Biophysical parameters control signal transfer in spiking network.

Introduction: Information transmission and representation in both natural and artificial networks is dependent on connectivity between units. Biological neurons, in addition, modulate synaptic dynamics and post-synaptic membrane properties, but how these relate to information transmission in a population of neurons is still poorly understood. A recent study investigated local learning rules and showed how a spiking neural network can learn to represent continuous signals. Our study builds on their model to explore how basic membrane properties and synaptic delays affect information transfer. Methods: The system consisted of three input and output units and a hidden layer of 300 excitatory and 75 inhibitory leaky integrate-and-fire (LIF) or adaptive integrate-and-fire (AdEx) units. After optimizing the connectivity to accurately replicate the input patterns in the output units, we transformed the model to more biologically accurate units and included synaptic delay and concurrent action potential generation in distinct neurons. We examined three different parameter regimes which comprised either identical physiological values for both excitatory and inhibitory units (Comrade), more biologically accurate values (Bacon), or the Comrade regime whose output units were optimized for low reconstruction error (HiFi). We evaluated information transmission and classification accuracy of the network with four distinct metrics: coherence, Granger causality, transfer entropy, and reconstruction error. Results: Biophysical parameters showed a major impact on information transfer metrics. The classification was surprisingly robust, surviving very low firing and information rates, whereas information transmission overall and particularly low reconstruction error were more dependent on higher firing rates in LIF units. In AdEx units, the firing rates were lower and less information was transferred, but interestingly the highest information transmission rates were no longer overlapping with the highest firing rates. Discussion: Our findings can be reflected on the predictive coding theory of the cerebral cortex and may suggest information transfer qualities as a phenomenological quality of biological cells.

Resting-state Functional Connectivity After Occipital Stroke.

BACKGROUND: Resting-state functional magnetic resonance imaging (rsfMRI) reflects spontaneous activation of cortical networks. After stroke, these networks reorganize, both due to structural lesion and reorganization of functional connectivity (FC). OBJECTIVE: We studied FC in chronic phase occipital stroke patients with homonymous visual field defects before and after repetitive transorbital alternating current stimulation (rtACS). METHODS: This spin-off study, embedded in the randomized, sham-controlled REVIS trial, comprised 16 chronic occipital stroke patients with visual field defect and 12 healthy control subjects. The patients underwent rsfMRI at baseline, after two weeks of rtACS or sham treatment, and after two months of treatment-free follow-up, whereas the control subjects were measured once. We used a multivariate regression connectivity model to determine mutual prediction accuracy between 74 cortical regions of interest. Additionally, the model parameters were included into a graph to analyze average path length, centrality eigenvector, centrality degree, and clustering of the network. The patients and controls at baseline and the two treatment groups were compared with multilevel modeling. RESULTS: Before treatment, the patients and controls had similar whole-network prediction accuracy and network parameters, whereas centrality eigenvector differed in perilesional regions, indicating local modification in connectivity. In line with behavioral results, neither prediction accuracy nor any network parameter changed systematically as a result of rtACS rehabilitation compared to sham. CONCLUSIONS: Whole-network FC showed no difference between occipital stroke patients and healthy population, congruent with the peripheral location of the visual network in relation to the high-density cortical core. rtACS treatment in the given setting did not affect FC.

Anatomy and Physiology of Macaque Visual Cortical Areas V1, V2, and V5/MT: Bases for Biologically Realistic Models.

The cerebral cortex of primates encompasses multiple anatomically and physiologically distinct areas processing visual information. Areas V1, V2, and V5/MT are conserved across mammals and are central for visual behavior. To facilitate the generation of biologically accurate computational models of primate early visual processing, here we provide an overview of over 350 published studies of these three areas in the genus Macaca, whose visual system provides the closest model for human vision. The literature reports 14 anatomical connection types from the lateral geniculate nucleus of the thalamus to V1 having distinct layers of origin or termination, and 194 connection types between V1, V2, and V5, forming multiple parallel and interacting visual processing streams. Moreover, within V1, there are reports of 286 and 120 types of intrinsic excitatory and inhibitory connections, respectively. Physiologically, tuning of neuronal responses to 11 types of visual stimulus parameters has been consistently reported. Overall, the optimal spatial frequency (SF) of constituent neurons decreases with cortical hierarchy. Moreover, V5 neurons are distinct from neurons in other areas for their higher direction selectivity, higher contrast sensitivity, higher temporal frequency tuning, and wider SF bandwidth. We also discuss currently unavailable data that could be useful for biologically accurate models.

Controlling Complexity of Cerebral Cortex Simulations-II: Streamlined Microcircuits.

Recently, Markram et al. (2015) presented a model of the rat somatosensory microcircuit (Markram model). Their model is high in anatomical and physiological detail, and its simulation requires supercomputers. The lack of neuroinformatics and computing power is an obstacle for using a similar approach to build models of other cortical areas or larger cortical systems. Simplified neuron models offer an attractive alternative to high-fidelity Hodgkin-Huxley-type neuron models, but their validity in modeling cortical circuits is unclear. We simplified the Markram model to a network of exponential integrate-and-fire (EIF) neurons that runs on a single CPU core in reasonable time. We analyzed the electrophysiology and the morphology of the Markram model neurons with eFel and NeuroM tools, provided by the Blue Brain Project. We then constructed neurons with few compartments and averaged parameters from the reference model. We used the CxSystem simulation framework to explore the role of short-term plasticity and GABA B and NMDA synaptic conductances in replicating oscillatory phenomena in the Markram model. We show that having a slow inhibitory synaptic conductance (GABA B) allows replication of oscillatory behavior in the high-calcium state. Furthermore, we show that qualitatively similar dynamics are seen even with a reduced number of cell types (from 55 to 17 types). This reduction halved the computation time. Our results suggest that qualitative dynamics of cortical microcircuits can be studied using limited neuroinformatics and computing resources supporting parameter exploration and simulation of cortical systems. The simplification procedure can easily be adapted to studying other microcircuits for which sparse electrophysiological and morphological data are available.

Controlling Complexity of Cerebral Cortex Simulations-I: CxSystem, a Flexible Cortical Simulation Framework.

Simulation of the cerebral cortex requires a combination of extensive domain-specific knowledge and efficient software. However, when the complexity of the biological system is combined with that of the software, the likelihood of coding errors increases, which slows model adjustments. Moreover, few life scientists are familiar with software engineering and would benefit from simplicity in form of a high-level abstraction of the biological model. Our primary aim was to build a scalable cortical simulation framework for personal computers. We isolated an adjustable part of the domain-specific knowledge from the software. Next, we designed a framework that reads the model parameters from comma-separated value files and creates the necessary code for Brian2 model simulation. This separation allows rapid exploration of complex cortical circuits while decreasing the likelihood of coding errors and automatically using efficient hardware devices. Next, we tested the system on a simplified version of the neocortical microcircuit proposed by Markram and colleagues ( 2015 ). Our results indicate that the framework can efficiently perform simulations using Python, C ++ , and GPU devices. The most efficient device varied with computer hardware and the duration and scale of the simulated system. The speed of Brian2 was retained despite an overlying layer of software. However, the Python and C ++ devices inherited the single core limitation of Brian2. The CxSystem framework supports exploration of complex models on personal computers and thus has the potential to facilitate research on cortical networks and systems.

Radial Frequency Analysis of Contour Shapes in the Visual Cortex.

Cumulative psychophysical evidence suggests that the shape of closed contours is analysed by means of their radial frequency components (RFC). However, neurophysiological evidence for RFC-based representations is still missing. We investigated the representation of radial frequency in the human visual cortex with functional magnetic resonance imaging. We parametrically varied the radial frequency, amplitude and local curvature of contour shapes. The stimuli evoked clear responses across visual areas in the univariate analysis, but the response magnitude did not depend on radial frequency or local curvature. Searchlight-based, multivariate representational similarity analysis revealed RFC specific response patterns in areas V2d, V3d, V3AB, and IPS0. Interestingly, RFC-specific representations were not found in hV4 or LO, traditionally associated with visual shape analysis. The modulation amplitude of the shapes did not affect the responses in any visual area. Local curvature, SF-spectrum and contrast energy related representations were found across visual areas but without similar specificity for visual area that was found for RFC. The results suggest that the radial frequency of a closed contour is one of the cortical shape analysis dimensions, represented in the early and mid-level visual areas.

From evoked potentials to cortical currents: Resolving V1 and V2 components using retinotopy constrained source estimation without fMRI.

Despite evoked potentials' (EP) ubiquity in research and clinical medicine, insights are limited to gross brain dynamics as it remains challenging to map surface potentials to their sources in specific cortical regions. Multiple sources cancellation due to cortical folding and cross-talk obscures close sources, e.g. between visual areas V1 and V2. Recently retinotopic functional magnetic resonance imaging (fMRI) responses were used to constrain source locations to assist separating close sources and to determine cortical current generators. However, an fMRI is largely infeasible for routine EP investigation. We developed a novel method that replaces the fMRI derived retinotopic layout (RL) by an approach where the retinotopy and current estimates are generated from EEG or MEG signals and a standard clinical T1-weighted anatomical MRI. Using the EEG-RL, sources were localized to within 2 mm of the fMRI-RL constrained localized sources. The EEG-RL also produced V1 and V2 current waveforms that closely matched the fMRI-RL's (n = 2) r(1,198) = 0.99, P < 0.0001. Applying the method to subjects without fMRI (n = 4) demonstrates it generates waveforms that agree closely with the literature. Our advance allows investigators with their current EEG or MEG systems to create a library of brain models tuned to individual subjects' cortical folding in retinotopic maps, and should be applicable to auditory and somatosensory maps. The novel method developed expands EP's ability to study specific brain areas, revitalizing this well-worn technique. Hum Brain Mapp 37:1696-1709, 2016. © 2016 Wiley Periodicals, Inc.

[Is there unused capacity in our brain?]. / Onko aivoissamme käyttämätöntä kapasiteettia?

Although a great deal of research has been performed on special abilities occurring in connection with developmental disorders and injuries, their biological background remains unknown. It is tempting to think that understanding of the mechanism of generation of special ability would help each of us to liberate our brain capacity and direct it in a desired manner. Poor knowledge of the general functioning principle of the brain remains an essential restriction against establishing whether there is extra capacity in the brain and whether its liberation is possible now or in the near future. Model-based brain research is rising to the central position in understanding the functional principle.

Feedback to distal dendrites links fMRI signals to neural receptive fields in a spiking network model of the visual cortex.

The blood oxygenation level-dependent (BOLD) response has been strongly associated with neuronal activity in the brain. However, some neuronal tuning properties are consistently different from the BOLD response. We studied the spatial extent of neural and hemodynamic responses in the primary visual cortex, where the BOLD responses spread and interact over much longer distances than the small receptive fields of individual neurons would predict. Our model shows that a feedforward-feedback loop between V1 and a higher visual area can account for the observed spread of the BOLD response. In particular, anisotropic landing of inputs to compartmental neurons were necessary to account for the BOLD signal spread, while retaining realistic spiking responses. Our work shows that simple dendrites can separate tuning at the synapses and at the action potential output, thus bridging the BOLD signal to the neural receptive fields with high fidelity.

Modeling fMRI signals can provide insights into neural processing in the cerebral cortex.

Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals.

Contextual Modulation is Related to Efficiency in a Spiking Network Model of Visual Cortex.

In the visual cortex, stimuli outside the classical receptive field (CRF) modulate the neural firing rate, without driving the neuron by themselves. In the primary visual cortex (V1), such contextual modulation can be parametrized with an area summation function (ASF): increasing stimulus size causes first an increase and then a decrease of firing rate before reaching an asymptote. Earlier work has reported increase of sparseness when CRF stimulation is extended to its surroundings. However, there has been no clear connection between the ASF and network efficiency. Here we aimed to investigate possible link between ASF and network efficiency. In this study, we simulated the responses of a biomimetic spiking neural network model of the visual cortex to a set of natural images. We varied the network parameters, and compared the V1 excitatory neuron spike responses to the corresponding responses predicted from earlier single neuron data from primate visual cortex. The network efficiency was quantified with firing rate (which has direct association to neural energy consumption), entropy per spike and population sparseness. All three measures together provided a clear association between the network efficiency and the ASF. The association was clear when varying the horizontal connectivity within V1, which influenced both the efficiency and the distance to ASF, DAS. Given the limitations of our biophysical model, this association is qualitative, but nevertheless suggests that an ASF-like receptive field structure can cause efficient population response.

Subjective characteristics of TMS-induced phosphenes originating in human V1 and V2.

One way to study the neural correlates of visual consciousness is to localize the cortical areas whose stimulation generates subjective visual sensations, called phosphenes. While there is support for the view that the stimulation of several different visual areas in the occipital lobe may produce phosphenes, it is not clear what the contribution of each area is. Here, we studied the roles of the primary visual cortex (V1) and the adjacent area V2 in eliciting phosphenes by using functional magnetic resonance imaging-guided transcranial magnetic stimulation (TMS) combined with spherical modeling of the TMS-induced electric field. Reports of the subjective visual features of phosphenes were systematically collected and analyzed. We found that selective stimulation of V1 and V2 are equally capable of generating phosphenes, as demonstrated by comparable phosphene thresholds and similar characteristics of phosphene shape, color, and texture. However, the phosphenes induced by V1 stimulation were systematically perceived as brighter than the phosphenes induced by the stimulation of V2. Thus, these results suggest that V1 and V2 have a similar capability to produce conscious percepts. Nevertheless, V1 and V2 contribute differently to brightness: neural activation originating in V1 generates a more intense sensation of brightness than similar activation originating in V2.

Visual interactions conform to pattern decorrelation in multiple cortical areas.

Neural responses to visual stimuli are strongest in the classical receptive field, but they are also modulated by stimuli in a much wider region. In the primary visual cortex, physiological data and models suggest that such contextual modulation is mediated by recurrent interactions between cortical areas. Outside the primary visual cortex, imaging data has shown qualitatively similar interactions. However, whether the mechanisms underlying these effects are similar in different areas has remained unclear. Here, we found that the blood oxygenation level dependent (BOLD) signal spreads over considerable cortical distances in the primary visual cortex, further than the classical receptive field. This indicates that the synaptic activity induced by a given stimulus occurs in a surprisingly extensive network. Correspondingly, we found suppressive and facilitative interactions far from the maximum retinotopic response. Next, we characterized the relationship between contextual modulation and correlation between two spatial activation patterns. Regardless of the functional area or retinotopic eccentricity, higher correlation between the center and surround response patterns was associated with stronger suppressive interaction. In individual voxels, suppressive interaction was predominant when the center and surround stimuli produced BOLD signals with the same sign. Facilitative interaction dominated in the voxels with opposite BOLD signal signs. Our data was in unison with recently published cortical decorrelation model, and was validated against alternative models, separately in different eccentricities and functional areas. Our study provides evidence that spatial interactions among neural populations involve decorrelation of macroscopic neural activation patterns, and suggests that the basic design of the cerebral cortex houses a robust decorrelation mechanism for afferent synaptic input.

Brightness and transparency in the early visual cortex.

Several psychophysical studies have shown that transparency can have drastic effects on brightness and lightness. However, the neural processes generating these effects have remained unresolved. Several lines of evidence suggest that the early visual cortex is important for brightness perception. While single cell recordings suggest that surface brightness is represented in the primary visual cortex, the results of functional magnetic resonance imaging (fMRI) studies have been discrepant. In addition, the location of the neural representation of transparency is not yet known. We investigated whether the fMRI responses in areas V1, V2, and V3 correlate with brightness and transparency. To dissociate the blood oxygen level-dependent (BOLD) response to brightness from the response to local border contrast and mean luminance, we used variants of White's brightness illusion, both opaque and transparent, in which luminance increments and decrements cancel each other out. The stimuli consisted of a target surface and a surround. The surround luminance was always sinusoidally modulated at 0.5 Hz to induce brightness modulation to the target. The target luminance was constant or modulated in counterphase to null brightness modulation. The mean signal changes were calculated from the voxels in V1, V2, and V3 corresponding to the retinotopic location of the target surface. The BOLD responses were significantly stronger for modulating brightness than for stimuli with constant brightness. In addition, the responses were stronger for transparent than for opaque stimuli, but there was more individual variation. No interaction between brightness and transparency was found. The results show that the early visual areas V1-V3 are sensitive to surface brightness and transparency and suggest that brightness and transparency are represented separately.

Fovea-periphery axis symmetry of surround modulation in the human visual system.

A visual stimulus activates different sized cortical area depending on eccentricity of the stimulus. Here, our aim is to understand whether the visual field size of a stimulus or cortical size of the corresponding representation determines how strongly it interacts with other stimuli. We measured surround modulation of blood-oxygenation-level-dependent signal and perceived contrast with surrounds that extended either towards the periphery or the fovea from a center stimulus, centered at 6° eccentricity. This design compares the effects of two surrounds which are identical in visual field size, but differ in the sizes of their cortical representations. The surrounds produced equally strong suppression, which suggests that visual field size of the surround determines suppression strength. A modeled population of neuronal responses, in which all the parameters were experimentally fixed, captured the pattern of results both in psychophysics and functional magnetic resonance imaging. Although the fovea-periphery anisotropy affects nearly all aspects of spatial vision, our results suggest that in surround modulation the visual system compensates for it.

Different orientation tuning of near- and far-surround suppression in macaque primary visual cortex mirrors their tuning in human perception.

In primary visual cortex (V1), neuronal responses to stimuli inside the receptive field (RF) are usually suppressed by stimuli in the RF surround. This suppression is orientation specific. Similarly, in human vision surround stimuli can suppress perceived contrast of a central stimulus in an orientation-dependent manner. The surround consists of two regions likely generated by different circuits: a near-surround generated predominantly by geniculocortical and intra-V1 horizontal connections, and a far-surround generated exclusively by interareal feedback. Using stimuli confined to the near- or far-surround of V1 neurons, and similar stimuli in human psychophysics, we find that near-surround suppression is more sharply orientation tuned than far-surround suppression in both macaque V1 and human perception. These results point to a similarity between surround suppression in macaque V1 and human vision, and suggest that feedback circuits are less orientation biased than horizontal circuits. We find the sharpest tuning of near-surround suppression in V1 layers (3, 4B, 4Cα) with patterned and orientation-specific horizontal connections. Sharpest tuning of far-surround suppression occurs in layer 4B, suggesting greater orientation specificity of feedback to this layer. Different orientation tuning of near- and far-surround suppression may reflect a statistical bias in natural images, whereby nearby edges have higher probability than distant edges of being co-oriented and belonging to the same contour. Surround suppression would, thus, increase the coding efficiency of frequently co-occurring contours and the saliency of less frequent ones. Such saliency increase can help detect small orientation differences in nearby edges (for contour completion), but large orientation differences in distant edges (for directing saccades/attention).

Seeing mathematics: perceptual experience and brain activity in acquired synesthesia.

We studied the patient JP who has exceptional abilities to draw complex geometrical images by hand and a form of acquired synesthesia for mathematical formulas and objects, which he perceives as geometrical figures. JP sees all smooth curvatures as discrete lines, similarly regardless of scale. We carried out two preliminary investigations to establish the perceptual nature of synesthetic experience and to investigate the neural basis of this phenomenon. In a functional magnetic resonance imaging (fMRI) study, image-inducing formulas produced larger fMRI responses than non-image inducing formulas in the left temporal, parietal and frontal lobes. Thus our main finding is that the activation associated with his experience of complex geometrical images emerging from mathematical formulas is restricted to the left hemisphere.

Retinotopic maps, spatial tuning, and locations of human visual areas in surface coordinates characterized with multifocal and blocked FMRI designs.

The localization of visual areas in the human cortex is typically based on mapping the retinotopic organization with functional magnetic resonance imaging (fMRI). The most common approach is to encode the response phase for a slowly moving visual stimulus and to present the result on an individual's reconstructed cortical surface. The main aims of this study were to develop complementary general linear model (GLM)-based retinotopic mapping methods and to characterize the inter-individual variability of the visual area positions on the cortical surface. We studied 15 subjects with two methods: a 24-region multifocal checkerboard stimulus and a blocked presentation of object stimuli at different visual field locations. The retinotopic maps were based on weighted averaging of the GLM parameter estimates for the stimulus regions. In addition to localizing visual areas, both methods could be used to localize multiple retinotopic regions-of-interest. The two methods yielded consistent retinotopic maps in the visual areas V1, V2, V3, hV4, and V3AB. In the higher-level areas IPS0, VO1, LO1, LO2, TO1, and TO2, retinotopy could only be mapped with the blocked stimulus presentation. The gradual widening of spatial tuning and an increase in the responses to stimuli in the ipsilateral visual field along the hierarchy of visual areas likely reflected the increase in the average receptive field size. Finally, after registration to Freesurfer's surface-based atlas of the human cerebral cortex, we calculated the mean and variability of the visual area positions in the spherical surface-based coordinate system and generated probability maps of the visual areas on the average cortical surface. The inter-individual variability in the area locations decreased when the midpoints were calculated along the spherical cortical surface compared with volumetric coordinates. These results can facilitate both analysis of individual functional anatomy and comparisons of visual cortex topology across studies.

Neuronavigated transcranial magnetic stimulation suggests that area V2 is necessary for visual awareness.

The primary visual cortex (V1) has been shown to be critical for visual awareness, but the importance of other low-level visual areas has remained unclear. To clarify the role of human cortical area V2 in visual awareness, we applied transcranial magnetic stimulation (TMS) over V2 while participants were carrying out a visual discrimination task and rating their subjective awareness. Individual retinotopic maps and modelling of the TMS-induced electric field in V1, V2 and V3d ensured that the electric field was at or under the phosphene threshold level in V1 and V3d, whereas in V2 it was at the higher suppressive level. As earlier shown for the V1, our results imply that also V2 is necessary for conscious visual experience. Visual awareness of stimulus presence was completely suppressed when the TMS pulse was delivered 44-84 ms after the onset of visual stimulus. Visual discrimination and awareness of stimulus features was impaired when the TMS pulse was delivered 44-104 ms after the visual stimulus onset. These results suggest that visual awareness cannot be generated without an intact V2.

Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER.

In this article we introduce the DRIFTER algorithm, which is a new model based Bayesian method for retrospective elimination of physiological noise from functional magnetic resonance imaging (fMRI) data. In the method, we first estimate the frequency trajectories of the physiological signals with the interacting multiple models (IMM) filter algorithm. The frequency trajectories can be estimated from external reference signals, or if the temporal resolution is high enough, from the fMRI data. The estimated frequency trajectories are then used in a state space model in combination of a Kalman filter (KF) and Rauch-Tung-Striebel (RTS) smoother, which separates the signal into an activation related cleaned signal, physiological noise, and white measurement noise components. Using experimental data, we show that the method outperforms the RETROICOR algorithm if the shape and amplitude of the physiological signals change over time.