Subthreshold membrane depolarization as memory trace for perceptual learning

Experience-dependent synaptic plasticity characterizes the adaptable brain and is believed to be the cellular substrate for perceptual learning. A chemical agent such as gamma-aminobutyric acid (GABA) is known to affect synaptic alteration, perhaps gating perceptual learning. We examined whether and how ambient (extrasynaptic) GABA affects experience-dependent synaptic alteration. A cortical neural network model was simulated. Transporters on GABAergic interneurons regulate ambient GABA levels around their axonal target neurons by removing GABA from (forward transport) or releasing it into (reverse transport) the extracellular space. The ambient GABA provides neurons with tonic inhibitory currents by activating extrasynaptic GABA(a) receptors. During repeated exposures to the same stimulus, we modified the synaptic connection strength between principal cells in a spike-timing-dependent manner. This modulated the activity of GABAergic interneurons, and reduced or augmented ambient GABA concentration. Reduction in ambient GABA concentration led to slight depolarization (less than several millivolts) in ongoing-spontaneous membrane potential. This was a subthreshold neuronal behavior because ongoing-spontaneous spiking activity remained almost unchanged. The ongoing-spontaneous subthreshold depolarization improved a suprathreshold neuronal response. If the stimulus was long absent for perceptual learning, augmentation of ambient GABA concentration took place and the ongoing-spontaneous subthreshold depolarization was depressed. We suggest that a perceptual memory trace could be left in neuronal circuitry as an ongoing-spontaneous subthreshold membrane depolarization, which would allow that memory to be accessed easily afterward, whereas a trace of a memory that has not recently been retrieved fades away when the ongoing-spontaneous subthreshold membrane depolarization built by previous perceptual learning is depressed. This would lead that memory to be accessed with some difficulty. In the brain, ambient GABA, whose level could be regulated by transporter may have an important role in leaving memory trace for perceptual learning.     

Coherent ongoing subthreshold state of a cortical neural network regulated by slow- and fast-spiking interneurons

Although details of cortical interneurons in anatomy and physiology have been well understood, little is known about how they contribute to ongoing spontaneous neuronal activity that could have a great impact on subsequent neuronal information processing. Simulating a cortical neural network model of an early sensory area, we investigated whether and how two distinct types of inhibitory interneurons, or fast-spiking interneurons with narrow axonal arbors and slow-spiking interneurons with wide axonal arbors, have a spatiotemporal influence on the ongoing activity of principal cells and subsequent cognitive information processing. In the model, dynamic cell assemblies, or population activation of principal cells, expressed information about specific sensory features. Within cell assemblies, fast-spiking interneurons give a feedback inhibitory effect on principal cells. Between cell assemblies, slow-spiking interneurons give a lateral inhibitory effect on principal cells. Here, we show that these interneurons keep the network at a subthreshold level for action potential generation under the ongoing state, by which the reaction time of principal cells to sensory stimulation could be accelerated. We suggest that the best timing of inhibition mediated by fast-spiking interneurons and slow-spiking interneurons allows the network to remain near threshold for rapid responses to input.     

Enhanced sound perception by widespread-onset neuronal responses in auditory cortex

Accumulating evidence suggests that auditory cortical neurons exhibit widespread-onset responses and restricted sustained responses to sound stimuli. When a sound stimulus is presented to a subject, the auditory cortex first responds with transient discharges across a relatively large population of neurons, showing widespread-onset responses. As time passes, the activation becomes restricted to a small population of neurons that are preferentially driven by the stimulus, showing restricted sustained responses. The sustained responses are considered to have a role in expressing information about the stimulus, but it remains to be seen what roles the widespread-onset responses have in auditory information processing. We carried out numerical simulations of a neural network model for a lateral belt area of auditory cortex. In the network, dynamic cell assemblies expressed information about auditory sounds. Lateral excitatory and inhibitory connections were made between cell assemblies, respectively, by direct and indirect projections via interneurons. Widespread-onset neuronal responses to sound stimuli (bandpassed noises) took place over the network if lateral excitation preceded lateral inhibition, making a time widow for the onset responses. The widespread-onset responses contributed to the accelerating reaction time of neurons to sensory stimulation. Lateral interaction among dynamic cell assemblies was essential for maintaining ongoing membrane potentials near thresholds for action potential generation, thereby accelerating reaction time to subsequent sensory input as well. We suggest that the widespread-onset neuronal responses and the ongoing subthreshold cortical state, for which the coordination of lateral synaptic interaction among dissimilar cell assemblies is essential, may work together in order for the auditory cortex to quickly detect the sudden occurrence of sounds from the external environment.     

Roles of coherent ongoing oscillations among dynamic cell assemblies in object perception

I constructed a cortical neural network model and investigated possible roles of coherent ongoing oscillations in membrane potentials of neurons in object perception. The model has a hierarchical structure consisting of two lower networks and one higher network that are reciprocally connected via divergent/convergent projections. Information about features and their relationships (or objects) is encoded by the population activities of neurons (or dynamic cell assemblies) of the lower networks and the higher network, respectively. The ongoing state of the network is expressed by 'random itinerancy' among these dynamic cell assemblies. Under the ongoing state, the dynamic cell assemblies belonging to the same object are transiently linked across the networks and coherently oscillate at lower frequencies (approximately 15 Hz). When the model perceives a presented object, the dynamic cell assemblies corresponding to the object are persistently linked together across the networks and coherently oscillate at higher frequencies (approximately 40 Hz). When the feedback pathways are impaired, the dynamic phase transition from the slow- to fast-oscillations is not induced by the object presentation, keeping the lower frequency oscillations (approximately 15 Hz) where the activated dynamic cell assemblies oscillate incoherently. Reaction times to the object presentation are greatly reduced if the ongoing oscillation frequencies fall within a specific range (approximately 20-30 Hz). I suggest that coherent ongoing slow-oscillations in cortical activity may serve as a ready state for sensory input, whereby the brain can respond effectively to sensory stimulation. Top-down processing via feedback pathways may give an essential contribution to the induction of coherent fast-oscillations across multiple cortical areas, by which relevant features are effectively integrated into a unified percept when stimulated with a sensory object.     

An ongoing subthreshold neuronal state established through dynamic coassembling of cortical cells

Ensemble activation of neurons, triggered or spontaneous, sometimes involves a common (overlapping) neuronal population known as core cells. It is speculated that the core cells functioning as a core nucleus have a role in dictating noncore cells' behavior and thus overall local network dynamics. However, the truth and its significance in neuronal information processing still remain to be seen. To address this issue, a neural network model of an early sensory cortical area was simulated. In the network model, noncore cells that have selective responsiveness to sensory features constituted noncore cell assemblies. Core cells, having unselective responsiveness, constituted a single core cell assembly. Sensory stimulation activated neuronal ensembles that were indistinguishable from those activated spontaneously. The core cells were active in every ensemble activation and recruited a changing complement of noncore cells, which varied from spontaneous event to spontaneous event or from triggered event to triggered event. Ensemble activation of neurons was established through what we call dynamic coassembling, in which the core cell assembly and one of the noncore cell assemblies were dynamically linked together. Transient dynamic coassembling frequently and randomly took place during the ongoing (spontaneous) neuronal activity period, and persistent dynamic coassembling did during the stimulus-triggered neuronal activity period. The frequent ongoing activation of core cells mediated through transient dynamic coassembling depolarized noncore cells just below firing threshold, whereby the noncore cells could respond rapidly to sensory stimulation. The persistent dynamic coassembling enhanced the responsiveness of noncore cells. We suggest that the core cells, functioning as a core nucleus, dictate how the noncore cells oscillate at a subthreshold level during the ongoing period and how to respond when stimulated. The transient and persistent dynamic coassembling may be an essential neuronal mechanism for the cortex to prepare and respond effectively to sensory input.     

Neuronal responses below firing threshold for subthreshold cross-modal enhancement

Multisensory integration (such as somatosensation-vision, gustation-olfaction) could occur even between subthreshold stimuli that in isolation do not reach perceptual awareness. For example, when a somatosensory (subthreshold) stimulus is delivered within a close spatiotemporal congruency, a visual (subthreshold) stimulus evokes a visual percept. Cross-modal enhancement of visual perception is maximal when the somatosensory stimulation precedes the visual one by tens of milliseconds. This rapid modulatory response would not be consistent with a top-down mechanism acting through higher-order multimodal cortical areas, but rather a direct interaction between lower-order unimodal areas. To elucidate the neuronal mechanisms of subthreshold cross-modal enhancement, we simulated a neural network model. In the model, lower unimodal (X, Y) and higher multimodal (M) networks are reciprocally connected by bottom-up and top-down axonal projections. The lower networks are laterally connected with each other. A pair of stimuli was presented to the lower networks, whose respective intensities were too weak to induce salient neuronal activity (population response) when presented alone. Neurons of the Y network were slightly depolarized below firing threshold when a cross-modal stimulus was presented alone to the X network. This allowed the Y network to make a rapid (within tens of milliseconds) population response when presented with a subsequent congruent stimulus. The reaction speed of the Y network was accelerated, provided that the top-down projections were strengthened. We suggest that a subthreshold (nonpopulation) response to a cross-modal stimulus, acting through interaction between lower (primary unisensory) areas, may be essential for a rapid suprathreshold (population) response to a congruent stimulus that follows. Top-down influences on cross-modal enhancement may be faster than expected, accelerating reaction speed to input, in which ongoing-spontaneous subthreshold excitation of lower-order unimodal cells by higher-order multimodal cells may play an active role.     

Mean-driven and fluctuation-driven persistent activity in recurrent networks

Spike trains from cortical neurons show a high degree of irregularity, with coefficients of variation (CV) of their interspike interval (ISI) distribution close to or higher than one. It has been suggested that this irregularity might be a reflection of a particular dynamical state of the local cortical circuit in which excitation and inhibition balance each other. In this "balanced" state, the mean current to the neurons is below threshold, and firing is driven by current fluctuations, resulting in irregular Poisson-like spike trains. Recent data show that the degree of irregularity in neuronal spike trains recorded during the delay period of working memory experiments is the same for both low-activity states of a few Hz and for elevated, persistent activity states of a few tens of Hz. Since the difference between these persistent activity states cannot be due to external factors coming from sensory inputs, this suggests that the underlying network dynamics might support coexisting balanced states at different firing rates. We use mean field techniques to study the possible existence of multiple balanced steady states in recurrent networks of current-based leaky integrate-and-fire (LIF) neurons. To assess the degree of balance of a steady state, we extend existing mean-field theories so that not only the firing rate, but also the coefficient of variation of the interspike interval distribution of the neurons, are determined self-consistently. Depending on the connectivity parameters of the network, we find bistable solutions of different types. If the local recurrent connectivity is mainly excitatory, the two stable steady states differ mainly in the mean current to the neurons. In this case, the mean drive in the elevated persistent activity state is suprathreshold and typically characterized by low spiking irregularity. If the local recurrent excitatory and inhibitory drives are both large and nearly balanced, or even dominated by inhibition, two stable states coexist, both with subthreshold current drive. In this case, the spiking variability in both the resting state and the mnemonic persistent state is large, but the balance condition implies parameter fine-tuning. Since the degree of required fine-tuning increases with network size and, on the other hand, the size of the fluctuations in the afferent current to the cells increases for small networks, overall we find that fluctuation-driven persistent activity in the very simplified type of models we analyze is not a robust phenomenon. Possible implications of considering more realistic models are discussed.     

Subtractive and divisive inhibition: effect of voltage-dependent inhibitory conductances and noise

The influence of voltage-dependent inhibitory conductances on firing rate versus input current (f-I) curves is studied using simulations from a new compartmental model of a pyramidal cell of the weakly electric fish Apteronotus leptorhynchus. The voltage dependence of shunting-type inhibition enhances the subtractive effect of inhibition on f-I curves previously demonstrated in Holt and Koch (1997) for the voltage-independent case. This increased effectiveness is explained using the behavior of the average subthreshold voltage with input current and, in particular, the nonlinearity of Ohm's law in the subthreshold regime. Our simulations also reveal, for both voltage-dependent and -independent inhibitory conductances, a divisive inhibition regime at low frequencies (f < 40 Hz). This regime, dependent on stochastic inhibitory synaptic input and a coupling of inhibitory strength and variance, gives way to subtractive inhibition at higher-output frequencies (f > 40 Hz). A simple leaky integrate-and-fire type model that incorporates the voltage dependence supports the results from our full ionic simulations.     

Cognitive enhancement mediated through postsynaptic actions of norepinephrine on ongoing cortical activity

We propose two distinct types of norepinephrine (NE)-neuromodulatory systems: an enhanced-excitatory and enhanced-inhibitory (E-E/E-I) system and a depressed-excitatory and enhanced-inhibitory (D-E/E-I) system. In both systems, inhibitory synaptic efficacies are enhanced, but excitatory ones are modified in a contradictory manner: the E-E/E-I system enhances excitatory synaptic efficacies, whereas the D-E/E-I system depresses them. The E-E/E-I and D-E/E-I systems altered the dynamic property of ongoing (background) neuronal activity and greatly influenced the cognitive performance (S/N ratio) of a cortical neural network. The E-E/E-I system effectively enhanced S/N ratio for weaker stimuli with lower doses of NE, whereas the D-E/E-I system enhanced stronger stimuli with higher doses of NE. The neural network effectively responded to weaker stimuli if brief gamma-bursts were involved in ongoing neuronal activity that is controlled under the E-E/E-I neuromodulatory system. If the E-E/E-I and the D-E/E-I systems interact within the neural network, depressed neurons whose activity is depressed by NE application have bimodal property. That is, S/N ratio can be enhanced not only for stronger stimuli as its original property but also for weaker stimuli, for which coincidental neuronal firings among enhanced neurons whose activity is enhanced by NE application are essential. We suggest that the recruitment of the depressed neurons for the detection of weaker (subthreshold) stimuli might be advantageous for the brain to cope with a variety of sensory stimuli.     

Characterization of subthreshold voltage fluctuations in neuronal membranes

Synaptic noise due to intense network activity can have a significant impact on the electrophysiological properties of individual neurons. This is the case for the cerebral cortex, where ongoing activity leads to strong barrages of synaptic inputs, which act as the main source of synaptic noise affecting on neuronal dynamics. Here, we characterize the subthreshold behavior of neuronal models in which synaptic noise is represented by either additive or multiplicative noise, described by Ornstein-Uhlenbeck processes. We derive and solve the Fokker-Planck equation for this system, which describes the time evolution of the probability density function for the membrane potential. We obtain an analytic expression for the membrane potential distribution at steady state and compare this expression with the subthreshold activity obtained in Hodgkin-Huxley-type models with stochastic synaptic inputs. The differences between multiplicative and additive noise models suggest that multiplicative noise is adequate to describe the high-conductance states similar to in vivo conditions. Because the steady-state membrane potential distribution is easily obtained experimentally, this approach provides a possible method to estimate the mean and variance of synaptic conductances in real neurons.     

Selectivity and stability via dendritic nonlinearity

Inspired by recent studies regarding dendritic computation, we constructed a recurrent neural network model incorporating dendritic lateral inhibition. Our model consists of an input layer and a neuron layer that includes excitatory cells and an inhibitory cell; this inhibitory cell is activated by the pooled activities of all the excitatory cells, and it in turn inhibits each dendritic branch of the excitatory cells that receive excitations from the input layer. Dendritic nonlinear operation consisting of branch-specifically rectified inhibition and saturation is described by imposing nonlinear transfer functions before summation over the branches. In this model with sufficiently strong recurrent excitation, on transiently presenting a stimulus that has a high correlation with feed- forward connections of one of the excitatory cells, the corresponding cell becomes highly active, and the activity is sustained after the stimulus is turned off, whereas all the other excitatory cells continue to have low activities. But on transiently presenting a stimulus that does not have high correlations with feedforward connections of any of the excitatory cells, all the excitatory cells continue to have low activities. Interestingly, such stimulus-selective sustained response is preserved for a wide range of stimulus intensity. We derive an analytical formulation of the model in the limit where individual excitatory cells have an infinite number of dendritic branches and prove the existence of an equilibrium point corresponding to such a balanced low-level activity state as observed in the simulations, whose stability depends solely on the signal-to-noise ratio of the stimulus. We propose this model as a model of stimulus selectivity equipped with self-sustainability and intensity-invariance simultaneously, which was difficult in the conventional competitive neural networks with a similar degree of complexity in their network architecture. We discuss the biological relevance of the model in a general framework of computational neuroscience.     

Correlations and population dynamics in cortical networks

The function of cortical networks depends on the collective interplay between neurons and neuronal populations, which is reflected in the correlation of signals that can be recorded at different levels. To correctly interpret these observations it is important to understand the origin of neuronal correlations. Here we study how cells in large recurrent networks of excitatory and inhibitory neurons interact and how the associated correlations affect stationary states of idle network activity. We demonstrate that the structure of the connectivity matrix of such networks induces considerable correlations between synaptic currents as well as between subthreshold membrane potentials, provided Dale's principle is respected. If, in contrast, synaptic weights are randomly distributed, input correlations can vanish, even for densely connected networks. Although correlations are strongly attenuated when proceeding from membrane potentials to action potentials (spikes), the resulting weak correlations in the spike output can cause substantial fluctuations in the population activity, even in highly diluted networks. We show that simple mean-field models that take the structure of the coupling matrix into account can adequately describe the power spectra of the population activity. The consequences of Dale's principle on correlations and rate fluctuations are discussed in the light of recent experimental findings.     

Neural Mechanism of Corticofugal Modulation of Tuning Property in Frequency Domain of Bat’s Auditory System

It is quite important for sensory processing to understand how feedback modulates the information processing in early brain areas in order to extract behaviorally-relevant features of stimulus. The auditory system of bats provides an ideal system for clarifying the mechanism of the adaptive processing by feedback. To investigate this mechanism, we developed a neural network model of bat’s auditory system for detecting Doppler-shifted frequency of sound echoes. Using the model, we demonstrate the receptive field modulation of cortical neurons and the modulation of the feedback to IC neurons, evoked by an electric stimulation and a GABA antagonist. Our model reproduces qualitatively the experimental results of best frequency (BF) shifts by Xiao and Suga (Proc Natl Acad Sci USA 99(24):15743–15748, 2002). We also show how the modulations cause the BF shifts of cortical and subcortical neurons. Furthermore we investigate the neuronal characteristics required for enhancing BF shifts.     

Exploring ligand recognition and ion flow in comparative models of the human GABA type A receptor

We present two comparative models of the GABA(A) receptor. Model 1 is based on the 4-A resolution structure of the nicotinic acetylcholine receptor from Torpedo marmorata and represents the unliganded receptor. Two agonists, GABA and muscimol, two benzodiazepines, flunitrazepam and alprazolam, together with the general anaesthetic halothane, have been docked to this model. The ion flow is also explored in model 1 by evaluating the interaction energy of a chloride ion as it traverses the extracellular, transmembrane and intracellular domains of the protein. Model 2 differs from model 1 only in the extracellular domain and represents the liganded receptor. Comparison between the two models not only allows us to explore commonalities and differences with comparative models of the nicotinic acetylcholine receptor, but also suggests possible protein sub-domain interactions with the GABA(A) receptor not previously addressed.     

Extraction of salient contours from cluttered scenes

The responses of neurons in the primary visual cortex (V1) to stimulus inside the receptive field (RF) can be markedly modulated by stimuli outside the classical receptive field. The modulation, relying on contextual configurations, yields excitatory and inhibitory activities. The V1 neurons compose a functional network by lateral interactions and accomplish specific visual tasks in a dynamic and flexible fashion. Well-organized structures and conspicuous image locations are more salient and thus can pop out perceptually from the background. The excitatory and inhibitory activities give different visual physiological interpretations to the two kinds of saliencies.A model of contour extraction, inspired by visual cortical mechanisms of perceptual grouping, is presented. We unify the dual processes of spatial facilitation and surround inhibition to extract salient contours from complex scenes, and in this way coherent spatial configurations and region boundaries could stand out from their surround. The proposed method can selectively retain object contours, and meanwhile can dramatically reduce non-meaningful elements resulting from a texture background. This work gives a clear understanding for the roles of the inhibition and facilitation in grouping, and provides a biologically motivated computational strategy for contour extraction in computer vision.     

Contextual modulation of orientation tuning contributes to efficient processing of natural stimuli

It has been proposed that sensory neurons are adapted to the statistical structure of the natural environment in order to encode natural stimuli efficiently. While spatiotemporal correlations in luminance signals may be decorrelated by neurons in early visual processing stages, higher-order correlations, such as those in the orientation domain, are likely to persist in the input representation until the cortical level. In this study, we first examine orientation correlations in natural stimuli across brief time intervals and across nearby regions of space, and find strong correlations in both domains. We then examine contextual modulation of orientation tuning. We find that both temporal and spatial contexts exert a common influence on orientation tuning, shifting tuning away from the orientation of either the adapting (temporal) or surrounding (spatial) grating. Finally, we incorporate this context-mediated repulsive shift in orientation tuning into a model of cortical responses. We find that a direct result of the shift is a reduction of the redundancy in the population responses evoked by the orientation configurations that are most common in natural stimuli. Thus, cortical neurons may be adapted to the statistics of orientation in natural stimuli in order to increase the efficiency of natural stimulus representation.     

A hierarchical dynamical map as a basic frame for cortical mapping and its application to priming

A hierarchical dynamical map is proposed as the basic framework for sensory cortical mapping. To show how the hierarchical dynamical map works in cognitive processes, we applied it to a typical cognitive task known as priming, in which cognitive performance is facilitated as a consequence of prior experience. Prior to the priming task, the network memorizes a sensory scene containing multiple objects presented simultaneously using a hierarchical dynamical map. Each object is composed of different sensory features. The hierarchical dynamical map presented here is formed by random itinerancy among limit-cycle attractors into which these objects are encoded. Each limit-cycle attractor contains multiple point attractors into which elemental features belonging to the same object are encoded. When a feature stimulus is presented as a priming cue, the network state is changed from the itinerant state to a limit-cycle attractor relevant to the priming cue. After a short priming period, the network state reverts to the itinerant state. Under application of the test cue, consisting of some feature belonging to the object relevant to the priming cue and fragments of features belonging to others, the network state is changed to a limit-cycle attractor and finally to a point attractor relevant to the target feature. This process is considered as the identification of the target. The model consistently reproduces various observed results for priming processes such as the difference in identification time between cross-modality and within-modality priming tasks, the effect of interval between priming cue and test cue on identification time, the effect of priming duration on the time, and the effect of repetition of the same priming task on neural activity.     

Gamma oscillations and stimulus selection

More coherent excitatory stimuli are known to have a competitive advantage over less coherent ones. We show here that this advantage is amplified greatly when the target includes inhibitory interneurons acting via GABA(A)-receptor-mediated synapses and the coherent input oscillates at gamma frequency. We hypothesize that therein lies, at least in part, the functional significance of the experimentally observed link between attentional biasing of stimulus competition and gamma frequency rhythmicity.     

Enhanced stimulus encoding capabilities with spectral selectivity in inhibitory circuits by STDP

The ability to encode and transmit a signal is an essential property that must demonstrate many neuronal circuits in sensory areas in addition to any processing they may provide. It is known that an appropriate level of lateral inhibition, as observed in these areas, can significantly improve the encoding ability of a population of neurons. We show here a homeostatic mechanism by which a spike-timing-dependent plasticity (STDP) rule with a symmetric timing window (swSTDP) spontaneously drives the inhibitory coupling to a level that ensures accurate encoding in response to input signals within a certain frequency range. Interpreting these results mathematically, we find that this coupling level depends on the overlap of spectral information between stimulus and STDP window function. Generalization to arbitrary swSTDP and arbitrary stimuli reveals that the signals for which this improvement of encoding takes place can be finely selected on spectral criteria. We finally show that this spectral overlap principle holds for a variety of neuron types and network characteristics. The highly tunable frequency-power domain of efficiency of this mechanism, together with its ability to operate in very various neuronal contexts, suggest that it may be at work in most sensory areas.     

Early cortical processing of vection-inducing visual stimulation as measured by event-related brain potentials (ERP)

Visual motion stimuli can induce the perception of self-motion in stationary observers (known as vection). In the present study, we investigated the sensory processing underlying vection by measuring the human event-related brain potentials (ERPs) elicited by the movement onset of a visual stimulus. We presented participants a visual stimulus consisting of alternating black-and-white vertical bars that moved in horizontal direction, creating the sensation of vection. The stimulus was presented on a screen that was divided into a central and a surrounding peripheral visual area. Both areas moved independently from each other, resulting in four different movement patterns: the peripheral and the central stimulus moved in the same or opposite direction, or one of the two visual areas of the stimulus moved while the other remained stable. In addition, two different stimulus types varying with respect to the bars’ width (narrow vs. wide) were used. Participants were presented with these stimuli in two phases of the experiment: During EEG-recording, only short phases (2.5–3.5 ms) of visual motion were applied. In a separate rating phase, visual motion was presented for 45 s. In this phase, vection onset, intensity, and duration were verbally recorded. Overall, the visual stimulation generated vection with different intensities (i.e., weakest vection with sole central stimulus movement). Stimulus type did not affect vection. In the ERPs, stimulus onset elicited parieto-occipital P2 and N2 components. The amplitudes of the ERP components differed significantly between the four movement patterns (irrespective of stimulus type), however, they did not fully align with the subjective vection ratings. Since the ERPs are associated with early sensory processing preceding vection, we argue that the ERPs mirror the contribution of sensory cortical processes to vection rather than the subjective sensation of vection per se.