemblies due to ongoing plasticity in a multiple area network for word learning is studied afterwards. Simulations show how assemblies can form by means of percolating activity across auditory and motor-related language areas, a process supported by rhythmic, synchronized propagating waves through the network. Simulations further reproduce differences in own EEG&MEG experiments between responses to word- versus non-word stimuli in human subjects.

Animals↗

PubMed2006

Cortical activity pattern computation.

Neural computations are modelled in various ways, but still there is no clear understanding of how the brain performs its computational tasks. This paper presents new results about analysis of neural processes in terms of activity pattern computations. It is shown that it is possible to extract from high-resolution EEG data a first order Markov approximation of a neural communication system employing pattern computations, which is significantly different from similar purely random systems. In our view this result shows that it is likely that neural activity patterns measurable at the macro-level by EEG are correlated with underlying neural computations.

Animals↗

PubMed2005

Finite state automata resulting from temporal information maximization and a temporal learning rule.

We extend Linkser's Infomax principle for feedforward neural networks to a measure for stochastic interdependence that captures spatial and temporal signal properties in recurrent systems. This measure, stochastic interaction, quantifies the Kullback-Leibler divergence of a Markov chain from a product of split chains for the single unit processes. For unconstrained Markov chains, the maximization of stochastic interaction, also called Temporal Infomax, has been previously shown to result in almost deterministic dynamics. This letter considers Temporal Infomax on constrained Markov chains, where some of the units are clamped to prescribed stochastic processes providing input to the system. Temporal Infomax in that case leads to finite state automata, either completely deterministic or weakly nondeterministic. Transitions between internal states of these systems are almost perfectly predictable given the complete current state and the input, but the activity of each single unit alone is virtually random. The results are demonstrated by means of computer simulations and confirmed analytically. It is furthermore shown numerically that Temporal Infomax leads to a high information flow from the input to internal units and that a simple temporal learning rule can approximately achieve the optimization of temporal interaction. We relate these results to experimental data concerning the correlation dynamics and functional connectivities observed in multiple electrode recordings.

Action Potentials↗

PubMed2003

Dynamical properties of strongly interacting Markov chains.

Spatial interdependences of multiple stochastic units can be suitably quantified by the Kullback-Leibler divergence of the joint probability distribution from the corresponding factorized distribution. In the present paper, a generalized measure for stochastic interaction, which also captures temporal interdependences, is analysed within the setting of Markov chains. The dynamical properties of systems with strongly interacting stochastic units are analytically studied and illustrated by computer simulations. In particular, the emergence of determinism in such systems is demonstrated.

Computer Simulation↗

PubMed2003

Pattern formation in intracortical neuronal fields.

This paper introduces a neuronal field model for both excitatory and inhibitory connections. A single integro-differential equation with delay is derived and studied at a critical point by stability analysis, which yields conditions for static periodic patterns and wave instabilities. It turns out that waves only occur below a certain threshold of the activity propagation velocity. An additional brief study exhibits increasing phase velocities of waves with decreasing slope subject to increasing activity propagation velocities, which are in accordance with experimental results. Numerical studies near and far from instability onset supplement the work.

Action Potentials↗

PubMed2002

Spatial dynamics of receptive fields in cat primary visual cortex related to the temporal structure of thalamocortical feedforward activity. Experiments and models.

We investigated how changes in the temporal firing rate of thalamocortical activity affect the spatiotemporal structure of receptive field (RF) subunits in cat primary visual cortex. Spike activity of 67 neurons (48 simple, 19 complex cells) was extracellulary recorded from area 17/18 of anesthetized and paralyzed cats. A total of 107 subfields (on/off) were mapped by applying a reverse correlation technique to the activity elicited by bright and dark rectangles flashed for 300 ms in a 20x10 grid. We found that the width of the (suprathreshold) discharge fields shrank on average by 22% during this 300-ms-long stimulus presentation time. Fifty-eight subfields (54%) shrank by more than 20% of peak width and only ten (less than 10%) showed a slight increase over time. The main size reduction took place 40-60 ms after response onset, which corresponded to the transition from transient peak firing to tonic visual activity in thalamocortical relay cells (TC). The experimentally obtained RFs were then fitted with the aid of a neural field model of the primary visual pathway. Assuming a Gaussian-shaped spatial sensitivity profile across the RF subfield width, the model allowed us to estimate the subthreshold RF (depolarization field, D-field) from the minimal discharge field (MDF). The model allowed us to test to what degree the temporal dynamics of thalamocortical activity contributes to the spatiotemporal changes of cortical RFs. To this end, we performed the fitting procedure either with a pure feedforward model or with a field model that also included intracortical feedback. Spatial and temporal parameters obtained from fits of the experimental RFs matched closely to those achieved by simulating a pure feedforward system with the field model but were not compatible with additional intracortical feedback. Thus, our results show that dot stimulation, which optimally excites thalamocortical cells, leads to a shrinkage with respect to the size of the RF subfield at the first transient response of visual cortical RFs which seems mainly due to a change in the thalamic firing pattern. In these experiments little or no influence from intracortical sources was observed, which, however, may play a role when using more complex visual stimuli.

Action Potentials↗

PubMed2002

Dynamic approximation of spatiotemporal receptive fields in nonlinear neural field models.

This article presents an approximation method to reduce the spatiotemporal behavior of localized activation peaks (also called "bumps") in non-linear neural field equations to a set of coupled ordinary differential equations (ODEs) for only the amplitudes and tuning widths of these peaks. This enables a simplified analysis of steady-state receptive fields and their stability, as well as spatiotemporal point spread functions and dynamic tuning properties. A lowest-order approximation for peak amplitudes alone shows that much of the well-studied behavior of small neural systems (e.g., the Wilson-Cowan oscillator) should carry over to localized solutions in neural fields. Full spatiotemporal response profiles can further be reconstructed from this low-dimensional approximation. The method is applied to two standard neural field models: a one-layer model with difference-of-gaussians connectivity kernel and a two-layer excitatory-inhibitory network. Similar models have been previously employed in numerical studies addressing orientation tuning of cortical simple cells. Explicit formulas for tuning properties, instabilities, and oscillation frequencies are given, and exemplary spatiotemporal response functions, reconstructed from the low-dimensional approximation, are compared with full network simulations.

Animals↗

PubMedDate not supplied

Separation of spatio-temporal receptive fields into sums of gaussian components.

Visual cortical simple cells have been experimentally shown to reveal non-trivial spatio-temporal orientation tuning functions comprising different phases of specifically tuned enhanced and suppressed activity. A recently developed analytical method based on nonlinear neural field models suggests that such space-time responses should be approximately separable into a sum of temporally amplitude modulated Gaussian spatial components. In the present work, we investigate this possibility by means of numerical fits of sums of Gaussians to response functions observed in experiments and computer simulations. Because the theory relates each single component to a particular connectivity kernel between the underlying cell classes shaping the response, the relative contribution of feedforward and cortex-intrinsical excitatory and inhibitory feedback mechanisms to single cell tuning can be approached and quantified in experimental data.

Animals↗