Synchrony unbound: a critical evaluation of the temporal binding hypothesis

Robust computation with rhythmic spike patterns

This work makes 2 contributions. First, we present a neural network model of associative memory that stores and retrieves sparse patterns of complex variables. This network can store analog information as fixed-point attractors in the complex domain; it is governed by an energy function and has increased memory capacity compared to early models. Second, we translate complex attractor networks into spiking networks, where the timing of the spike indicates the phase of a complex number. We show that complex fixed points correspond to stable periodic spike patterns. It is demonstrated that such networks can be constructed with resonate-and-fire or integrate-and-fire neurons with biologically plausible mechanisms and be used for robust computations, such as image retrieval.

Information coding by precise timing of spikes can be faster and more energy efficient than traditional rate coding. However, spike-timing codes are often brittle, which has limited their use in theoretical neuroscience and computing applications. Here, we propose a type of attractor neural network in complex state space and show how it can be leveraged to construct spiking neural networks with robust computational properties through a phase-to-timing mapping. Building on Hebbian neural associative memories, like Hopfield networks, we first propose threshold phasor associative memory (TPAM) networks. Complex phasor patterns whose components can assume continuous-valued phase angles and binary magnitudes can be stored and retrieved as stable fixed points in the network dynamics. TPAM achieves high memory capacity when storing sparse phasor patterns, and we derive the energy function that governs its fixed-point attractor dynamics. Second, we construct 2 spiking neural networks to approximate the complex algebraic computations in TPAM, a reductionist model with resonate-and-fire neurons and a biologically plausible network of integrate-and-fire neurons with synaptic delays and recurrently connected inhibitory interneurons. The fixed points of TPAM correspond to stable periodic states of precisely timed spiking activity that are robust to perturbation. The link established between rhythmic firing patterns and complex attractor dynamics has implications for the interpretation of spike patterns seen in neuroscience and can serve as a framework for computation in emerging neuromorphic devices.

Neural dynamics of spreading attentional labels in mental contour tracing

Behavioral and neural data suggest that visual attention spreads along contour segments to bind them into a unified object representation. Such attentional labeling segregates the target contour from distractors in a process known as mental contour tracing. A recurrent competitive map is developed to simulate the dynamics of mental contour tracing. In the model, local excitation opposes global inhibition and enables enhanced activity to propagate on the path offered by the contour. The extent of local excitatory interactions is modulated by the output of the multi-scale contour detection network, which constrains the speed of activity spreading in a scale-dependent manner. Furthermore, an L-junction detection network enables tracing to switch direction at the L-junctions, but not at the X- or T-junctions, thereby preventing spillover to a distractor contour. Computer simulations reveal that the model exhibits a monotonic increase in tracing time as a function of the distance to be traced. Also, the speed of tracing increases with decreasing proximity to the distractor contour and with the reduced curvature of the contours. The proposed model demonstrated how an elaborated version of the winner-takes-all network can implement a complex cognitive operation such as contour tracing.

Dynamic Computation in Visual Thalamocortical Networks

Contemporary neurodynamical frameworks, such as coordination dynamics and winnerless competition, posit that the brain approximates symbolic computation by transitioning between metastable attractive states. This article integrates these accounts with electrophysiological data suggesting that coherent, nested oscillations facilitate information representation and transmission in thalamocortical networks. We review the relationship between criticality, metastability, and representational capacity, outline existing methods for detecting metastable oscillatory patterns in neural time series data, and evaluate plausible spatiotemporal coding schemes based on phase alignment. We then survey the circuitry and the mechanisms underlying the generation of coordinated alpha and gamma rhythms in the primate visual system, with particular emphasis on the pulvinar and its role in biasing visual attention and awareness. To conclude the review, we begin to integrate this perspective with longstanding theories of consciousness and cognition.

Visual Feature Integration of Three Attributes in Stimulus-Response Mapping Is Distinct From That of Two

In the human visual system, different attributes of an object are processed separately and are thought to be then temporarily bound by attention into an integrated representation to produce a specific response. However, if such representations existed in the brain for arbitrary multi-attribute objects, a combinatorial explosion problem would be unavoidable. Here, we show that attention may bind features of different attributes only in pairs and that bound feature pairs, rather than integrated object representations, are associated with responses for unfamiliar objects. We found that in a mapping task from three-attribute stimuli to responses, presenting three attributes in pairs (two attributes in each window) did not significantly complicate feature integration and response selection when the stimuli were not very familiar. We also found that repeated presentation of the same triple conjunctions significantly improved performance on the stimulus-response task when the correct responses were determined by the combination of three attributes, but this familiarity effect was not observed when the response could be determined by two attributes. These findings indicate that integration of three or more attributes is a distinct process from that of two, requiring long-term learning or some serial process. This suggests that integrated object representations are not formed or are formed only for a limited number of very familiar objects, which resolves the computational difficulty of the binding problem.

Effect of adenosine on short-term synaptic plasticity in mouse piriform cortex in vitro: adenosine acts as a high-pass filter

We examined the effect of adenosine and of adenosine A1 receptor blockage on short-term synaptic plasticity in slices of adult mouse anterior piriform cortex maintained in vitro in an in vivo-like ACSF. Extracellular recording of postsynaptic responses was performed in layer 1a while repeated electrical stimulation (5-pulse-trains, frequency between 3.125 and 100 Hz) was applied to the lateral olfactory tract. Our stimulation protocol was aimed at covering the frequency range of oscillatory activities observed in the olfactory bulb in vivo. In control condition, postsynaptic response amplitude showed a large enhancement for stimulation frequencies in the beta and gamma frequency range. A phenomenological model of short-term synaptic plasticity fitted to the data suggests that this frequency-dependent enhancement can be explained by the interplay between a short-term facilitation mechanism and two short-term depression mechanisms, with fast and slow recovery time constants. In the presence of adenosine, response amplitude evoked by low-frequency stimulation decreased in a dose-dependent manner (IC50  = 70 μmol/L). Yet short-term plasticity became more dominated by facilitation and less influenced by depression. Both changes compensated for the initial decrease in response amplitude in a way that depended on stimulation frequency: compensation was strongest at high frequency, up to restoring response amplitudes to values similar to those measured in control condition. The model suggested that the main effects of adenosine were to decrease neurotransmitter release probability and to attenuate short-term depression mechanisms. Overall, these results suggest that adenosine does not merely inhibit neuronal activity but acts in a more subtle, frequency-dependent manner.

Beyond Rehabilitation of Acuity, Ocular Alignment, and Binocularity in Infantile Strabismus

Infantile strabismus impairs the perception of all attributes of the visual scene. High spatial frequency components are no longer visible, leading to amblyopia. Binocularity is altered, leading to the loss of stereopsis. Spatial perception is impaired as well as detection of vertical orientation, the fastest movements, directions of movement, the highest contrasts and colors. Infantile strabismus also affects other vision-dependent processes such as control of postural stability. But presently, rehabilitative therapies for infantile strabismus by ophthalmologists, orthoptists and optometrists are restricted to preventing or curing amblyopia of the deviated eye, aligning the eyes and, whenever possible, preserving or restoring binocular vision during the critical period of development, i.e., before ~10 years of age. All the other impairments are thus ignored; whether they may recover after strabismus treatment even remains unknown. We argue here that medical and paramedical professionals may extend their present treatments of the perceptual losses associated with infantile strabismus. This hypothesis is based on findings from fundamental research on visual system organization of higher mammals in particular at the cortical level. In strabismic subjects (as in normal-seeing ones), information about all of the visual attributes converge, interact and are thus inter-dependent at multiple levels of encoding ranging from the single neuron to neuronal assemblies in visual cortex. Thus if the perception of one attribute is restored this may help to rehabilitate the perception of other attributes. Concomitantly, vision-dependent processes may also improve. This could occur spontaneously, but still should be assessed and validated. If not, medical and paramedical staff, in collaboration with neuroscientists, will have to break new ground in the field of therapies to help reorganize brain circuitry and promote more comprehensive functional recovery. Findings from fundamental research studies in both young and adult patients already support our hypothesis and are reviewed here. For example, presenting different contrasts to each eye of a strabismic patient during training sessions facilitates recovery of acuity in the amblyopic eye as well as of 3D perception. Recent data also demonstrate that visual recoveries in strabismic subjects improve postural stability. These findings form the basis for a roadmap for future research and clinical development to extend presently applied rehabilitative therapies for infantile strabismus.

Neural Coding of Cell Assemblies via Spike-Timing Self-Information

Cracking brain's neural code is of general interest. In contrast to the traditional view that enormous spike variability in resting states and stimulus-triggered responses reflects noise, here, we examine the "Neural Self-Information Theory" that the interspike-interval (ISI), or the silence-duration between 2 adjoining spikes, carries self-information that is inversely proportional to its variability-probability. Specifically, higher-probability ISIs convey minimal information because they reflect the ground state, whereas lower-probability ISIs carry more information, in the form of "positive" or "negative surprisals," signifying the excitatory or inhibitory shifts from the ground state, respectively. These surprisals serve as the quanta of information to construct temporally coordinated cell-assembly ternary codes representing real-time cognitions. Accordingly, we devised a general decoding method and unbiasedly uncovered 15 cell assemblies underlying different sleep cycles, fear-memory experiences, spatial navigation, and 5-choice serial-reaction time (5CSRT) visual-discrimination behaviors. We further revealed that robust cell-assembly codes were generated by ISI surprisals constituted of ~20% of the skewed ISI gamma-distribution tails, conforming to the "Pareto Principle" that specifies, for many events-including communication-roughly 80% of the output or consequences come from 20% of the input or causes. These results demonstrate that real-time neural coding arises from the temporal assembly of neural-clique members via silence variability-based self-information codes.

Neural Mechanisms of High-Level Vision

The last three decades have seen major strides in our understanding of neural mechanisms of high-level vision, or visual cognition of the world around us. Vision has also served as a model system for the study of brain function. Several broad insights, as yet incomplete, have recently emerged. First, visual perception is best understood not as an end unto itself, but as a sensory process that subserves the animal's behavioral goal at hand. Visual perception is likely to be simply a side effect that reflects the readout of visual information processing that leads to behavior. Second, the brain is essentially a probabilistic computational system that produces behaviors by collectively evaluating, not necessarily consciously or always optimally, the available information about the outside world received from the senses, the behavioral goals, prior knowledge about the world, and possible risks and benefits of a given behavior. Vision plays a prominent role in the overall functioning of the brain providing the lion's share of information about the outside world. Third, the visual system does not function in isolation, but rather interacts actively and reciprocally with other brain systems, including other sensory faculties. Finally, various regions of the visual system process information not in a strict hierarchical manner, but as parts of various dynamic brain-wide networks, collectively referred to as the "connectome." Thus, a full understanding of vision will ultimately entail understanding, in granular, quantitative detail, various aspects of dynamic brain networks that use visual sensory information to produce behavior under real-world conditions. © 2017 American Physiological Society. Compr Physiol 8:903-953, 2018.

Optimal Readout of Correlated Neural Activity in a Decision-Making Circuit

The neural correlates of decision making have been extensively studied with tasks involving a choice between two alternatives that is guided by visual cues. While a large body of work argues for a role of the lateral intraparietal (LIP) region of cortex in these tasks, this role may be confounded by the interaction between LIP and other regions, including medial temporal (MT) cortex. Here, we describe a simplified linear model of decision making that is adapted to two tasks: a motion discrimination and a categorization task. We show that the distinct contribution of MT and LIP may indeed be confounded in these tasks. In particular, we argue that the motion discrimination task relies on a straightforward visuomotor mapping, which leads to redundant information between MT and LIP. The categorization task requires a more complex mapping between visual information and decision behavior, and therefore does not lead to redundancy between MT and LIP. Going further, the model predicts that noise correlations within LIP should be greater in the categorization compared to the motion discrimination task due to the presence of shared inputs from MT. The impact of these correlations on task performance is examined by analytically deriving error estimates of an optimal linear readout for shared and unique inputs. Taken together, results clarify the contribution of MT and LIP to decision making and help characterize the role of noise correlations in these regions.

Computational Neuroscience: Mathematical and Statistical Perspectives

Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.

A neural circuit for gamma-band coherence across the retinotopic map in mouse visual cortex

Cortical gamma oscillations have been implicated in a variety of cognitive, behavioral, and circuit-level phenomena. However, the circuit mechanisms of gamma-band generation and synchronization across cortical space remain uncertain. Using optogenetic patterned illumination in acute brain slices of mouse visual cortex, we define a circuit composed of layer 2/3 (L2/3) pyramidal cells and somatostatin (SOM) interneurons that phase-locks ensembles across the retinotopic map. The network oscillations generated here emerge from non-periodic stimuli, and are stimulus size-dependent, coherent across cortical space, narrow band (30 Hz), and depend on SOM neuron but not parvalbumin (PV) neuron activity; similar to visually induced gamma oscillations observed in vivo. Gamma oscillations generated in separate cortical locations exhibited high coherence as far apart as 850 μm, and lateral gamma entrainment depended on SOM neuron activity. These data identify a circuit that is sufficient to mediate long-range gamma-band coherence in the primary visual cortex.

A neuronal theory of sequential economic choice

Results of recent studies point towards a new framework for the neural bases of economic choice. The principles of this framework include the idea that evaluation is limited to a single option within the focus of attention and that we accept or reject that option relative to the entire set of alternatives. Rejection leads attention to a new option, although it can later switch back to a previously rejected one. The option to which a neuron's firing rate refers is determined dynamically by attention and not stably by labelled lines. Value is always computed relative to the value of rejection. Comparison results not from explicit competition between discrete populations of neurons, but indirectly, as in a horse race, from the fact that the first option whose value crosses a threshold is selected. Consequently, comparison can occur within a single pool of neurons rather than by competition between two or more neuronal populations. The computations that constitute comparison thus occur at multiple levels, including premotor levels, simultaneously (i.e. the brain uses a distributed consensus), and not in discrete stages. This framework suggests a solution to a set of otherwise unresolved neuronal binding problems that result from the need to link options to values, comparisons to actions, and choices to outcomes.

Gap junctional coupling between retinal amacrine and ganglion cells underlies coherent activity integral to global object perception

Coherent spike activity occurs between widely separated retinal ganglion cells (RGCs) in response to a large, contiguous object, but not to disjointed objects. Since the large spatial separation between the RGCs precludes common excitatory inputs from bipolar cells, the mechanism underlying this long-range coherence remains unclear. Here, we show that electrical coupling between RGCs and polyaxonal amacrine cells in mouse retina forms the synaptic mechanism responsible for long-range coherent activity in the retina. Pharmacological blockade of gap junctions or genetic ablation of connexin 36 (Cx36) subunits eliminates the long-range correlated spiking between RGCs. Moreover, we find that blockade of gap junctions or ablation of Cx36 significantly reduces the ability of mice to discriminate large, global objects from small, disjointed stimuli. Our results indicate that synchronous activity of RGCs, derived from electrical coupling with amacrine cells, encodes information critical to global object perception.

Widespread theta synchrony and high-frequency desynchronization underlies enhanced cognition

The idea that synchronous neural activity underlies cognition has driven an extensive body of research in human and animal neuroscience. Yet, insufficient data on intracranial electrical connectivity has precluded a direct test of this hypothesis in a whole-brain setting. Through the lens of memory encoding and retrieval processes, we construct whole-brain connectivity maps of fast gamma (30-100 Hz) and slow theta (3-8 Hz) spectral neural activity, based on data from 294 neurosurgical patients fitted with indwelling electrodes. Here we report that gamma networks desynchronize and theta networks synchronize during encoding and retrieval. Furthermore, for nearly all brain regions we studied, gamma power rises as that region desynchronizes with gamma activity elsewhere in the brain, establishing gamma as a largely asynchronous phenomenon. The abundant phenomenon of theta synchrony is positively correlated with a brain region's gamma power, suggesting a predominant low-frequency mechanism for inter-regional communication.

The temporal dynamics involved in object representation updating to predict change

The synchronization of cortically disparate neural assemblies at frequencies in the gamma-band range (30-70Hz) is considered to be involved in the perceptual organization of the environment. In support of this Elliott (2014) demonstrated improved detection of a target stimulus when this target was primed in a matrix that flickered at specific frequencies in the gamma-band range, each found to be separated by regular intervals which correspond with a 6.69Hz period. This can be explained in terms of the interaction of the stimulus (and stimulus-induced) rhythm with a slow endogenous theta rhythm. When the interaction is in phase between these rhythms and target presentation time is slightly ahead of the priming stimulus presentation, improved detection times are recorded indicating an anticipatory response. However, when these rhythms are out of phase and the target is presented during or slightly after priming stimulus presentation, improved responding also occurs, suggesting a retroactive response is facilitated. Research in the auditory domain supports these findings (Aksentijevic et al., 2011). The conclusions of this research suggest that synchronization of neural assemblies contributes to the temporal code necessary to facilitate representation updating in order to respond to a dynamic environment and anticipate the logical next event.

Neural Code-Neural Self-information Theory on How Cell-Assembly Code Rises from Spike Time and Neuronal Variability

A major stumbling block to cracking the real-time neural code is neuronal variability - neurons discharge spikes with enormous variability not only across trials within the same experiments but also in resting states. Such variability is widely regarded as a noise which is often deliberately averaged out during data analyses. In contrast to such a dogma, we put forth the Neural Self-Information Theory that neural coding is operated based on the self-information principle under which variability in the time durations of inter-spike-intervals (ISI), or neuronal silence durations, is self-tagged with discrete information. As the self-information processor, each ISI carries a certain amount of information based on its variability-probability distribution; higher-probability ISIs which reflect the balanced excitation-inhibition ground state convey minimal information, whereas lower-probability ISIs which signify rare-occurrence surprisals in the form of extremely transient or prolonged silence carry most information. These variable silence durations are naturally coupled with intracellular biochemical cascades, energy equilibrium and dynamic regulation of protein and gene expression levels. As such, this silence variability-based self-information code is completely intrinsic to the neurons themselves, with no need for outside observers to set any reference point as typically used in the rate code, population code and temporal code models. Moreover, temporally coordinated ISI surprisals across cell population can inherently give rise to robust real-time cell-assembly codes which can be readily sensed by the downstream neural clique assemblies. One immediate utility of this self-information code is a general decoding strategy to uncover a variety of cell-assembly patterns underlying external and internal categorical or continuous variables in an unbiased manner.

Prominent facilitation at beta and gamma frequency range revealed with physiological calcium concentration in adult mouse piriform cortex in vitro

Neuronal activity is characterized by a diversity of oscillatory phenomena that are associated with multiple behavioral and cognitive processes, yet the functional consequences of these oscillations are not fully understood. Our aim was to determine whether and how these different oscillatory activities affect short-term synaptic plasticity (STP), using the olfactory system as a model. In response to odorant stimuli, the olfactory bulb displays a slow breathing rhythm as well as beta and gamma oscillations. Since the firing of olfactory bulb projecting neurons is phase-locked with beta and gamma oscillations, structures downstream from the olfactory bulb should be driven preferentially at these frequencies. We examined STP exhibited by olfactory bulb inputs in slices of adult mouse piriform cortex maintained in vitro in an in vivo-like ACSF (calcium concentration: 1.1 mM). We replaced the presynaptic neuronal firing rate by repeated electrical stimulation (frequency between 3.125 and 100 Hz) applied to the lateral olfactory tract. Our results revealed a considerable enhancement of postsynaptic response amplitude for stimulation frequencies in the beta and gamma range. A phenomenological model of STP fitted to the data suggests that the experimental results can be explained by the interplay between three mechanisms: a short-term facilitation mechanism (time constant ≈160 msec), and two short-term depression mechanisms (recovery time constants <20 msec and ≈140 msec). Increasing calcium concentration (2.2 mM) resulted in an increase in the time constant of facilitation and in a strengthening of the slowest depression mechanism. As a result, response enhancement was reduced and its peak shifted toward the low beta and alpha ranges while depression became predominant in the gamma band. Using environmental conditions corresponding to those that prevail in vivo, our study shows that STP in the lateral olfactory tract to layer Ia synapse allows amplification of olfactory bulb inputs at beta and gamma frequencies.

Decoding the orientation of contrast edges from MEG evoked and induced responses

Visual gamma oscillations have been proposed to subserve perceptual binding, but their strong modulation by diverse stimulus features confounds interpretations of their precise functional role. Overcoming this challenge necessitates a comprehensive account of the relationship between gamma responses and stimulus features. Here we used multivariate pattern analyses on human MEG data to characterize the relationships between gamma responses and one basic stimulus feature, the orientation of contrast edges. Our findings confirmed we could decode orientation information from induced responses in two dominant frequency bands at 24-32 Hz and 50-58 Hz. Decoding was higher for cardinal than oblique orientations, with similar results also obtained for evoked MEG responses. In contrast to multivariate analyses, orientation information was mostly absent in univariate signals: evoked and induced responses in early visual cortex were similar in all orientations, with only exception an inverse oblique effect observed in induced responses, such that cardinal orientations produced weaker oscillatory signals than oblique orientations. Taken together, our results showed multivariate methods are well suited for the analysis of gamma oscillations, with multivariate patterns robustly encoding orientation information and predominantly discriminating cardinal from oblique stimuli.

Interhemispheric Binding of Ambiguous Visual Motion Is Associated with Changes in Beta Oscillatory Activity but Not with Gamma Range Synchrony

In vision, perceptual features are processed in several regions distributed across the brain. Yet, the brain achieves a coherent perception of visual scenes and objects through integration of these features, which are encoded in spatially segregated brain areas. How the brain seamlessly achieves this accurate integration is currently unknown and is referred to as the "binding problem." Among the proposed mechanisms meant to resolve the binding problem, the binding-by-synchrony hypothesis proposes that binding is carried out by the synchronization of distant neuronal assemblies. This study aimed at providing a critical test to the binding-by-synchrony hypothesis by evaluating long-range connectivity using EEG during a motion integration visual task that entails binding across hemispheres. Our results show that large-scale perceptual binding is not associated with long-range interhemispheric gamma synchrony. However, distinct perceptual interpretations were found to correlate with changes in beta power. Increased beta activity was observed during binding under ambiguous conditions and originates mainly from parietal regions. These findings reveal that the visual experience of binding can be identified by distinct signatures of oscillatory activity, regardless of long-range gamma synchrony, suggesting that such type of synchrony does not underlie perceptual binding.

Cortical Spike Synchrony as a Measure of Input Familiarity

Spike synchrony, which occurs in various cortical areas in response to specific perception, action, and memory tasks, has sparked a long-standing debate on the nature of temporal organization in cortex. One prominent view is that this type of synchrony facilitates the binding or grouping of separate stimulus components. We argue instead for a more general function: a measure of the prior probability of incoming stimuli, implemented by long-range, horizontal, intracortical connections. We show that networks of this kind-pulse-coupled excitatory spiking networks in a noisy environment-can provide a sufficient substrate for stimulus-dependent spike synchrony. This allows for a quick (few spikes) estimate of the match between inputs and the input history as encoded in the network structure. Given the ubiquity of small, strongly excitatory subnetworks in cortex, we thus propose that many experimental observations of spike synchrony can be viewed as signs of input patterns that resemble long-term experience-that is, of patterns with high prior probability.

Cortical gamma band synchronization through somatostatin interneurons

Gamma band rhythms may synchronize distributed cell assemblies to facilitate information transfer within and across brain areas, yet their underlying mechanisms remain hotly debated. Most circuit models postulate that soma-targeting parvalbumin-positive GABAergic neurons are the essential inhibitory neuron subtype necessary for gamma rhythms. Using cell-type-specific optogenetic manipulations in behaving animals, we show that dendrite-targeting somatostatin (SOM) interneurons are critical for a visually induced, context-dependent gamma rhythm in visual cortex. A computational model independently predicts that context-dependent gamma rhythms depend critically on SOM interneurons. Further in vivo experiments show that SOM neurons are required for long-distance coherence across the visual cortex. Taken together, these data establish an alternative mechanism for synchronizing distributed networks in visual cortex. By operating through dendritic and not just somatic inhibition, SOM-mediated oscillations may expand the computational power of gamma rhythms for optimizing the synthesis and storage of visual perceptions.

Interplay of delay and multiplexing: Impact on cluster synchronization

Communication delays and multiplexing are ubiquitous features of real-world network systems. We here introduce a simple model where these two features are simultaneously present and report the rich phenomenology which is actually due to their interplay on cluster synchronization. A delay in one layer has non trivial impacts on the collective dynamics of the other layers, enhancing or suppressing synchronization. At the same time, multiplexing may also enhance cluster synchronization of delayed layers. We elucidate several nontrivial (and anti-intuitive) scenarios, which are of interest and potential application in various real-world systems, where the introduction of a delay may render synchronization of a layer robust against changes in the properties of the other layers.

Neural Architecture for Feature Binding in Visual Working Memory

Binding refers to the operation that groups different features together into objects. We propose a neural architecture for feature binding in visual working memory that employs populations of neurons with conjunction responses. We tested this model using cued recall tasks, in which subjects had to memorize object arrays composed of simple visual features (color, orientation, and location). After a brief delay, one feature of one item was given as a cue, and the observer had to report, on a continuous scale, one or two other features of the cued item. Binding failure in this task is associated with swap errors, in which observers report an item other than the one indicated by the cue. We observed that the probability of swapping two items strongly correlated with the items' similarity in the cue feature dimension, and found a strong correlation between swap errors occurring in spatial and nonspatial report. The neural model explains both swap errors and response variability as results of decoding noisy neural activity, and can account for the behavioral results in quantitative detail. We then used the model to compare alternative mechanisms for binding nonspatial features. We found the behavioral results fully consistent with a model in which nonspatial features are bound exclusively via their shared location, with no indication of direct binding between color and orientation. These results provide evidence for a special role of location in feature binding, and the model explains how this special role could be realized in the neural system.SIGNIFICANCE STATEMENT The problem of feature binding is of central importance in understanding the mechanisms of working memory. How do we remember not only that we saw a red and a round object, but that these features belong together to a single object rather than to different objects in our environment? Here we present evidence for a neural mechanism for feature binding in working memory, based on encoding of visual information by neurons that respond to the conjunction of features. We find clear evidence that nonspatial features are bound via space: we memorize directly where a color or an orientation appeared, but we memorize which color belonged with which orientation only indirectly by virtue of their shared location.

Human visual perceptual organization beats thinking on speed

What is the degree to which knowledge influences visual perceptual processes? This question, which is central to the seeing-versus-thinking debate in cognitive science, is often discussed using examples claimed to be proof of one stance or another. It has, however, also been muddled by the usage of different and unclear definitions of perception. Here, for the well-defined process of perceptual organization, I argue that including speed (or efficiency) into the equation opens a new perspective on the limits of top-down influences of thinking on seeing. While the input of the perceptual organization process may be modifiable and its output enrichable, the process itself seems so fast (or efficient) that thinking hardly has time to intrude and is effective mostly after the fact.

Synchronous spikes are necessary but not sufficient for a synchrony code in populations of spiking neurons

Synchronous activity in populations of neurons potentially encodes special stimulus features. Selective readout of either synchronous or asynchronous activity allows formation of two streams of information processing. Theoretical work predicts that such a synchrony code is a fundamental feature of populations of spiking neurons if they operate in specific noise and stimulus regimes. Here we experimentally test the theoretical predictions by quantifying and comparing neuronal response properties in tuberous and ampullary electroreceptor afferents of the weakly electric fish Apteronotus leptorhynchus These related systems show similar levels of synchronous activity, but only in the more irregularly firing tuberous afferents a synchrony code is established, whereas in the more regularly firing ampullary afferents it is not. The mere existence of synchronous activity is thus not sufficient for a synchrony code. Single-cell features such as the irregularity of spiking and the frequency dependence of the neuron's transfer function determine whether synchronous spikes possess a distinct meaning for the encoding of time-dependent signals.

Cell assemblies at multiple time scales with arbitrary lag constellations

Hebb's idea of a cell assembly as the fundamental unit of neural information processing has dominated neuroscience like no other theoretical concept within the past 60 years. A range of different physiological phenomena, from precisely synchronized spiking to broadly simultaneous rate increases, has been subsumed under this term. Yet progress in this area is hampered by the lack of statistical tools that would enable to extract assemblies with arbitrary constellations of time lags, and at multiple temporal scales, partly due to the severe computational burden. Here we present such a unifying methodological and conceptual framework which detects assembly structure at many different time scales, levels of precision, and with arbitrary internal organization. Applying this methodology to multiple single unit recordings from various cortical areas, we find that there is no universal cortical coding scheme, but that assembly structure and precision significantly depends on the brain area recorded and ongoing task demands.

DOI:http://dx.doi.org/10.7554/eLife.19428.001

Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events

Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate.

Disambiguation, Binding, and the Unity of Visual Consciousness

Recent findings in neuroscience strongly suggest that an object's features (e.g., its color, texture, shape, etc.) are represented in separate areas of the visual cortex. Although represented in separate neuronal areas, somehow the feature representations are brought together as a single, unified object of visual consciousness. This raises a question of binding: how do neural activities in separate areas of the visual cortex function to produce a feature-unified object of visual consciousness? Several prominent neuroscientists have adopted neural synchrony and attention-based approaches to explain object feature binding. I argue that although neural synchrony and/or attentional mechanisms might function to disambiguate an object's features, it is difficult to see how either of these mechanisms could fully explain the unity of an object's features at the level of visual consciousness. After presenting a detailed critique of neural synchrony and attention-based approaches to object feature binding, I propose interactive hierarchical structuralism (IHS) . This view suggests that a unified percept (i.e., a feature-unified object of visual consciousness) is not reducible to the activity of any cognitive capacity or to any localized neural area, but emerges out of the interaction of visual information organized by spatial structuring capacities correlated with lower, higher, and intermediate levels of the visual hierarchy. After clarifying different notions of emergence and elaborating evidence for IHS, I discuss how IHS can be tested through transcranial magnetic stimulation and masking. In the final section I present some further implications/advantages of IHS.

ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains

With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one potential model for such network processing. Previous work introduced a method for visualization of SSEs in massively parallel spike trains, based on an intersection matrix that contains in each entry the degree of overlap of active neurons in two corresponding time bins. Repeated SSEs are reflected in the matrix as diagonal structures of high overlap values. The method as such, however, leaves the task of identifying these diagonal structures to visual inspection rather than to a quantitative analysis. Here we present ASSET (Analysis of Sequences of Synchronous EvenTs), an improved, fully automated method which determines diagonal structures in the intersection matrix by a robust mathematical procedure. The method consists of a sequence of steps that i) assess which entries in the matrix potentially belong to a diagonal structure, ii) cluster these entries into individual diagonal structures and iii) determine the neurons composing the associated SSEs. We employ parallel point processes generated by stochastic simulations as test data to demonstrate the performance of the method under a wide range of realistic scenarios, including different types of non-stationarity of the spiking activity and different correlation structures. Finally, the ability of the method to discover SSEs is demonstrated on complex data from large network simulations with embedded synfire chains. Thus, ASSET represents an effective and efficient tool to analyze massively parallel spike data for temporal sequences of synchronous activity.

Coding of Sensory Signals by Neuronal Populations: The Role of Correlated Activity

How is sensory information encoded by the patterns of action potentials emitted by ensembles of neurons? Computational methods have recently been applied to this fundamental question and have found, both in the somatosensory and visual system, that the basic unit of information transmission is the timing of individual spikes. In systems studied to date, the neuronal population codes appear to be simple ones that do not rely on complex patterns of correlated spikes.

Neural mechanisms of selective attention in the somatosensory system

Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates.

More Gamma More Predictions: Gamma-Synchronization as a Key Mechanism for Efficient Integration of Classical Receptive Field Inputs with Surround Predictions

During visual stimulation, neurons in visual cortex often exhibit rhythmic and synchronous firing in the gamma-frequency (30–90 Hz) band. Whether this phenomenon plays a functional role during visual processing is not fully clear and remains heavily debated. In this article, we explore the function of gamma-synchronization in the context of predictive and efficient coding theories. These theories hold that sensory neurons utilize the statistical regularities in the natural world in order to improve the efficiency of the neural code, and to optimize the inference of the stimulus causes of the sensory data. In visual cortex, this relies on the integration of classical receptive field (CRF) data with predictions from the surround. Here we outline two main hypotheses about gamma-synchronization in visual cortex. First, we hypothesize that the precision of gamma-synchronization reflects the extent to which CRF data can be accurately predicted by the surround. Second, we hypothesize that different cortical columns synchronize to the extent that they accurately predict each other’s CRF visual input. We argue that these two hypotheses can account for a large number of empirical observations made on the stimulus dependencies of gamma-synchronization. Furthermore, we show that they are consistent with the known laminar dependencies of gamma-synchronization and the spatial profile of intercolumnar gamma-synchronization, as well as the dependence of gamma-synchronization on experience and development. Based on our two main hypotheses, we outline two additional hypotheses. First, we hypothesize that the precision of gamma-synchronization shows, in general, a negative dependence on RF size. In support, we review evidence showing that gamma-synchronization decreases in strength along the visual hierarchy, and tends to be more prominent in species with small V1 RFs. Second, we hypothesize that gamma-synchronized network dynamics facilitate the emergence of spiking output that is particularly information-rich and sparse.

Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks

Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura's and van Rossum's spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous synchronized network burst events that propagated between layers and highlight the potential applications of these MEMs devices as a tool for further investigation of structure and functional dynamics among neural populations.

Neuronal selectivity for spatial positions of offers and choices in five reward regions

When we evaluate an option, how is the neural representation of its value linked to information that identifies it, such as its position in space? We hypothesized that value information and identity cues are not bound together at a particular point but are represented together at the single unit level throughout the entirety of the choice process. We examined neuronal responses in two-option gambling tasks with lateralized and asynchronous presentation of offers in five reward regions: orbitofrontal cortex (OFC, area 13), ventromedial prefrontal cortex (vmPFC, area 14), ventral striatum (VS), dorsal anterior cingulate cortex (dACC), and subgenual anterior cingulate cortex (sgACC, area 25). Neuronal responses in all areas are sensitive to the positions of both offers and of choices. This selectivity is strongest in reward-sensitive neurons, indicating that it is not a property of a specialized subpopulation of cells. We did not find consistent contralateral or any other organization to these responses, indicating that they may be difficult to detect with aggregate measures like neuroimaging or studies of lesion effects. These results suggest that value coding is wed to factors that identify the object throughout the reward system and suggest a possible solution to the binding problem raised by abstract value encoding schemes.

Neural Networks for Beat Perception in Musical Rhythm

Entrainment of cortical rhythms to acoustic rhythms has been hypothesized to be the neural correlate of pulse and meter perception in music. Dynamic attending theory first proposed synchronization of endogenous perceptual rhythms nearly 40 years ago, but only recently has the pivotal role of neural synchrony been demonstrated. Significant progress has since been made in understanding the role of neural oscillations and the neural structures that support synchronized responses to musical rhythm. Synchronized neural activity has been observed in auditory and motor networks, and has been linked with attentional allocation and movement coordination. Here we describe a neurodynamic model that shows how self-organization of oscillations in interacting sensory and motor networks could be responsible for the formation of the pulse percept in complex rhythms. In a pulse synchronization study, we test the model's key prediction that pulse can be perceived at a frequency for which no spectral energy is present in the amplitude envelope of the acoustic rhythm. The result shows that participants perceive the pulse at the theoretically predicted frequency. This model is one of the few consistent with neurophysiological evidence on the role of neural oscillation, and it explains a phenomenon that other computational models fail to explain. Because it is based on a canonical model, the predictions hold for an entire family of dynamical systems, not only a specific one. Thus, this model provides a theoretical link between oscillatory neurodynamics and the induction of pulse and meter in musical rhythm.

Electrophysiological and firing properties of neurons: Categorizing soloists and choristers in primary visual cortex

Visual processing in the cortex involves various aspects of neuronal properties such as morphological, electrophysiological and molecular. In particular, the neural firing pattern is an important indicator of dynamic circuitry within a neuronal population. Indeed, in microcircuits, neurons act as soloists or choristers wherein the characteristical activity of a 'soloist' differs from the firing pattern of a 'chorister'. Both cell types correlate their respective firing rate with the global populational activity in a unique way. In the present study, we sought to examine the relationship between the spike shape (thin spike neurons and broad spike neurons) of cortical neurons recorded from V1, their firing levels and their propensity to act as soloists or choristers. We found that thin spike neurons, which exhibited higher levels of firing, generally correlate their activity with the neuronal population (choristers). On the other hand, broad spike neurons showed lower levels of firing and demonstrated weak correlations with the assembly (soloists). A major consequence of the present study is: estimating the correlation of neural spike trains with their neighboring population is a predictive indicator of spike waveforms and firing level. Indeed, we found a continuum distribution of coupling strength ranging from weak correlation-strength (attributed to low-firing neurons) to high correlation-strength (attributed to high-firing neurons). The tendency to exhibit high- or low-firing is conducive to the spike shape of neurons. Our results offer new insights into visual processing by showing how high-firing rate neurons (mostly thin spike neurons) could modulate the neuronal responses within cell-assemblies.

The visual system's internal model of the world

The Bayesian paradigm has provided a useful conceptual theory for understanding perceptual computation in the brain. While the detailed neural mechanisms of Bayesian inference are not fully understood, recent computational and neurophysiological works have illuminated the underlying computational principles and representational architecture. The fundamental insights are that the visual system is organized as a modular hierarchy to encode an internal model of the world, and that perception is realized by statistical inference based on such internal model. In this paper, I will discuss and analyze the varieties of representational schemes of these internal models and how they might be used to perform learning and inference. I will argue for a unified theoretical framework for relating the internal models to the observed neural phenomena and mechanisms in the visual cortex.

Population responses in V1 encode different figures by response amplitude

The visual system simultaneously segregates between several objects presented in a visual scene. The neural code for encoding different objects or figures is not well understood. To study this question, we trained two monkeys to discriminate whether two elongated bars are either separate, thus generating two different figures, or connected, thus generating a single figure. Using voltage-sensitive dyes, we imaged at high spatial and temporal resolution V1 population responses evoked by the two bars, while keeping their local attributes similar among the two conditions. In the separate condition, unlike the connected condition, the population response to one bar is enhanced, whereas the response to the other is simultaneously suppressed. The response to the background remained unchanged between the two conditions. This divergent pattern developed ∼200 ms poststimulus onset and could discriminate well between the separate and connected single trials. The stimulus separation saliency and behavioral report were highly correlated with the differential response to the bars. In addition, the proximity and/or the specific location of the connectors seemed to have only a weak effect on this late activity pattern, further supporting the involvement of top-down influences. Additional neural codes were less informative about the separate and connected conditions, with much less consistency and discriminability compared with a response amplitude code. We suggest that V1 is involved in the encoding of each figure by different neuronal response amplitude, which can mediate their segregation and perception.

False discovery rate regression: an application to neural synchrony detection in primary visual cortex

Many approaches for multiple testing begin with the assumption that all tests in a given study should be combined into a global false-discovery-rate analysis. But this may be inappropriate for many of today's large-scale screening problems, where auxiliary information about each test is often available, and where a combined analysis can lead to poorly calibrated error rates within different subsets of the experiment. To address this issue, we introduce an approach called false-discovery-rate regression that directly uses this auxiliary information to inform the outcome of each test. The method can be motivated by a two-groups model in which covariates are allowed to influence the local false discovery rate, or equivalently, the posterior probability that a given observation is a signal. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the paper.

Neural networks and neuroscience-inspired computer vision

Brains are, at a fundamental level, biological computing machines. They transform a torrent of complex and ambiguous sensory information into coherent thought and action, allowing an organism to perceive and model its environment, synthesize and make decisions from disparate streams of information, and adapt to a changing environment. Against this backdrop, it is perhaps not surprising that computer science, the science of building artificial computational systems, has long looked to biology for inspiration. However, while the opportunities for cross-pollination between neuroscience and computer science are great, the road to achieving brain-like algorithms has been long and rocky. Here, we review the historical connections between neuroscience and computer science, and we look forward to a new era of potential collaboration, enabled by recent rapid advances in both biologically-inspired computer vision and in experimental neuroscience methods. In particular, we explore where neuroscience-inspired algorithms have succeeded, where they still fail, and we identify areas where deeper connections are likely to be fruitful.

Behavioural correlates of combinatorial versus temporal features of odour codes

Most sensory stimuli evoke spiking responses that are distributed across neurons and are temporally structured. Whether the temporal structure of ensemble activity is modulated to facilitate different neural computations is not known. Here, we investigated this issue in the insect olfactory system. We found that an odourant can generate synchronous or asynchronous spiking activity across a neural ensemble in the antennal lobe circuit depending on its relative novelty with respect to a preceding stimulus. Regardless of variations in temporal spiking patterns, the activated combinations of neurons robustly represented stimulus identity. Consistent with this interpretation, locusts reliably recognized both solitary and sequential introductions of trained odourants in a quantitative behavioural assay. However, predictable behavioural responses across locusts were observed only to novel stimuli that evoked synchronized spiking patterns across neural ensembles. Hence, our results indicate that the combinatorial ensemble response encodes for stimulus identity, whereas the temporal structure of the ensemble response selectively emphasizes novel stimuli.

In the olfactory system, odourants typically evoke spiking responses in neurons that are both spatially and temporally structured. Here, the authors demonstrate that odour identity is encoded purely by the combinations of neurons activated and is insensitive to changes in temporal structure.

Long-range neural synchrony in behavior

Long-range synchrony between distant brain regions accompanies multiple forms of behavior. This review compares and contrasts the methods by which long-range synchrony is evaluated in both humans and model animals. Three examples of behaviorally relevant long-range synchrony are discussed in detail: gamma-frequency synchrony during visual perception, hippocampal-prefrontal synchrony during working memory, and prefrontal-amygdala synchrony during anxiety. Implications for circuit mechanism, translation, and clinical relevance are discussed.

Human intracranial high-frequency activity during memory processing: neural oscillations or stochastic volatility?

Intracranial high-frequency activity (HFA), which refers to fast fluctuations in electrophysiological recordings, increases during memory processing. Two views have emerged to explain this effect: (1) HFA reflects a synchronous signal, related to underlying gamma oscillations, that plays a mechanistic role in human memory and (2) HFA reflects an asynchronous signal that is a non-specific marker of brain activation. We review recent data supporting each of these views and conclude that HFA during memory processing is more consistent with an asynchronous signal. Memory-related HFA is therefore best conceptualized as a biomarker of neural activation that can functionally map memory with high spatial and temporal precision.

Population coding in area V4 during rapid shape detections

While previous studies have suggested that neuronal correlations are common in visual cortex over a range of timescales, the effect of correlations on rapid visually based decisions has received little attention. We trained Macaca mulatta to saccade to a peripherally presented shape embedded in dynamic noise as soon as the shape appeared. While the monkeys performed the task, we recorded from neuronal populations (5-29 cells) using a microelectrode array implanted in area V4, a visual area thought to be involved in form perception. While modest correlations were present between cells during visual stimulation, their magnitude did not change significantly subsequent to the appearance of a shape. We quantified the reliability and temporal precision with which neuronal populations signaled the appearance of the shape and predicted the animals' choices using mutual information analyses. To study the impact of correlations, we shuffled the activity from each cell across observations while retaining stimulus-dependent modulations in firing rate. We found that removing correlations by shuffling across trials minimally affected the reliability or timing with which pairs, or larger groups of cells, signaled the presence of a shape. To assess the downstream impact of correlations, we also studied how shuffling affected the ability of V4 populations to predict behavioral choices. Surprisingly, shuffling created a modest increase in the accuracy of such predictions, suggesting that the reliability of downstream neurons is slightly compromised by activity correlations. Our findings are consistent with neuronal correlations having a minimal effect on the reliability and timing of rapid perceptual decisions.