Multi-Variate EEG Analysis as a Novel Tool to Examine Brain Responses to Naturalistic Music Stimuli

Note onsets in music are acoustic landmarks providing auditory cues that underlie the perception of more complex phenomena such as beat, rhythm, and meter. For naturalistic ongoing sounds a detailed view on the neural representation of onset structure is hard to obtain, since, typically, stimulus-related EEG signatures are derived by averaging a high number of identical stimulus presentations. Here, we propose a novel multivariate regression-based method extracting onset-related brain responses from the ongoing EEG. We analyse EEG recordings of nine subjects who passively listened to stimuli from various sound categories encompassing simple tone sequences, full-length romantic piano pieces and natural (non-music) soundscapes. The regression approach reduces the 61-channel EEG to one time course optimally reflecting note onsets. The neural signatures derived by this procedure indeed resemble canonical onset-related ERPs, such as the N1-P2 complex. This EEG projection was then utilized to determine the Cortico-Acoustic Correlation (CACor), a measure of synchronization between EEG signal and stimulus. We demonstrate that a significant CACor (i) can be detected in an individual listener''s EEG of a single presentation of a full-length complex naturalistic music stimulus, and (ii) it co-varies with the stimuli’s average magnitudes of sharpness, spectral centroid, and rhythmic complexity. In particular, the subset of stimuli eliciting a strong CACor also produces strongly coordinated tension ratings obtained from an independent listener group in a separate behavioral experiment. Thus musical features that lead to a marked physiological reflection of tone onsets also contribute to perceived tension in music.     

Non-zero mean alpha oscillations revealed with computational model and empirical data

Ongoing oscillations and evoked responses are two main types of neuronal activity obtained with diverse electrophysiological recordings (EEG/MEG/iEEG/LFP). Although typically studied separately, they might in fact be closely related. One possibility to unite them is to demonstrate that neuronal oscillations have non-zero mean which predicts that stimulus- or task-triggered amplitude modulation of oscillations can contribute to the generation of evoked responses. We validated this mechanism using computational modelling and analysis of a large EEG data set. With a biophysical model, we indeed demonstrated that intracellular currents in the neuron are asymmetric and, consequently, the mean of alpha oscillations is non-zero. To understand the effect that neuronal currents exert on oscillatory mean, we varied several biophysical and morphological properties of neurons in the network, such as voltage-gated channel densities, length of dendrites, and intensity of incoming stimuli. For a very large range of model parameters, we observed evidence for non-zero mean of oscillations. Complimentary, we analysed empirical rest EEG recordings of 90 participants (50 young, 40 elderly) and, with spatio-spectral decomposition, detected at least one spatially-filtred oscillatory component of non-zero mean alpha oscillations in 93% of participants. In order to explain a complex relationship between the dynamics of amplitude-envelope and corresponding baseline shifts, we performed additional simulations with simple oscillators coupled with different time delays. We demonstrated that the extent of spatial synchronisation may obscure macroscopic estimation of alpha rhythm modulation while leaving baseline shifts unchanged. Overall, our results predict that amplitude modulation of neural oscillations should at least partially explain the generation of evoked responses. Therefore, inference about changes in evoked responses with respect to cognitive conditions, age or neuropathologies should be constructed while taking into account oscillatory neuronal dynamics.     

Dissociable Neural Response Signatures for Slow Amplitude and Frequency Modulation in Human Auditory Cortex

Natural auditory stimuli are characterized by slow fluctuations in amplitude and frequency. However, the degree to which the neural responses to slow amplitude modulation (AM) and frequency modulation (FM) are capable of conveying independent time-varying information, particularly with respect to speech communication, is unclear. In the current electroencephalography (EEG) study, participants listened to amplitude- and frequency-modulated narrow-band noises with a 3-Hz modulation rate, and the resulting neural responses were compared. Spectral analyses revealed similar spectral amplitude peaks for AM and FM at the stimulation frequency (3 Hz), but amplitude at the second harmonic frequency (6 Hz) was much higher for FM than for AM. Moreover, the phase delay of neural responses with respect to the full-band stimulus envelope was shorter for FM than for AM. Finally, the critical analysis involved classification of single trials as being in response to either AM or FM based on either phase or amplitude information. Time-varying phase, but not amplitude, was sufficient to accurately classify AM and FM stimuli based on single-trial neural responses. Taken together, the current results support the dissociable nature of cortical signatures of slow AM and FM. These cortical signatures potentially provide an efficient means to dissect simultaneously communicated slow temporal and spectral information in acoustic communication signals.     

Causes and consequences of stress

Stress involves real or perceived changes within an organismin the environment that activate an organism's attempts to copeby means of evolutionarily ancient neural and endocrine mechanisms.Responses to acute stressors involve catecholamines releasedin varying proportion at different sites in the sympatheticand central nervous systems. These responses may interact withand be complemented by intrinsic rythms and responses to chronicor intermittent stressors involving the hypothalamic-pituitary-adrenalaxis. Varying patterns of responses to stressors are also affectedby an animal's assessment of their prospects for successfulcoping. Subsequent central and systemic consequences of thestress response include apparent changes in affect, motivation,and cognition that can result in an altered relationship toenvironmental and social stimuli. This review will summarizerecent developments in our understanding of the causes and consequencesof stress. Special problems that need to be explored involvethe manner in which ensembles of adaptive responses are assembled,how autonomic and neurohormonal reflexes of the stress responsecome under the influence of environmental stimuli, and how somespecific aspects of the stress response may be integrated intothe life history of a species.     

A field-theoretic approach to understanding scale-free neocortical dynamics

A mesoscopic field-theoretic approach is compared with neural network and brain imaging approaches to understanding brain dynamics. Analysis of high spatiotemporal resolution rabbit electroencephalogram (EEG) reveals neural fields in the form of spatial patterns in amplitude (AM) and phase (PM) modulation of gamma and beta carrier waves that serve to classify EEGs from trials with differing conditioned stimuli (CS+/−). Paleocortex exemplified by olfactory EEG has one AM–PM pattern at a time that forms by an input-dependent phase transition. Neocortex shows multiple overlapping AM–PM patterns before and during presentation of CSs. Modeling suggests that neocortex is stabilized in a scale-free state of self-organized criticality, enabling cooperative domains to form virtually instantaneously by phase transitions ranging in size from a few hypercolumns to an entire hemisphere. Self-organized local domains precede formation of global domains that supervene and contribute global modulations to local domains. This mechanism is proposed to explain Gestalt formation in perception.     

Topography of alpha and theta oscillatory responses upon auditory and visual stimuli in humans

Brain resonance phenomena and induced rhythms in the brain recently gained importance in electroencephalographic, magnetoencephalographic and cellular studies (Ba\c sar and Bullock 1992). It was hypothesized that evoked potentials are superpositions of induced rhythms caused by resonance phenomena in neural populations (Ba\c sar et al. 1992). According to Ba\c sar (1972), such resonance phenomena are reflected in the main peaks of the amplitude frequency characteristics computed from EEG responses. The present study is based on a frequency domain approach for the evaluation of topography- and modality-dependent properties of oscillatory brain responses. EEG and evoked potentials were recorded from vertex, parietal and occipital scalp locations in 24 volunteers. Two combined methods were applied: (1) amplitude frequency characteristics were computed from the transient evoked responses, and (2) frequency components of the transient responses were obtained by adaptive digital filtering. Our main goal was to investigate theta (4--7 Hz) and alpha (8--15 Hz) response components. (1) Amplitude frequency characteristics. Auditory stimuli elicited theta-alpha compound responses in the 4--11 Hz frequency band (e.g. typical peaking frequency around 7 Hz for vertex recordings). Visual stimuli elicited alpha responses (e.g. typical peaking frequency for vertex recordings around 9--12 Hz). Frequency maxima for visual stimuli thus had main peaks at higher frequency values than frequency maxima for auditory stimuli. (2) Digital filtering confirmed these results: for vertex recordings, theta vs. alpha response amplitudes were 9 vs 6 for auditory stimuli and 5 vs 5 for visual stimuli, thus confirming a shift towards higher frequencies, i.e. a more prominent contribution of the alpha range, in the case of visual stimulation. We hypothesize that these properties might reflect site- and modality-specific features of stimulus encoding in the brain in which resonance properties of neuron populations are involved. Furthermore we emphasize the utility of the systems theory approach for a better understanding of brain function by means of EPs. Received: 25 February 1994 / Accepted in revised form: 5 August 1994     

Neurophysiological Age Differences During Task-Performance in a Stereoscopic Virtual Environment

In today’s society, there is an increasing number of workplaces in virtual environments (VE). But, there are only a few reports dealing with occupational health issues or age effects. The question arises how VR generally interferes with cognitive processes. This interference might have relevant implications for workability and work-efficiency in virtual environments. Event-related potentials are known to reflect different stages of stimulus reception, evaluation, and response. We have established an electroencephalographic (EEG) monitoring, focussing on event-related potentials (N100; mismatch negativity, i.e., MMN) to obtain access to attention dependent and pre-attentive processing of sensory stimuli applied in VE. The MMN is known to be correlated with the ability of subjects to react to an unexpected event. The aim of the present study was to investigate cognitive responses to distracting auditory stimuli in two different age groups in a virtual environment (VE) and in a real environment (“real reality”), and to compare characteristic neurophysiological response patterns. Data show that stimulus detection as given by the N100 amplitude and latency does not differ in both age groups and task conditions. In contrast, the pre-attentive processing as given by the MMN is altered in the VR such as the non-VR condition in an age-related manner. A relevant finding of the present study was that the age related differences seen in the non-VR condition were not strengthened in VR.     

Dopaminergic modulation of the P50 auditory-evoked potential in a computer model of the CA3 region of the hippocampus: its relationship to sensory gating in schizophrenia

 We modeled the neuronal circuits that may underlie a sensory-processing deficit associated with schizophrenia. Schizophrenic patients have small P50 auditory-evoked responses to click stimuli compared to normal subjects. The P50 auditory-evoked response is a positive waveform recorded in the EEG approximately 50 ms after the auditory click stimulus. In addition to relatively small amplitudes, schizophrenic patients do not gate or suppress the P50 auditory-evoked response to the second of two paired-click stimuli spaced 0.5 s apart. Neuropleptic medication, which decreases dopaminergic neuronal transmission, increases the amplitude of the P50 auditory-evoked response but does not improve gating. Normal subjects have large P50 auditory-evoked responses to click stimuli when compared to unmedicated schizophrenic patients, and they gate their response to paired click stimuli or have smaller P50 auditory-evoked response amplitudes to the second of two click stimuli spaced 0.5 s apart. Schizophrenic patients do not gate and have similar response amplitudes to both clicks. We hypothesized that the small amplitudes of unmedicated schizophrenic subjects were due to a state of occlusion whereby excessive background noise in local circuits reduced the ability of cells to respond synchronously to sensory input, thereby reducing the amplitude of the P50 waveform in the EEG. Because the P50 auditory-evoked potential amplitudes increased with neuroleptic medication, which reduces dopaminergic neuronal transmission, we hypothesized a role for dopamine in modulating the signal-to-noise (S/N) in the local circuits responsible for sensory gating. To test the hypothesis that modulation of the S/N ratio reduces sensory gating, we developed a model of the effects of dopaminergic neuronal transmission that modulates the S/N in neuronal circuits. The model uses the biologically relevant computer model of the CA3 region of the hippocampus developed in the companion paper [Moxon et al. (2003) Biol Cybern, this volume]. Modified Hebb cell assemblies represented the response of the network to the click stimulus. The results of our model showed that excessive dopaminergic input impaired the ability of cells to respond synchronously to sensory input, which reduced the amplitudes of the P50 evoked responses. Received: 3 December 2001 / Accepted: 23 October 2002 / Published online: 28 February 2003 Correspondence to: K.A. Moxon (e-mail: karen.moxon@drexel.edu, Tel.: +1-215-8951959, Fax: +1-215-8954983) Supported by USPHS, MH01245 & MH58414, MH-01121, and research grants from the Department of Veterans Affairs and the National Alliance for Research on Schizophrenia and Depression.     

The Perception of Dynamic and Static Facial Expressions of Happiness and Disgust Investigated by ERPs and fMRI Constrained Source Analysis

A recent functional magnetic resonance imaging (fMRI) study by our group demonstrated that dynamic emotional faces are more accurately recognized and evoked more widespread patterns of hemodynamic brain responses than static emotional faces. Based on this experimental design, the present study aimed at investigating the spatio-temporal processing of static and dynamic emotional facial expressions in 19 healthy women by means of multi-channel electroencephalography (EEG), event-related potentials (ERP) and fMRI-constrained regional source analyses. ERP analysis showed an increased amplitude of the LPP (late posterior positivity) over centro-parietal regions for static facial expressions of disgust compared to neutral faces. In addition, the LPP was more widespread and temporally prolonged for dynamic compared to static faces of disgust and happiness. fMRI constrained source analysis on static emotional face stimuli indicated the spatio-temporal modulation of predominantly posterior regional brain activation related to the visual processing stream for both emotional valences when compared to the neutral condition in the fusiform gyrus. The spatio-temporal processing of dynamic stimuli yielded enhanced source activity for emotional compared to neutral conditions in temporal (e.g., fusiform gyrus), and frontal regions (e.g., ventromedial prefrontal cortex, medial and inferior frontal cortex) in early and again in later time windows. The present data support the view that dynamic facial displays trigger more information reflected in complex neural networks, in particular because of their changing features potentially triggering sustained activation related to a continuing evaluation of those faces. A combined fMRI and EEG approach thus provides an advanced insight to the spatio-temporal characteristics of emotional face processing, by also revealing additional neural generators, not identifiable by the only use of an fMRI approach.     

Travelling waves and EEG patterns during epileptic seizure: analysis with an integrate-and-fire neural network

Epilepsy is characterized by paradoxical patterns of neural activity. They may cause different types of electroencephalogram (EEG), which dynamically change in shape and frequency content during the temporal evolution of seizure. It is generally assumed that these epileptic patterns may originate in a network of strongly interconnected neurons, when excitation dominates over inhibition. The aim of this work is to use a neural network composed of 50 x 50 integrate-and-fire neurons to analyse which parameter alterations, at the level of synapse topology, may induce network instability and epileptic-like discharges, and to study the corresponding spatio-temporal characteristics of electrical activity in the network. We assume that a small group of central neurons is stimulated by a depolarizing current (epileptic focus) and that neurons are connected via a Mexican-hat topology of synapses. A signal representative of cortical EEG (ECoG) is simulated by summing the membrane potential changes of all neurons. A sensitivity analysis on the parameters describing the synapse topology shows that an increase in the strength and in spatial extension of excitatory vs. inhibitory synapses may cause the occurrence of travelling waves, which propagate along the network. These propagating waves may cause EEG patterns with different shape and frequency, depending on the particular parameter set used during the simulations. The resulting model EEG signals include irregular rhythms with large amplitude and a wide frequency content, low-amplitude high-frequency rapid discharges, isolated or repeated bursts, and low-frequency quasi-sinusoidal patterns. A slow progressive temporal variation in a single parameter may cause the transition from one pattern to another, thus generating a highly non-stationary signal which resembles that observed during ECoG measurements. These results may help to elucidate the mechanisms at the basis of some epileptic discharges, and to relate rapid changes in EEG patterns with the underlying alterations at the network level.     

Responses of recurrent nets of asymmetric ON and OFF cells

A neural field model of ON and OFF cells with all-to-all inhibitory feedback is investigated. External spatiotemporal stimuli drive the ON and OFF cells with, respectively, direct and inverted polarity. The dynamic differences between networks built of ON and OFF cells (“ON/OFF”) and those having only ON cells (“ON/ON”) are described for the general case where ON and OFF cells can have different spontaneous firing rates; this asymmetric case is generic. Neural responses to nonhomogeneous static and time-periodic inputs are analyzed in regimes close to and away from self-oscillation. Static stimuli can cause oscillatory behavior for certain asymmetry levels. Time-periodic stimuli expose dynamical differences between ON/OFF and ON/ON nets. Outside the stimulated region, we show that ON/OFF nets exhibit frequency doubling, while ON/ON nets cannot. On the other hand, ON/ON networks show antiphase responses between stimulated and unstimulated regions, an effect that does not rely on specific receptive field circuitry. An analysis of the resonance properties of both net types reveals that ON/OFF nets exhibit larger response amplitude. Numerical simulations of the neural field models agree with theoretical predictions for localized static and time-periodic forcing. This is also the case for simulations of a network of noisy integrate-and-fire neurons. We finally discuss the application of the model to the electrosensory system and to frequency-doubling effects in retina.     

Are Stripes Beneficial? Dazzle Camouflage Influences Perceived Speed and Hit Rates

In the animal kingdom, camouflage refers to patterns that help potential prey avoid detection. Mostly camouflage is thought of as helping prey blend in with their background. In contrast, disruptive or dazzle patterns protect moving targets and have been suggested as an evolutionary force in shaping the dorsal patterns of animals. Dazzle patterns, such as stripes and zigzags, are thought to reduce the probability with which moving prey will be captured by impairing predators'' perception of speed. We investigated how different patterns of stripes (longitudinal—i.e., parallel to movement direction–and vertical–i.e., perpendicular to movement direction) affect the probability with which humans can hit moving objects and if differences in hitting probability are caused by a misperception of speed. A first experiment showed that longitudinally striped objects were hit more often than unicolored objects. However, vertically striped objects did not differ from unicolored objects. A second study examining the link between perceived speed and hitting probability showed that longitudinally and vertically striped objects were both perceived as moving faster and were hit more often than unicolored objects. In sum, our results provide evidence that striped patterns disrupt the perception of speed, which in turn influences how often objects are hit. However, the magnitude and the direction of the effects depend on additional factors such as speed and the task setup.     

Maximum decoding abilities of temporal patterns and synchronized firings: application to auditory neurons responding to click trains and amplitude modulated white noise

Simultaneous recordings of an increasing number of neurons have recently become available, but few methods have been proposed to handle this activity. Here, we extract and investigate all the possible temporal neural activity patterns based on synchronized firings of neurons recorded on multiple electrodes, or based on bursts of single-electrode activity in cat primary auditory cortex. We apply this to responses to periodic click trains or sinusoïdal amplitude modulated noise by obtaining for each pattern its temporal modulation transfer function. An algorithm that maximizes the mutual information between all patterns and stimuli subsequently leads to the identification of patterns that optimally decode modulation frequency (MF). We show that stimulus information contained in multi-electrode synchronized firing is not redundant with single-electrode firings and leads to improved efficiency of MF decoding. We also show that the combined use of firing rate and temporal codes leads to a better discrimination of the MF.     

Effect of steady-state response versus excitatory/inhibitory balance on spiking synchronization in neural networks with log-normal synaptic weight distribution

Synchronization of neural activity, especially at the gamma band, contributes to perceptual functions. In several psychiatric disorders, deficits of perceptual functions are reflected in synchronization abnormalities. Plausible cause of this impairment is an alteration in the balance between excitation and inhibition (E/I balance); a disruption in the E/I balance leads to abnormal neural interactions reminiscent of pathological states. Moreover, the local lateral excitatory-excitatory synaptic connections in the cortex exhibit excitatory postsynaptic potentials (EPSPs) that follow a log-normal amplitude distribution. This long-tailed distribution is considered an important factor for the emergence of spatiotemporal neural activity. In this context, we hypothesized that manipulating the EPSP distribution under abnormal E/I balance conditions would provide insights into psychiatric disorders characterized by deficits in perceptual functions, potentially revealing the mechanisms underlying pathological neural behaviors. In this study, we evaluated the synchronization of neural activity with external periodic stimuli in spiking neural networks in cases of both E/I balance and imbalance with or without a long-tailed EPSP amplitude distribution. The results showed that external stimuli of a high frequency lead to a decrease in the degree of synchronization with an increasing ratio of excitatory to inhibitory neurons in the presence, but not in the absence, of high-amplitude EPSPs. This monotonic reduction can be interpreted as an autonomous, strong-EPSP-dependent spiking activity selectively interfering with the responses to external stimuli. This observation is consistent with pathological findings. Thus, our modeling approach has potential to improve the understanding of the steady-state response in both healthy and pathological states.     

Neural mechanisms of tactile motion integration in somatosensory cortex

How are local motion signals integrated to form a global motion percept? We investigate the neural mechanisms of tactile motion integration by presenting tactile gratings and plaids to the fingertips of monkeys, using the tactile analogue of a visual monitor and recording the responses evoked in somatosensory cortical neurons. The perceived directions of the gratings and plaids are measured in parallel psychophysical experiments. We identify a population of somatosensory neurons that exhibit integration properties comparable to those induced by analogous visual stimuli in area MT and find that these neural responses account for the perceived direction of the stimuli across all stimulus conditions tested. The preferred direction of the neurons and the perceived direction of the stimuli can be predicted from the weighted average of the directions of the individual stimulus features, highlighting that the somatosensory system implements a vector average mechanism to compute tactile motion direction that bears striking similarities to its visual counterpart.     

Spatially Pooled Contrast Responses Predict Neural and Perceptual Similarity of Naturalistic Image Categories

The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs) in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis). Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.     

Functional significance of cortical evoked potentials

Experimental results indicating a limited and indirect dependence of the amplitudes of the first two phases of the primary response (PR) of the cat auditory cortex on the information content of the stimulus are described. The PR amplitude is slightly reduced during the action of positive acoustic stimuli. This is due, primarily, to activation of the EEG in response to positive stimuli, for a similar decrease in amplitude of the cortical PR against the background of an activated EEG is also observed to fine differential stimuli. No PRs to acoustic stimulation are found in the sensomotor cortex either before or after motor-food conditioning; they are recorded only in the auditory projection zone. The amplitude of the PRs falls regularly during fast (25–30/min) and prolonged repetitive acoustic stimulation, and the rate of fall is greater when the interval between stimuli is shortened. The appearance of PRs of high amplitude in response to infrequent stimuli of whatever quality indicates that these responses are dependent on a component of the orienting reaction. It is concluded that the role of the first two phases of the PR as an indicator of fine analysis of information by the brain is limited.I. P. Pavlov Institute of Physiology, Academy of Sciences of the USSR, Leningrad. Translated from Neirofiziologiya, Vol. 2, No. 4, pp. 423–428, July–August, 1970.     

A Generalized Framework for Quantifying the Dynamics of EEG Event-Related Desynchronization

Brains were built by evolution to react swiftly to environmental challenges. Thus, sensory stimuli must be processed ad hoc, i.e., independent—to a large extent—from the momentary brain state incidentally prevailing during stimulus occurrence. Accordingly, computational neuroscience strives to model the robust processing of stimuli in the presence of dynamical cortical states. A pivotal feature of ongoing brain activity is the regional predominance of EEG eigenrhythms, such as the occipital alpha or the pericentral mu rhythm, both peaking spectrally at 10 Hz. Here, we establish a novel generalized concept to measure event-related desynchronization (ERD), which allows one to model neural oscillatory dynamics also in the presence of dynamical cortical states. Specifically, we demonstrate that a somatosensory stimulus causes a stereotypic sequence of first an ERD and then an ensuing amplitude overshoot (event-related synchronization), which at a dynamical cortical state becomes evident only if the natural relaxation dynamics of unperturbed EEG rhythms is utilized as reference dynamics. Moreover, this computational approach also encompasses the more general notion of a “conditional ERD,” through which candidate explanatory variables can be scrutinized with regard to their possible impact on a particular oscillatory dynamics under study. Thus, the generalized ERD represents a powerful novel analysis tool for extending our understanding of inter-trial variability of evoked responses and therefore the robust processing of environmental stimuli.     

Spike-timing-dependent plasticity leads to gamma band responses in a neural network

Early gamma band responses of the human electroencephalogram have been identified as an early interface linking top-down and bottom-up processing. This was based on findings that observed strong sensitivity of this signal to stimulus size and at the same time, to processes of attention and memory. Here, we simulate these findings in a simple random network of biologically plausible spiking neurons. During a learning phase, different stimuli were presented to the network and the synaptic connections were modified according to a spike-timing-dependent plasticity learning rule. In a subsequent test phase, we stimulated the network with (i) patterns of different sizes to simulate bottom-up effects and (ii) with patterns that were or were not presented during the learning phase. The network displayed qualitatively similar behavior as early gamma band responses measured from the scalp of human subjects: there was a general increase in response strength with increasing stimulus size and stronger responses for learned stimuli. We demonstrated that within one neural architecture early gamma band responses can be modulated both by bottom-up factors and by basal learning mechanisms mediated via spike-timing-dependent plasticity.     

Perceived motion in complementary afterimages: verification of a neural network theory

Steady fixation of a regular pattern like a bar grating or concentric circles leads to a complementary afterimage at pattern offset. The afterimage has the appearance of shimmering lines that are locally orthogonal to the orientations of the inducing image. Additionally, the afterimage includes motion running parallel to the orientation of the afterimage lines. We argue that this afterimage motion supports the existence of a cue to motion that is based on the spatial organization of oriented responses. This cue was previously proposed after analysis of a neural network model of visual perception. We test predictions of the model on various types of complementary afterimage inducing stimuli. When a contrast or size gradient is included in the inducing image, the afterimage motion moves toward the higher part of the gradient, in agreement with the model. Implications of this cue for computational and neurophysiological theories of motion perception are discussed.