Lars Schwabe

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.

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.

Preventing the Stress-Induced Shift from Goal-Directed to Habit Action with a β-Adrenergic Antagonist

Stress modulates instrumental action in favor of habit processes that encode the association between a response and preceding stimuli and at the expense of goal-directed processes that learn the association between an action and the motivational value of the outcome. Here, we asked whether this stress-induced shift from goal-directed to habit action is dependent on noradrenergic activation and may therefore be blocked by a β-adrenoceptor antagonist. To this end, healthy men and women were administered a placebo or the β-adrenoceptor antagonist propranolol before they underwent a stress or a control procedure. Shortly after the stress or control procedure, participants were trained in two instrumental actions that led to two distinct food outcomes. After training, one of the food outcomes was selectively devalued by feeding participants to satiety with that food. A subsequent extinction test indicated whether instrumental behavior was goal-directed or habitual. As expected, stress after placebo rendered participants behavior insensitive to the change in the value of the outcome and thus habitual. After propranolol intake, however, stressed participants behaved, same as controls, goal-directed, suggesting that propranolol blocked the stress-induced bias toward habit behavior. Our findings show that the shift from goal-directed to habitual control of instrumental action under stress necessitates noradrenergic activation and could have important clinical implications, particularly for addictive disorders.

The limits of agency in walking humans

An important principle of human ethics is that individuals are not responsible for actions performed when unconscious. Recent research found that the generation of an action and the building of a conscious experience of that action (agency) are distinct processes and crucial mechanisms for self-consciousness. Yet, previous agency studies have focussed on actions of a finger or hand. Here, we investigate how agents consciously monitor actions of the entire body in space during locomotion. This was motivated by previous work revealing that (1) a fundamental aspect of self-consciousness concerns a single and coherent representation of the entire spatially situated body and (2) clinical instances of human behaviour without consciousness occur in rare neurological conditions such as sleepwalking or epileptic nocturnal wandering. Merging techniques from virtual reality, full-body tracking, and cognitive science of conscious action monitoring, we report experimental data about consciousness during locomotion in healthy participants. We find that agents consciously monitor the location of their entire body and its locomotion only with low precision and report that while precision remains low it can be systematically modulated in several experimental conditions. This shows that conscious action monitoring in locomoting agents can be studied in a fine-grained manner. We argue that the study of the mechanisms of agency for a persons full body may help to refine our scientific criteria of self-hood and discuss sleepwalking and related conditions as alterations in neural systems encoding motor awareness in walking humans.

Cognitive neuroscience of ownership and agency

Keywords: Awareness ; Body Image ; Consciousness ; Self Concept Note: Comment on: Conscious Cogn.;16(3):645-60 Reference EPFL-ARTICLE-154872doi:10.1016/j.concog.2007.07.007View record in Web of Science Record created on 2010-11-16, modified on 2017-05-12

The vestibular component in out-of-body experiences: a computational approach

Neurological evidence suggests that disturbed vestibular processing may play a key role in triggering out-of-body experiences (OBEs). Little is known about the brain mechanisms during such pathological conditions, despite recent experimental evidence that the scientific study of such experiences may facilitate the development of neurobiological models of a crucial aspect of self-consciousness: embodied self-location. Here we apply Bayesian modeling to vestibular processing and show that OBEs and the reported illusory changes of self-location and translation can be explained as the result of a mislead Bayesian inference, in the sense that ambiguous bottom-up signals from the vestibular otholiths in the supine body position are integrated with a top-down prior for the upright body position, which we measure during natural head movements. Our findings have relevance for self-location and translation under normal conditions and suggest novel ways to induce and study experimentally both aspects of bodily self-consciousness in healthy subjects.

The timing of temporoparietal and frontal activations during mental own body transformations from different visuospatial perspectives.

The perspective from where the world is perceived is an important aspect of the bodily self and may break down in neurological conditions such as out‐of‐body experiences (OBEs). These striking disturbances are characterized by disembodiment, an external perspective and have been observed after temporoparietal damage. Using mental own body imagery, recent neuroimaging work has linked perspectival changes to the temporoparietal cortex. Because the disembodied perspective during OBEs is elevated in the majority of cases, we tested whether an elevated perspective will interfere with such temporoparietal mechanisms mental own body imagery. We designed stimuli of life‐sized humans rotated around the vertical axis and rendered as if viewed from three different perspectives: elevated, lowered, and normal. Reaction times (RTs) in an own body transformation task, but not the control condition, were dependent on the rotation angle. Furthermore, RTs were shorter for the elevated as compared with the normal or lowered perspective. Using high‐density EEG and evoked potential (EP) mapping, we found a bilateral temporoparietal and frontal activation at ≈330–420 ms after stimulus onset that was dependent on the rotation angle, but not on the perspective. This activation was also found in response‐locked EPs. In the time period ≈210–330 ms we found a temporally distinct posterior temporal activation with its duration being dependent on the perspective, but not the rotation angle. Collectively, the present findings suggest that temporoparietal and frontal as well as posterior temporal activations and their timing are crucial neuronal correlates of the bodily self as studied by mental imagery. Hum Brain Mapp, 2009.

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.

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 Marrs 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.

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.