Biomedical subjects

Lars Schwabe

Publications and source records attributed to Lars Schwabe.

8 recordsLinked to original sources

PubMed2006

Cardiovascular reactivity to mental stress is not affected by alpha2-adrenoreceptor activation or inhibition.

RATIONALE: It has been postulated that cardiovascular reactivity to mental stress varies with tonic central sympathetic nervous system activity, but pharmacological evidence is missing. OBJECTIVE: To test whether modulation of central sympathetic nervous system activity by alpha2-adrenergic agonism and antagonism affects cardiovascular reactivity to mental stress. MATERIALS AND METHODS: On three five-stepped dose/concentration-response study days, 12 healthy male volunteers received intravenous infusions of dexmedetomidine (alpha2-agonist, target plasma concentrations: 0.04-0.32 ng/ml), yohimbine (alpha2-antagonist, doses: 0.016-0.125 mg/kg), and placebo, respectively. During each dose step, subjects performed a 5-Choice Reaction Time Task (CRTT) and a Paced Auditory Serial Addition Task (PASAT) to induce moderate mental stress. Prestress baseline, as well as stress-induced responses of heart rate, and noninvasive finger arterial blood pressure (Finapres) were assessed. RESULTS: Prestress baseline heart rate and blood pressure decreased with increasing doses of dexmedetomidine and increased with increasing doses of yohimbine. However, dexmedetomidine and yohimbine did not affect stress-induced heart-rate and blood-pressure changes. CONCLUSIONS: Cardiovascular reactivity to mental stress is not related to pharmacologically manipulated tonic central sympathetic nervous system activity by alpha2-adrenergic agonists and antagonists. These results do not support the assumption that cardiovascular reactivity is an index of tonic central sympathetic nervous system activity.

Adrenergic alpha-2 Receptor Agonists↗

PubMed2006

The role of feedback in shaping the extra-classical receptive field of cortical neurons: a recurrent network model.

The responses of neurons in sensory cortices are affected by the spatial context within which stimuli are embedded. In the primary visual cortex (V1), orientation-selective responses to stimuli in the receptive field (RF) center are suppressed by similarly oriented stimuli in the RF surround. Surround suppression, a likely neural correlate of perceptual figure-ground segregation, is traditionally thought to be generated within V1 by long-range horizontal connections. Recently however, it has been shown that these connections are too short and too slow to mediate fast suppression from distant regions of the RF surround. We use an anatomically and physiologically constrained recurrent network model of macaque V1 to show how interareal feedback connections, which are faster and longer-range than horizontal connections, can generate "far" surround suppression. We provide a novel solution to the puzzle of how surround suppression can arise from excitatory feedback axons contacting predominantly excitatory neurons in V1. The basic mechanism involves divergent feedback connections from the far surround targeting pyramidal neurons sending monosynaptic horizontal connections to excitatory and inhibitory neurons in the RF center. One of several predictions of our model is that the "suppressive far surround" is not always suppressive, but can facilitate the response of the RF center, depending on the amount of excitatory drive to the local inhibitors. Our model provides a general mechanism of how top-down feedback signals directly contribute to generating cortical neuron responses to simple sensory stimuli.

Action Potentials↗

PubMed2005

Learning top-down gain control of feature selectivity in a recurrent network model of a visual cortical area.

We propose that the effects of attentional top-down modulations observed in the visual cortex reflect the simple strategy of strengthening currently relevant pathways in a task-dependent manner. To exemplify this idea, we set up a network model of a visual area and simulate the learning of a context-dependent 'go/no-go'-task. The model learns top-down gain-modulations of sensory representations based on reinforcements received from the environment. We also discuss how this idea relates to alternative interpretations like optimal coding hypotheses.

Adaptation, Physiological↗

PubMed2005

Adaptivity of tuning functions in a generic recurrent network model of a cortical hypercolumn.

The representation of orientation information in the adult visual cortex is plastic as exemplified by phenomena such as perceptual learning or attention. Although these phenomena operate on different time scales and give rise to different changes in the response properties of neurons, both lead to an improvement in visual discrimination or detection tasks. If, however, optimal performance is indeed the goal, the question arises as to why the changes in neuronal response properties are so different. Here, we hypothesize that these differences arise naturally if optimal performance is achieved by means of different mechanisms. To evaluate this hypothesis, we set up a recurrent network model of a visual cortical hypercolumn and asked how each of four different parameter sets (strength of afferent and recurrent synapses, neuronal gains, and additive background inputs) must be changed to optimally improve the encoding accuracy of a particular set of visual stimuli. We find that the predicted changes in the population responses and the tuning functions were different for each set of parameters, hence were strongly dependent on the plasticity mechanism that was operative. An optimal change in the strength of the recurrent connections, for example, led to changes in the response properties that are similar to the changes observed in perceptual learning experiments. An optimal change in the neuronal gains led to changes mimicking neural effects of attention. Assuming the validity of the optimal encoding hypothesis, these model predictions can be used to disentangle the mechanisms of perceptual learning, attention, and other adaptation phenomena.

Animals↗

PubMed2005

Invariant computations in local cortical networks with balanced excitation and inhibition.

Cortical computations critically involve local neuronal circuits. The computations are often invariant across a cortical area yet are carried out by networks that can vary widely within an area according to its functional architecture. Here we demonstrate a mechanism by which orientation selectivity is computed invariantly in cat primary visual cortex across an orientation preference map that provides a wide diversity of local circuits. Visually evoked excitatory and inhibitory synaptic conductances are balanced exquisitely in cortical neurons and thus keep the spike response sharply tuned at all map locations. This functional balance derives from spatially isotropic local connectivity of both excitatory and inhibitory cells. Modeling results demonstrate that such covariation is a signature of recurrent rather than purely feed-forward processing and that the observed isotropic local circuit is sufficient to generate invariant spike tuning.

Action Potentials↗

PubMed2003

Modeling the adaptive visual system: a survey of principled approaches.

Modeling the visual system can be done at multiple levels of description ranging from computer simulations of detailed biophysical models to firing rate and so-called 'black-box' models. Re-introducing David Marr's analysis levels for the visual system, we motivate the use of more abstract models in order to answer the question of what the visual system is computing. The approaches we selected to review in this article concentrate on modeling the changes of sensory representations. The considered time-scales, range from the developmental time-scale of receptive field formation to fast transient neuronal dynamics during a single stimulus presentation. Common to all approaches is their focus on providing functional interpretations, instead of 'only' explanations in terms of mechanisms. Although the concrete approaches can be distinguished along different lines, a common theme is emerging which may qualify as a paradigm for providing functional interpretations for changes of receptive field properties, i.e. the dynamic adjustment of sensory representations to varying external or internal conditions.

Adaptation, Physiological↗

PubMed2002

Rapid adaptation to internal states as a coding strategy in visual cortex?

Adaptation is a prominent feature of biological neuronal systems. A common interpretation of adaptation in terms of function is that it provides flexibility for a neuronal system to perform well under varying external conditions, for example by adjusting the input/output relation of a sensory system with reference to the ensemble of stimuli the organism currently perceives. This interpretation, however, only applies if the time-scale of adaptation is slower than the time-scale at which the environment changes. Experimentally it is observed, however, that adaptation can be very rapid. Spike-frequency adaptation of cortical neurons, for example, occurs on a time-scale of approximately 100 ms. Here we show that those rapid adaptation processes can also be understood within the framework of information theory. We start with the hypothesis that neuronal codes are designed to optimize the information a neuronal representation conveys about an input stimulus for any increasing time window beginning with stimulus onset, and we show that this implies a rapid adaptation of the neuronal code on the time-scale of stimulus presentation. Adaptation, however, does not occur because the state of the environment changes. Rather it is a reaction to changes of the organisms own internal state, e.g. the level of noise in the neuronal representation. We apply this approach to a model of an orientation hypercolumn in the primary visual cortex, and predict that inter-columnar interactions should adapt on the time-scale of a typical fixation period ( approximately 300 ms).

Adaptation, Psychological↗

PubMedDate not supplied

Rapid adaptation and efficient coding.

Rapid adaptation is a prominent feature of biological neuronal systems. From a functional perspective the adaptation of neuronal properties, namely the input-output relation of sensory neurons, is usually interpreted as an adaptation of the sensory system to changing environments as characterized by their stimulus statistics. Here we argue that this interpretation is only applicable as long as the adaptation processes are slower than the time-scale at which the stimulus statistics change. We present a definition of optimality of a neuronal code which still captures the idea of efficient coding, but which can also explain rapid adaptation without referring to an adaptation to different sensory environments. Finally, we apply our new idea to a simple model of an orientation hypercolumn in the primary visual cortex and predict that the interactions between orientation columns should adapt at the time-scale of a single stimulus presentation.

Adaptation, Biological↗