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.

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.

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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.