Toward Self-Referential Autonomous Learning of Object and Situation Models.

Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach.

Structural synaptic plasticity has high memory capacity and can explain graded amnesia, catastrophic forgetting, and the spacing effect.

Although already William James and, more explicitly, Donald Hebb's theory of cell assemblies have suggested that activity-dependent rewiring of neuronal networks is the substrate of learning and memory, over the last six decades most theoretical work on memory has focused on plasticity of existing synapses in prewired networks. Research in the last decade has emphasized that structural modification of synaptic connectivity is common in the adult brain and tightly correlated with learning and memory. Here we present a parsimonious computational model for learning by structural plasticity. The basic modeling units are "potential synapses" defined as locations in the network where synapses can potentially grow to connect two neurons. This model generalizes well-known previous models for associative learning based on weight plasticity. Therefore, existing theory can be applied to analyze how many memories and how much information structural plasticity can store in a synapse. Surprisingly, we find that structural plasticity largely outperforms weight plasticity and can achieve a much higher storage capacity per synapse. The effect of structural plasticity on the structure of sparsely connected networks is quite intuitive: Structural plasticity increases the "effectual network connectivity", that is, the network wiring that specifically supports storage and recall of the memories. Further, this model of structural plasticity produces gradients of effectual connectivity in the course of learning, thereby explaining various cognitive phenomena including graded amnesia, catastrophic forgetting, and the spacing effect.

Auditory event-related response in visual cortex modulates subsequent visual responses in humans.

Growing evidence from electrophysiological data in animal and human studies suggests that multisensory interaction is not exclusively a higher-order process, but also takes place in primary sensory cortices. Such early multisensory interaction is thought to be mediated by means of phase resetting. The presentation of a stimulus to one sensory modality resets the phase of ongoing oscillations in another modality such that processing in the latter modality is modulated. In humans, evidence for such a mechanism is still sparse. In the current study, the influence of an auditory stimulus on visual processing was investigated by measuring the electroencephalogram (EEG) and behavioral responses of humans to visual, auditory, and audiovisual stimulation with varying stimulus-onset asynchrony (SOA). We observed three distinct oscillatory EEG responses in our data. An initial gamma-band response around 50 Hz was followed by a beta-band response around 25 Hz, and a theta response around 6 Hz. The latter was enhanced in response to cross-modal stimuli as compared to either unimodal stimuli. Interestingly, the beta response to unimodal auditory stimuli was dominant in electrodes over visual areas. The SOA between auditory and visual stimuli--albeit not consciously perceived--had a modulatory impact on the multisensory evoked beta-band responses; i.e., the amplitude depended on SOA in a sinusoidal fashion, suggesting a phase reset. These findings further support the notion that parameters of brain oscillations such as amplitude and phase are essential predictors of subsequent brain responses and might be one of the mechanisms underlying multisensory integration.

Gamma in motion: pattern reversal elicits stronger gamma-band responses than motion.

Previous studies showed higher gamma-band responses (GBRs, ≈40 Hz) of the electroencephalogram (EEG) for moving compared to stationary stimuli. However, it is unclear whether this modulation by motion reflects a special responsiveness of the GBR to the stimulus feature "motion," or whether GBR enhancements of similar magnitude can be elicited also by a salient change within a static stimulus that does not include motion. Therefore, we measured the EEG of healthy subjects watching stationary square wave gratings of high contrast that either started to move or reversed their black and white pattern shortly after their onset. The strong contrast change of the pattern reversal represented a salient but motionless change within the grating that was compared to the onset of the stationary grating and the motion onset. Induced and evoked GBRs were analyzed for all three display conditions. In order to assess the influence of fixational eye movements on the induced GBRs, we also examined the time courses of microsaccade rates during the three display conditions. Amplitudes of both evoked and induced GBRs were stronger for pattern reversal than for motion onset. There was no significant amplitude difference between the onsets of the stationary and moving gratings. However, mean frequencies of the induced GBR were ~10 Hz higher in response to the onsets of moving compared to stationary gratings. Furthermore, the modulations of the induced GBR did not parallel the modulations of microsaccade rate, indicating that our induced GBRs reflect neuronal processes. These results suggest that, within the gamma-band range, the encoding of moving gratings in early visual cortex is primarily based on an upward frequency shift, whereas contrast changes within static gratings are reflected by amplitude enhancement.

Cortext: a columnar model of bottom-up and top-down processing in the neocortex.

Experimental data suggests that a first hypothesis about the content of a complex visual scene is available as early as 150 ms after stimulus presentation. Other evidence suggests that recognition in the visual cortex of mammals is a bidirectional, often top-down driven process. Here, we present a spiking neural network model that demonstrates how the cortex can use both strategies: Faced with a new stimulus, the cortex first tries to catch the gist of the scene. The gist is then fed back as global hypothesis to influence and redirect further bottom-up processing. We propose that these two modes of processing are carried out in different layers of the cortex. A cortical column may, thus, be primarily defined by the specific connectivity that links neurons in different layers into a functional circuit. Given an input, our model generates an initial hypothesis after only a few milliseconds. The first wave of action potentials traveling up the hierarchy activates representations of features and feature combinations. In most cases, the correct feature representation is activated strongest and precedes all other candidates with millisecond precision. Thus, our model codes the reliability of a response in the relative latency of spikes. In the subsequent refinement stage where high-level activity modulates lower stages, this activation dominance is propagated back, influencing its own afferent activity to establish a unique decision. Thus, top-down influence de-activates representations that have contributed to the initial hypothesis about the current stimulus, comparable to predictive coding. Features that do not match the top-down prediction trigger an error signal that can be the basis for learning new representations.

Anticipation of natural stimuli modulates EEG dynamics: physiology and simulation.

In everyday life we often encounter situations in which we can expect a visual stimulus before we actually see it. Here, we study the impact of such stimulus anticipation on the actual response to a visual stimulus. Participants were to indicate the sex of deer and cattle on photographs of the respective animals. On some trials, participants were cued on the species of the upcoming animal whereas on other trials this was not the case. Time frequency analysis of the simultaneously recorded EEG revealed modulations by this cue stimulus in two time windows. Early [Formula: see text] spectral responses [Formula: see text] displayed strongest stimulus-locking for stimuli that were preceded by a cue if they were sufficiently large. Late [Formula: see text] responses displayed enhanced amplitudes in response to large stimuli and to stimuli that were preceded by a cue. For late responses, however, no interaction between cue and stimulus size was observed. We were able to explain these results in a simulation by prestimulus gain modulations (early response) and by decreased response thresholds (late response). Thus, it seems plausible, that stimulus anticipation results in a pretuning of local neural populations.

Time pressure modulates electrophysiological correlates of early visual processing.

BACKGROUND: Reactions to sensory events sometimes require quick responses whereas at other times they require a high degree of accuracy-usually resulting in slower responses. It is important to understand whether visual processing under different response speed requirements employs different neural mechanisms. METHODOLOGY/PRINCIPAL FINDINGS: We asked participants to classify visual patterns with different levels of detail as real-world or non-sense objects. In one condition, participants were to respond immediately, whereas in the other they responded after a delay of 1 second. As expected, participants performed more accurately in delayed response trials. This effect was pronounced for stimuli with a high level of detail. These behavioral effects were accompanied by modulations of stimulus related EEG gamma oscillations which are an electrophysiological correlate of early visual processing. In trials requiring speeded responses, early stimulus-locked oscillations discriminated real-world and non-sense objects irrespective of the level of detail. For stimuli with a higher level of detail, oscillatory power in a later time window discriminated real-world and non-sense objects irrespective of response speed requirements. CONCLUSIONS/SIGNIFICANCE: Thus, it seems plausible to assume that different response speed requirements trigger different dynamics of processing.

EEG oscillations in the gamma and alpha range respond differently to spatial frequency.

Physical properties of visual stimuli affect electrophysiological markers of perception. One important stimulus property is spatial frequency (SF). Therefore, we studied the influence of SF on human alpha (8-13 Hz) and gamma (>30 Hz) electroencephalographic (EEG) responses in a choice reaction task. Since real world images contain multiple SFs, an SF mixture was also examined. Event related potentials were modulated by SF around 80 and 300 ms. Evoked gamma responses were strongest for the low SF and the mixture stimulus; alpha responses were strongest for high SFs. The results link evoked and induced alpha and evoked gamma responses in human EEG to different modes of stimulus processing.

From perception to action: phase-locked gamma oscillations correlate with reaction times in a speeded response task.

BACKGROUND: Phase-locked gamma oscillations have so far mainly been described in relation to perceptual processes such as sensation, attention or memory matching. Due to its very short latency ( approximately 90 ms) such oscillations are a plausible candidate for very rapid integration of sensory and motor processes. RESULTS: We measured EEG in 13 healthy participants in a speeded reaction task. Participants had to press a button as fast as possible whenever a visual stimulus was presented. The stimulus was always identical and did not have to be discriminated from other possible stimuli. In trials in which the participants showed a fast response, a slow negative potential over central electrodes starting approximately 800 ms before the response and highly phase-locked gamma oscillations over central and posterior electrodes between 90 and 140 ms after the stimulus were observed. In trials in which the participants showed a slow response, no slow negative potential was observed and phase-locked gamma oscillations were significantly reduced. Furthermore, for slow response trials the phase-locked gamma oscillations were significantly delayed with respect to fast response trials. CONCLUSION: These results indicate the relevance of phase-locked gamma oscillations for very fast (not necessarily detailed) integration processes.

Evoked gamma oscillations in human scalp EEG are test-retest reliable.

OBJECTIVE: Evoked, phase-locked gamma oscillations of the electroencephalogram (EEG) have been demonstrated to be modulated by both bottom-up as well as top-down factors. However, to date the test-retest reliability of these oscillations has not been studied systematically. METHODS: We recorded EEG activity of 12 healthy volunteers in response to stimuli of different sizes. Each participant took part in two sessions separated by two weeks in time. To obtain an estimate of the reliability of evoked gamma band responses (GBRs), we compared frequency and magnitude of phase-locked EEG oscillations between sessions. RESULTS: In response to large stimuli magnitude and frequency of the evoked GBR yielded significant reliability. However, this was not the case for stimuli which were too small to evoke detectable GBRs. CONCLUSIONS: The results are in accordance with studies demonstrating a dependence of gamma oscillations on stimulus parameters. SIGNIFICANCE: The current findings suggest that using appropriate stimulation, the evoked gamma response has sufficient test-retest reliability for use in assessing clinical changes in neurophysiological status.