Selective attention allows to process stimuli which are behaviorally relevant, while attenuating distracting information. However, it is an open question what mechanisms implement selective routing, and how they are engaged in dependence on behavioral need. Here we introduce a novel framework for selective processing by spontaneous synchronization. Input signals become organized into 'avalanches' of synchronized spikes which propagate to target populations. Selective attention enhances spontaneous synchronization and boosts signal transfer by a simple disinhibition of a control population, without requiring changes in synaptic weights. Our framework is fully analytically tractable and provides a complete understanding of all stages of the routing mechanism, yielding closed-form expressions for input-output correlations. Interestingly, although gamma oscillations can naturally occur through a recurrent dynamics, we can formally show that the routing mechanism itself does not require such oscillatory activity and works equally well if synchronous events would be randomly shuffled over time. Our framework explains a large range of physiological findings in a unified framework and makes specific predictions about putative control mechanisms and their effects on neural dynamics.

Visual inputs to the human brain are very rich, originating from about 130 million photoreceptors. Despite this high resolution, little information is actually necessary for recognizing objects. For example, just a few fragments of an object’s outline are usually required for its recognition. It is largely unknown which type of fragments provide the largest amount of information for object recognition. Past research has either tested mid-level vision fragments, such as curved segments, or only simple dots placed along the object’s contours. So far, mid- and low-level fragments in object recognition have never been compared. Here, we hypothesize that observers need a smaller number of curved segments than dots to recognize objects while keeping luminance, fragment’s size and number of fragments comparable. To test this hypothesis, we developed a novel object encoding algorithm based on contours’ extraction and on convolutions between the object’s contours and low- as well as mid-level fragments. Ten human observers identified thirty fragmented objects (curved segments vs. dots) generated by our algorithm. Participants were able to recognize every object both expressed as low- and mid-level fragments. The odds of recognizing objects were greater, the more fragments were presented. In line with our hypothesis, the interaction effect (fragment type x number of fragments) shows that with an increasing number of fragments, indeed less curved segments are needed than dots to recognize the objects. These results suggest that additional local information provided by the mid-level fragments are used by observers, which facilitates object recognition. Our results are important both for basic vision research as well as visual neuroprostheses where the number of electrodes on the retinal or cortical implant is strongly limited.

Cortical networks exhibit synchronized activity which often occurs in spontaneous events in the form of spike avalanches. Since synchronization has been causally linked to central aspects of brain function such as selective signal processing and integration of stimulus information, participating in an avalanche is a form of a transient synchrony which temporarily creates neural assemblies and hence might especially be useful for implementing flexible information processing. For understanding how assembly formation supports neural computation, it is therefore essential to establish a comprehensive theory of how network structure and dynamics interact to generate specific avalanche patterns and sequences. Here we derive exact avalanche distributions for a finite network of recurrently coupled spiking neurons with arbitrary non-negative interaction weights, which is made possible by formally mapping the model dynamics to a linear, random dynamical system on the $N$-torus and by exploiting self-similarities inherent in the phase space. We introduce the notion of relative unique ergodicity and show that this property is guaranteed if the system is driven by a time-invariant Bernoulli process. This approach allows us not only to provide closed-form analytical expressions for avalanche size, but also to determine the detailed set(s) of units firing in an avalanche (i.e., the avalanche assembly). The underlying dependence between network structure and dynamics is made transparent by expressing the distribution of avalanche assemblies in terms of the induced graph Laplacian. We explore analytical consequences of this dependence and provide illustrating examples.

Sudden changes in visual scenes often indicate important events for behavior. For their quick and reliable detection, the brain must be capable to process these changes as independent as possible from its current activation state. In motion-selective area MT, neurons respond to instantaneous speed changes with pronounced transients, often far exceeding the expected response as derived from their speed tuning profile. We here show that this complex, non-linear behavior emerges from the combined temporal dynamics of excitation and divisive inhibition, and provide a comprehensive formal analysis. A central prediction derived from this investigation is that attention increases the steepness of the transient response irrespective of the activation state prior to a stimulus change, and irrespective of the sign of the change. Extracellular recordings of attention-dependent representation of both speed increments and decrements confirmed this prediction and suggest that improved change detection derives from basic computations in a canonical cortical circuitry.

When probed with complex stimuli that extend beyond their classical receptive field, neurons in primary visual cortex display complex and non-linear response characteristics. Sparse coding models reproduce some of the observed contextual effects, but still fail to provide a satisfactory explanation in terms of realistic neural structures and cortical mechanisms, since the connection scheme they propose consists only of interactions among neurons with overlapping input fields. Here we propose an extended generative model for visual scenes that includes spatial dependencies among different features. We derive a neurophysiologically realistic inference scheme under the constraint that neurons have direct access only to local image information. The scheme can be interpreted as a network in primary visual cortex where two neural populations are organized in different layers within orientation hypercolumns that are connected by local, short-range and long-range recurrent interactions. When trained with natural images, the model predicts a connectivity structure linking neurons with similar orientation preferences matching the typical patterns found for long-ranging horizontal axons and feedback projections in visual cortex. Subjected to contextual stimuli typically used in empirical studies, our model replicates several hallmark effects of contextual processing and predicts characteristic differences for surround modulation between the two model populations. In summary, our model provides a novel framework for contextual processing in the visual system proposing a well-defined functional role for horizontal axons and feedback projections.

The existence of power law distributions is only a first requirement in the validation of the critical behavior of a system. Long-range spatio-temporal correlations are fundamental for the spontaneous neuronal activity to be the expression of a system acting close to a critical point. This chapter focuses on temporal correlations and avalanche dynamics in the spontaneous activity of cortex slice cultures and in the resting fMRI BOLD signal. Long-range correlations are investigated by means of the scaling of power spectra and of Detrended Fluctuations Analysis. The existence of 1/ f decay in the power spectrum, as well as of power-law scaling in the root mean square fluctuation function for the appropriate balance of excitation and inhibition suggests that long-range temporal correlations are distinctive of “healthy brains”. The corresponding temporal organization of neuronal avalanches can be dissected by analyzing the distribution of inter-event times between successive events. In rat cortex slice cultures this distribution exhibits a non-monotonic behavior, not usually found in other natural processes. Numerical simulations provide evidences that this behavior is a consequence of the alternation between states of high and low activity, leading to a dynamic balance between excitation and inhibition that tunes the system at criticality. In this scenario, inter-times show a peculiar relation with avalanche sizes, resulting in a hierarchical structure of avalanche sequences. Large avalanches correspond to low-frequency oscillations, and trigger cascades of smaller ones that are part of higher frequency rhythms. The self-regulated balance of excitation and inhibition observed in cultures is confirmed at larger scales, i.e. on F. Lombardi Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, MA, USA e-mail: fabri@bu.edu H. J. Herrmann Institute of Computational Physics for Engineering Materials, IfB, ETH Zürich, Zürich, Switzerland e-mail: hans@ifb.baug.ethz.ch L. de Arcangelis (B) Department of Engineering, University of Campania “Luigi Vanvitelli”, INFN sez. Napoli Gr. Coll., Salerno, Aversa (CE), Italy e-mail: lucilla.dearcangelis@unicampania.it © Springer Nature Switzerland AG 2019 N. Tomen et al. (eds.), The Functional Role of Critical Dynamics in Neural Systems, Springer Series on Bioand Neurosystems 11, https://doi.org/10.1007/978-3-030-20965-0_1 1 2 F. Lombardi et al. fMRI data from resting brain activity, and appears to be closely related to critical features of avalanche activity, which could play an important role in learning and other functional performance of neuronal systems.

Dynamical systems close to a critical state have the ability to spontaneously engage large numbers of units in collective events called avalanches-but how can this property be actively employed by the brain in order to perform meaningful computations under realistic circumstances? In our study we investigate this question by focusing on the visual system which has to meet a major challenge: to rapidly integrate information from a large number of single channels, and in a flexible manner depending on behavioral and external context. In this framework we are going to discuss two distinct examples, the first a bottom-up figure-ground segregation scenario and the second a top-down enhancement of object discriminability under selective attention. Both scenarios make explicit use of critical states for information processing, while formally extending the concept of criticality to inhomogeneous systems subject to a strong external drive.

Selective attention allows focusing on only part of the incoming sensory information. Neurons in the extrastriate visual cortex reflect such selective processing when different stimuli are simultaneously present in their large receptive fields. Their spiking response then resembles the response to the attended stimulus when presented in isolation. Unclear is where in the neuronal pathway attention intervenes to achieve such selective signal routing and processing. To investigate this question, we tagged two equivalent visual stimuli by independent broadband luminance noise and used the spectral coherence of these behaviorally irrelevant signals with the field potential of a local neuronal population in male macaque monkeys' area V4 as a measure for their respective causal influences. This new experimental paradigm revealed that signal transmission was considerably weaker for the not-attended stimulus. Furthermore, our results show that attention does not need to modulate responses in the input populations sending signals to V4 to selectively represent a stimulus, nor do they suggest a change of the V4 neurons' output gain depending on their feature similarity with the stimuli. Our results rather imply that selective attention uses a gating mechanism comprising the synaptic "inputs" that transmit signals from upstream areas into the V4 neurons. A minimal model implementing attention-dependent routing by gamma-band synchrony replicated the attentional gating effect and the signals' spectral transfer characteristics. It supports the proposal that selective interareal gamma-band synchrony subserves signal routing and explains our experimental finding that attention selectively gates signals already at the level of afferent synaptic input.SIGNIFICANCE STATEMENT Depending on the behavioral context, the brain needs to channel the flow of information through its networks of massively interconnected neurons. We designed an experiment that allows to causally assess routing of information originating from an attended object. We found that attention "gates" signals at the interplay between afferent fibers and the local neurons. A minimal model demonstrated that coherent gamma-rhythmic activity (∼60 Hz) between local neurons and their afferent-providing input neurons can realize the gating. Importantly, the attended signals did not need to be amplified already in an earlier processing stage, nor did they get amplified by a simple output response modulation. The method provides a useful tool to study mechanisms of dynamic network configuration underlying cognitive processes.

Perception seems so simple.I look out of the window to see houses, trees, people walking past, the sky above, the grass below.I hear birds in the trees, cars going past, the distant sound of an alarm.The world is full of objects that make their presence known to me through my senses -what could be more simple?Yet the efficacy of perceptual experience hides a host of questions for which we do not yet have the answers.Information reaching our senses is generally incomplete, ambiguous, distributed in space and time and not neatly sorted according to its source, so a key function of our perceptual systems is to discover the likely causes of our sensations.Perception as inference or hypothesis testing, formalised in the predictive coding theory, offers an attractive framework for exploring these issues.From this perspective, regularities or patterns provide perceptual systems with some traction, allowing the formation of expectations and a basis for decomposing the world into discrete objects.But in the dynamic world which we inhabit, object representations must be similarly dynamic, and need to form and dissolve, dominate and yield, in a way that facilitates veridical perception.In this talk I will discuss auditory scene analysis in the context of predictive coding using experimental data, exemplar models, and the phenomenon of perceptual multistability.

In the visual system complex scenes have to be integrated from simple local features into global and meaningful percepts. Contour integration, a basic process useful for figure-ground segregation and object recognition, is already well understood in terms of orientation alignment. However, there are other features playing a role in this process. Spatial frequency for example has a strong influence on contour visibility.

Processing natural scenes requires the visual system to integrate local features into global object descriptions. To achieve coherent representations, the human brain uses statistical dependencies to guide weighting of local feature conjunctions. Pairwise interactions among feature detectors in early visual areas may form the early substrate of these local feature bindings. To investigate local interaction structures in visual cortex, we combined psychophysical experiments with computational modeling and natural scene analysis. We first measured contrast thresholds for 2 × 2 grating patch arrangements (plaids), which differed in spatial frequency composition (low, high, or mixed), number of grating patch co-alignments (0, 1, or 2), and inter-patch distances (1° and 2° of visual angle). Contrast thresholds for the different configurations were compared to the prediction of probability summation (PS) among detector families tuned to the four retinal positions. For 1° distance the thresholds for all configurations were larger than predicted by PS, indicating inhibitory interactions. For 2° distance, thresholds were significantly lower compared to PS when the plaids were homogeneous in spatial frequency and orientation, but not when spatial frequencies were mixed or there was at least one misalignment. Next, we constructed a neural population model with horizontal laminar structure, which reproduced the detection thresholds after adaptation of connection weights. Consistent with prior work, contextual interactions were medium-range inhibition and long-range, orientation-specific excitation. However, inclusion of orientation-specific, inhibitory interactions between populations with different spatial frequency preferences were crucial for explaining detection thresholds. Finally, for all plaid configurations we computed their likelihood of occurrence in natural images. The likelihoods turned out to be inversely related to the detection thresholds obtained at larger inter-patch distances. However, likelihoods were almost independent of inter-patch distance, implying that natural image statistics could not explain the crowding-like results at short distances. This failure of natural image statistics to resolve the patch distance modulation of plaid visibility remains a challenge to the approach.

submission Bernstein Conference 2016 Status: Submitted Model-based inferences into attention and bistablity information routing control via preciselytimed pertubations Dmitriy Lisitsyn Udo Ernst 1. Intitute for Theoretical Physics, Bremen University, Hochschulring 18, 28359 Bremen, Germany Depending on behavioral context, sensory signal information is selectively routed throughout the brain. For the visual system, studies have shown that V4 neurons can ‘select’ the behaviorally relevant stimulus out of multiple active inputs [1]. The corresponding mechanisms are still debated, however, frequency-band synchronization has emerged as a promising candidate, proposing that information routing (IR) occurs between populations with a favorable phase-locked relationship and is suppressed otherwise (communication-through-coherence, CTC) [2]. Electrical stimulation offers the possibility of causally testing CTC by externally perturbing the neural dynamics, and by quantifying its impact on IR. However, there are two major obstacles. First, one has to quickly and accurately determine the actual network state from noisy, non-stationary activity. Second, the behavior of a coupled system is much more constrained than the dynamics of its constituting modules in isolation, making it difficult to control by external stimulation. Here, we address these two problems in a biophysically realistic model [3] (Fig A) by exploring a paradigm that employs precisely timed perturbations derived from phase-response curves (PRCs) to control the relative phase of neural populations, allowing for direct control over IR [4]. In contrast to enforcing a desired state by strong stimulation, we use minimal perturbations to push a multistable system into one of its preferred states. To determine the network's state in real-time, we investigated multiple methods, finding that a combination of predictive autoregression (AR), bandpass filter and Hilbert transform provides the best results [5]. It is fast and accurate, and perfectly applicable to animal experiments even in presence of recording/stimulation artifacts. Further, we focused on the network's dynamics and analyzed its tendency to spontaneously switch between states where either population X or Y is in favorable phase with Z. Using previously calculated PRCs together with AR real-time Gamma phase estimation (Fig B), we exploit this bistability for controlling which populations will enter a favorable phase relationship. In particular, we propose two alternative scenarios in which this is possible by stimulating higher level, or by stimulating lower level areas (Fig A+B). By imposing independent signal streams onto the input populations, we prove that our controlled synchronization routes information as desired. Acknowledgements This work was supported by BMBF (Bernstein Award Udo Ernst, grant no. 01GQ1106). References 1 1 6/16/2016 Bernstein Conference 2016 https://abstracts.g­node.org/myabstracts/f4079ef2­a8bf­4de3­a696­32a57541e2ed/edit 2/3 1. Moran, Jeffrey, and Robert Desimone. "Selective attention gates visual processing in the extrastriate cortex." Science 229.4715 (1985): 782-784. (http://science.sciencemag.org/content/229/4715/782), 10.1126/science.4023713 (http://dx.doi.org/10.1126/science.4023713) 2. Fries, Pascal. "Rhythms for cognition: communication through coherence." Neuron 88.1 (2015): 220235. (http://www.sciencedirect.com/science/article/pii/S0896627315008235), 10.1016/j.neuron.2015.09.034 (http://dx.doi.org/10.1016/j.neuron.2015.09.034) 3. Harnack, Daniel, Udo Alexander Ernst, and Klaus Richard Pawelzik. "A model for attentional information routing through coherence predicts biased competition and multistable perception." Journal of neurophysiology 114.3 (2015): 1593-1605. (http://jn.physiology.org/content/114/3/1593), 10.1152/jn.01038.2014 (http://dx.doi.org/10.1152/jn.01038.2014) 4. Voloh, Benjamin, and Thilo Womelsdorf. "A Role of Phase-Resetting in Coordinating Large Scale Neural Networks During Attention and Goal-Directed Behavior." Frontiers in systems neuroscience 10 (2016). (http://www.frontiersin.org/Journal/Abstract.aspx? s=1091&name=systems_neuroscience&ART_DOI=10.3389/fnsys.2016.00018), 10.3389/fnsys.2016.00018 (http://dx.doi.org/10.3389/fnsys.2016.00018 ) 5. Chen, L. Leon, et al. "Real-time brain oscillation detection and phase-locked stimulation using autoregressive spectral estimation and time-series forward prediction." Biomedical Engineering, IEEE Transactions on 60.3 (2013): 753-762. (http://ieeexplore.ieee.org/xpl/articleDetails.jsp? arnumber=5705563), 10.1109/TBME.2011.2109715 (http://dx.doi.org/10.1109/TBME.2011.2109715) Figure Figure 1: A. Schematic of the model. B. Top: higher level Z is pulsed, causing a shift in its phase and thus switching its favorable phase relationship from X to Y. Bottom: lower level Y is pulsed, allowing it to suppress competing X sufficiently enough to entrain Z. Topic Neurons, networks, dynamical systems Presentati n Preference 6/16/2016 Bernstein Conference 2016 https://abstracts.g­node.org/myabstracts/f4079ef2­a8bf­4de3­a696­32a57541e2ed/edit 3/3 Contact (/contact) Imprint (/impressum) About (/about) The G-Node Conference Site — © G-Node (http://www.g-node.org) 2016 Presentation Preference Talk or Poster Reason: The study should be of interest to a wide audience. The goal is to excite the community to pursue precise stimulation paradigms, rather than 'brute-force' pulse trains to entrain the brain's various dynamical states.

Attention is an important prerequisite for visual information processing. It allows the brain to focus on particular aspects of a visual scene, and enables or enhances perception of complex shapes and objects. However, attentional selection is an intricate computational problem, since the target of attentional deployment is often given by an arbitrary combination of features such as the location of an object having a conjunction of particular properties (features), and it is subjected to various neural constraints such as a given anatomical connectivity and broad tuning of neurons. How the brain solves this problem to boost the processing of combinations of different features that are represented across multiple neural areas is largely unknown. Recently, progress in understanding the properties of joint attentional selection was made in a psychophysical study investigating attentional spreading within and across objects [1]: Subjects were asked to report color and speed changes on one of two overlapping random dot patterns. Only one of the features was unique for each object, while the other was shared by both. Reaction times (RTs) recorded under different cueing conditions demonstrated the co-selection of unattended features, with attention spreading from the attended feature attribute in a particular feature dimension to other feature attributes and other feature dimensions. Importantly, this processing benefit was not restricted to the task-relevant object but extended to the unattended object. It is an open question how these observations can be modeled and understood in a coherent framework. In our contribution, we propose two structurally simple models, implementing two complementary neural mechanisms: The first model assumes that the specificity of anatomical connections providing attentional feedback from higher cortical areas is constrained. Consequently, top-down modulation of neural activity in lower visual areas is broadly tuned, targeting all cells representing the cued feature dimension(s) with only a weak preference for the cued feature attribute(s). As a result, attention spreads to uncued feature attributes and to jointly cued feature dimensions. In the second model we assume joint coding of stimulus features such that neurons having a preference for a particular motion direction also have a preference for a specific color. We also assume that neurons are not always perfectly tuned to their preferred feature(s), thus providing a (small) response to non-preferred feature values as well. Attentional feedback in this model precisely targets cells tuned for the cued feature attribute, or the combination of attributes. In this approach, attentional spreading to uncued feature dimensions is mediated by joint tuning, whereas spreading to uncued feature attributes in the same feature dimension is mediated by imperfect tuning. It turns out that both approaches, with appropriately chosen parameters, can qualitatively explain the differences in RTs between the stimulation conditions (Fig. 1). The next step is to develop a neurophysiologically plausible model that allows for explicit predictions for electrophysiological experiments to critically test the proposed mechanisms.

Selective attention allows to focus on relevant information and to ignore distracting features of a visual scene. These principles of information processing are reflected in response properties of neurons in visual area V4: if a neuron is presented with two stimuli in its receptive field, and one is attended, it responds as if the nonattended stimulus was absent (biased competition). In addition, when the luminance of the two stimuli is temporally and independently varied, local field potentials are correlated with the modulation of the attended stimulus and not, or much less, correlated with the nonattended stimulus (information routing). To explain these results in one coherent framework, we present a two-layer spiking cortical network model with distance-dependent lateral connectivity and converging feed-forward connections. With oscillations arising inherently from the network structure, our model reproduces both experimental observations. Hereby, lateral interactions and shifts of relative phases between sending and receiving layers (communication through coherence) are identified as the main mechanisms underlying both biased competition as well as selective routing. Exploring the parameter space, we show that the effects are robust and prevalent over a broad range of parameters. In addition, we identify the strength of lateral inhibition in the first model layer as crucial for determining the working regime of the system: increasing lateral inhibition allows a transition from a network configuration with mixed representations to one with bistable representations of the competing stimuli. The latter is discussed as a possible neural correlate of multistable perception phenomena such as binocular rivalry.