Spatial EEG patterns, non-linear dynamics and perception: the neo-Sherringtonian view

Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex

Biological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons. Recent models suggest that these assemblies contain both excitatory and inhibitory neurons (E/I assemblies), resulting in co-tuning and precise balance of excitation and inhibition. To understand computational consequences of E/I assemblies under biologically realistic constraints we built a spiking network model based on experimental data from telencephalic area Dp of adult zebrafish, a precisely balanced recurrent network homologous to piriform cortex. We found that E/I assemblies stabilized firing rate distributions compared to networks with excitatory assemblies and global inhibition. Unlike classical memory models, networks with E/I assemblies did not show discrete attractor dynamics. Rather, responses to learned inputs were locally constrained onto manifolds that 'focused' activity into neuronal subspaces. The covariance structure of these manifolds supported pattern classification when information was retrieved from selected neuronal subsets. Networks with E/I assemblies therefore transformed the geometry of neuronal coding space, resulting in continuous representations that reflected both relatedness of inputs and an individual's experience. Such continuous representations enable fast pattern classification, can support continual learning, and may provide a basis for higher-order learning and cognitive computations.

Milking a spherical cow: Toy models in neuroscience

There are many different kinds of models, and they play many different roles in the scientific endeavour. Neuroscience, and biology more generally, has understandably tended to emphasise empirical models that are grounded in data and make specific, experimentally testable predictions. Meanwhile, strongly idealised or 'toy' models have played a central role in the theoretical development of other sciences such as physics. In this paper, we examine the nature of toy models and their prospects in neuroscience.

The Wire Is Not the Territory: Understanding Representational Drift in Olfaction With Dynamical Systems Theory

Representational drift is a phenomenon of increasing interest in the cognitive and neural sciences. While investigations are ongoing for other sensory cortices, recent research has demonstrated the pervasiveness in which it occurs in the piriform cortex for olfaction. This gradual weakening and shifting of stimulus-responsive cells has critical implications for sensory stimulus-response models and perceptual decision-making. While representational drift may complicate traditional sensory processing models, it could be seen as an advantage in olfaction, as animals live in environments with constantly changing and unpredictable chemical information. Non-topographical encoding in the olfactory system may aid in contextualizing reactions to promiscuous odor stimuli, facilitating adaptive animal behavior and survival. This article suggests that traditional models of stimulus-(neural) response mapping in olfaction may need to be reevaluated and instead motivates the use of dynamical systems theory as a methodology and conceptual framework.

Principles of large-scale neural interactions

What mechanisms underlie flexible inter-areal communication in the cortex? We consider four mechanisms for temporal coordination and their contributions to communication: (1) Oscillatory synchronization (communication-through-coherence); (2) communication-through-resonance; (3) non-linear integration; and (4) linear signal transmission (coherence-through-communication). We discuss major challenges for communication-through-coherence based on layer- and cell-type-specific analyses of spike phase-locking, heterogeneity of dynamics across networks and states, and computational models for selective communication. We argue that resonance and non-linear integration are viable alternative mechanisms that facilitate computation and selective communication in recurrent networks. Finally, we consider communication in relation to cortical hierarchy and critically examine the hypothesis that feedforward and feedback communication use fast (gamma) and slow (alpha/beta) frequencies, respectively. Instead, we propose that feedforward propagation of prediction errors relies on the non-linear amplification of aperiodic transients, whereas gamma and beta rhythms represent rhythmic equilibrium states that facilitate sustained and efficient information encoding and amplification of short-range feedback via resonance.

Mechanisms and functions of respiration-driven gamma oscillations in the primary olfactory cortex

Gamma oscillations are believed to underlie cognitive processes by shaping the formation of transient neuronal partnerships on a millisecond scale. These oscillations are coupled to the phase of breathing cycles in several brain areas, possibly reflecting local computations driven by sensory inputs sampled at each breath. Here, we investigated the mechanisms and functions of gamma oscillations in the piriform (olfactory) cortex of awake mice to understand their dependence on breathing and how they relate to local spiking activity. Mechanistically, we find that respiration drives gamma oscillations in the piriform cortex, which correlate with local feedback inhibition and result from recurrent connections between local excitatory and inhibitory neuronal populations. Moreover, respiration-driven gamma oscillations are triggered by the activation of mitral/tufted cells in the olfactory bulb and are abolished during ketamine/xylazine anesthesia. Functionally, we demonstrate that they locally segregate neuronal assemblies through a winner-take-all computation leading to sparse odor coding during each breathing cycle. Our results shed new light on the mechanisms of gamma oscillations, bridging computation, cognition, and physiology.

Prediction of Seizure Recurrence. A Note of Caution

Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism.

Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

Localization of movable electrodes in a multi-electrode microdrive in nonhuman primates

BACKGROUND

Recently, large-scale semi-chronic recording systems have been developed, unique in their capability to record simultaneously from multiple individually moveable electrodes. As these recording systems can cover a large area, knowledge of the exact location of each individual electrode is crucial. Currently, the only method of keeping track of electrode depth and thus location is through detailed notebook keeping on neural activity.

NEW METHOD

We have improved the electrode localization by combining pre- and postoperative anatomical magnetic resonance imaging (MRI) scans with high resolution computed tomography (CT) scans throughout the experiment, and validated our method by comparing the resulting location estimates with traditional notebook-keeping. Finally, the actual location of a selection of electrodes was marked at the end of the experiment by creating small metallic depositions using electrical stimulation, and thereby made visible on MRI.

RESULTS

Combining CT scans with a high resolution, artefact reducing sequence during the experiment with a preoperative MRI scan provides crucial information about the exact electrode location of multielectrode arrays with individually moveable electrodes.

COMPARISON WITH EXISTING METHODS

The information obtained from the hybrid CT-MR image and the notes on spiking activity showed a similar pattern, with the clear advantage of the visualization of the exact position of the electrodes using our method.

CONCLUSIONS

The described technique allows for a precise anatomical identification of the recorded brain areas and thus to draw strong conclusions about the role of each targeted cortical area in the behavior under study.

Modeling effects of neural fluctuations and inter-scale interactions

One of the greatest challenges to science, in particular, to neuroscience, is to understand how processes at different levels of organization are related to each other. In connection with this problem is the question of the functional significance of fluctuations, noise, and chaos. This paper deals with three related issues: (1) how processes at different organizational levels of neural systems might be related, (2) the functional significance of non-linear neurodynamics, including oscillations, chaos, and noise, and (3) how computational models can serve as useful tools in elucidating these types of issues. In order to capture and describe phenomena at different micro (molecular), meso (cellular), and macro (network) scales, the computational models need to be of appropriate complexity making use of available experimental data. I exemplify by two major types of computational models, those of Hans Braun and colleagues and those of my own group, which both aim at bridging gaps between different levels of neural systems. In particular, the constructive role of noise and chaos in such systems is modelled and related to functions, such as sensation, perception, learning/memory, decision making, and transitions between different (un-)conscious states. While there is, in general, a focus on upward causation, I will also discuss downward causation, where higher level activity may affect the activity at lower levels, which should be a condition for any functional role of consciousness and free will, often considered to be problematic to science.

A coupled-oscillator model of olfactory bulb gamma oscillations

The olfactory bulb transforms not only the information content of the primary sensory representation, but also its underlying coding metric. High-variance, slow-timescale primary odor representations are transformed by bulbar circuitry into secondary representations based on principal neuron spike patterns that are tightly regulated in time. This emergent fast timescale for signaling is reflected in gamma-band local field potentials, presumably serving to efficiently integrate olfactory sensory information into the temporally regulated information networks of the central nervous system. To understand this transformation and its integration with interareal coordination mechanisms requires that we understand its fundamental dynamical principles. Using a biophysically explicit, multiscale model of olfactory bulb circuitry, we here demonstrate that an inhibition-coupled intrinsic oscillator framework, pyramidal resonance interneuron network gamma (PRING), best captures the diversity of physiological properties exhibited by the olfactory bulb. Most importantly, these properties include global zero-phase synchronization in the gamma band, the phase-restriction of informative spikes in principal neurons with respect to this common clock, and the robustness of this synchronous oscillatory regime to multiple challenging conditions observed in the biological system. These conditions include substantial heterogeneities in afferent activation levels and excitatory synaptic weights, high levels of uncorrelated background activity among principal neurons, and spike frequencies in both principal neurons and interneurons that are irregular in time and much lower than the gamma frequency. This coupled cellular oscillator architecture permits stable and replicable ensemble responses to diverse sensory stimuli under various external conditions as well as to changes in network parameters arising from learning-dependent synaptic plasticity.

Development of Odor Hedonics: Experience-Dependent Ontogeny of Circuits Supporting Maternal and Predator Odor Responses in Rats

A major component of perception is hedonic valence: perceiving stimuli as pleasant or unpleasant. Here, we used early olfactory experiences that shape odor preferences and aversions to explore developmental plasticity in circuits mediating odor hedonics. We used 2-deoxyglucose autoradiographic mapping of neural activity to identify circuits differentially activated by biologically relevant preferred and avoided odors across rat development. We then further probed this system by increasing or decreasing hedonic value. Using both region of interest and functional connectivity analyses, we identified regions within primary olfactory, amygdala/hippocampal, and prefrontal cortical networks that were activated differentially by maternal and male odors. Although some activated regions remained stable across development (postnatal days 7-23), there was a developmental emergence of others that resulted in an age-dependent elaboration of hedonic-response-specific circuitry despite stable behavioral responses (approach/avoidance) to the odors across age. Hedonic responses to these biologically important odors were modified through diet suppression of the maternal odor and co-rearing with a male. This allowed assessment of hedonic circuits in isolation of the specific odor quality and/or intensity. Early experience significantly modified odor-evoked circuitry in an age-dependent manner. For example, co-rearing with a male, which induced pup attraction to male odor, reduced activity in amygdala regions normally activated by the unfamiliar avoided male odor, making this region more consistent with maternal odor. Understanding the development of odor hedonics, particularly within the context of altered early life experience, provides insight into the development of sensory processes, food preferences, and the formation of social affiliations, among other behaviors.

SIGNIFICANCE STATEMENT

Odor hedonic valence controls approach-avoidance behaviors, but also modulates ongoing behaviors ranging from food preferences and social affiliation with the caregiver to avoidance of predator odors. Experiences can shape hedonic valence. This study explored brain circuitry involved in odor hedonic encoding throughout development using maternal and predator odors and assessed the effects of early life experience on odor hedonic encoding by increasing/decreasing the hedonic value of these odors. Understanding the role of changing brain circuitry during development and its impact on behavioral function is critical for understanding sensory processing across development. These data converge with exciting literature on the brain's hedonic network and highlight the significant role of early life experience in shaping the neural networks of highly biologically relevant stimuli.

From globally coupled maps to complex-systems biology

Studies of globally coupled maps, introduced as a network of chaotic dynamics, are briefly reviewed with an emphasis on novel concepts therein, which are universal in high-dimensional dynamical systems. They include clustering of synchronized oscillations, hierarchical clustering, chimera of synchronization and desynchronization, partition complexity, prevalence of Milnor attractors, chaotic itinerancy, and collective chaos. The degrees of freedom necessary for high dimensionality are proposed to equal the number in which the combinatorial exceeds the exponential. Future analysis of high-dimensional dynamical systems with regard to complex-systems biology is briefly discussed.

[To err is human? Interests of chaotic models to study adult psychiatric disorders and developmental disorders]

BACKGROUND

Many clinical and biological parameters have nonlinear chaotic fluctuations. These variations result in unexpected pseudo-random transitions. In these models, few risk factors can lead to unexpected phenomena if oscillations and self-reinforcement patterns occur. Complex rhythms could ease the ability of a physiological system to adapt and react quickly to a constantly changing environment.

OBJECTIVES

It has been proposed that several psychiatric disorders and developmental disorders are characterized by a loss of complex rhythm in favor of a more organized pattern. We examine evidence to support these assumptions in literatures.

METHODS

We performed a literature review of the main computerized databases (Medline, PubMed) and manual searches of the literature concerning non dynamic rhythms in time series analysis, in adults with psychiatric disorder and children with developmental disorder. These results were interpreted through a developmental approach that highlights the role of the learning process in the emergence of abilities.

RESULTS

Analysis of clinical scores and electroencephalographic data have found that subjects with bipolar disorder or schizophrenia, tested over a time series, have lower chaotic rhythms compared with healthy subjects. Growing children share several properties of a complex system: the interdependence of developmental axes (motor, emotional, language, social skills), multiple hierarchical levels (i.e. genetic, biological, environmental, and cultural), the two-way transactions between the child and his environment, and the sensitivity to initial conditions. This could explain the difficulty to predict the emergence of abilities or the long-term prognosis of impairment in children. This limitation is not only due to errors in the explanatory model or the lack of explanatory variable. It is also caused by instability, which is a core characteristic of a chaotic system.

CONCLUSION

The study of chaotic rhythms in time-series clinical and nonclinical data (e.g. EEG, functional neuroimaging) could improve the prediction of an acute event, such as relapse of mood disorder. Moreover, the complex rhythms in children may play a major part in synchronicity during interactions with a caregiver, held as essential for later development of self-regulation skills, such as emotional stability.

EVENT-RELATED COMPLEXITY ANALYSIS AND ITS APPLICATION IN THE DETECTION OF FACIAL ATTRACTIVENESS

In this study, an event-related complexity (ERC) analysis method is proposed and used to explore the neural correlates of facial attractiveness detection in the context of a cognitive experiment. The ERC method gives a quantitative index for measuring the diverse brain activation properties that represent the neural correlates of event-related responses. This analysis reveals distinct effects of facial attractiveness processing and also provides further information that could not have been achieved from event-related potential alone.

Multistate network model for the pathfinding problem with a self-recovery property

In this study, we propose a continuous model for a pathfinding system. We consider acyclic graphs whose vertices are connected by unidirectional edges. The proposed model autonomously finds a path connecting two specified vertices, and the path is represented by a stable solution of the proposed model. The system has a self-recovery property, i.e., the system can find a path when one of the connections in the existing path is suddenly terminated. Further, we demonstrate that the appropriate installation of inhibitory interaction improves the search time.

Multistate network for loop searching system with self-recovery property

We propose a network of excitable systems that spontaneously initiates and completes loop searching against the removal and attachment of connection links. Network nodes are excitable systems of the FitzHugh-Nagumo type that have three equilibrium states depending on input from other nodes. The attractors of this network are stationary solutions that form loops, except in the case of an acyclic network. Thus, the system is regarded as a loop searching system. To design a system capable of self-recovery (the ability to find a loop when one of the connections in an existing loop is suddenly removed), we have investigated regulatory rules for the interaction between nodes and have used two characteristic properties of nonlinear dynamical systems to provide a solution: postinhibitory rebound phenomena and saddle-node bifurcation.

High-frequency neural oscillations and visual processing deficits in schizophrenia

Visual information is fundamental to how we understand our environment, make predictions, and interact with others. Recent research has underscored the importance of visuo-perceptual dysfunctions for cognitive deficits and pathophysiological processes in schizophrenia. In the current paper, we review evidence for the relevance of high frequency (beta/gamma) oscillations towards visuo-perceptual dysfunctions in schizophrenia. In the first part of the paper, we examine the relationship between beta/gamma band oscillations and visual processing during normal brain functioning. We then summarize EEG/MEG-studies which demonstrate reduced amplitude and synchrony of high-frequency activity during visual stimulation in schizophrenia. In the final part of the paper, we identify neurobiological correlates as well as offer perspectives for future research to stimulate further inquiry into the role of high-frequency oscillations in visual processing impairments in the disorder.

The embodied transcendental: a Kantian perspective on neurophenomenology

Neurophenomenology is a research programme aimed at bridging the explanatory gap between first-person subjective experience and neurophysiological third-person data, through an embodied and enactive approach to the biology of consciousness. The present proposal attempts to further characterize the bodily basis of the mind by adopting a naturalistic view of the phenomenological concept of intentionality as the a priori invariant character of any lived experience. Building on the Kantian definition of transcendentality as “what concerns the a priori formal structures of the subject's mind” and as a precondition for the very possibility of human knowledge, we will suggest that this transcendental core may in fact be rooted in biology and can be examined within an extension of the theory of autopoiesis. The argument will be first clarified by examining its application to previously proposed elementary autopoietic models, to the bacterium, and to the immune system; it will be then further substantiated and illustrated by examining the mirror-neuron system and the default mode network as biological instances exemplifying the enactive nature of knowledge, and by discussing the phenomenological aspects of selected neurological conditions (neglect, schizophrenia). In this context, the free-energy principle proposed recently by Karl Friston will be briefly introduced as a rigorous, neurally-plausible framework that seems to accomodate optimally these ideas. While our approach is biologically-inspired, we will maintain that lived first-person experience is still critical for a better understanding of brain function, based on our argument that the former and the latter share the same transcendental structure. Finally, the role that disciplined contemplative practices can play to this aim, and an interpretation of the cognitive processes taking place during meditation under this perspective, will be also discussed.

A review of gamma oscillations in healthy subjects and in cognitive impairment

This review describes a wide range of functional correlates of gamma oscillations in whole-brain work, in neuroethology, sensory-cognitive dynamics, emotion, and cognitive impairment. This survey opens a new window towards understanding the brain's gamma activity. Gamma responses are selectively distributed in the whole brain, and do not reflect only a unique, specific function of the nervous system. Sensory responses from cortex, thalamus, hippocampus, and reticular formations in animal and human brains, and also cognitive responses, were described by several authors. According to reviewed results, it becomes obvious that cognitive disorders, and medication-which influence the transmitter release-change entirely the understanding of the big picture in cognitive processes. Gamma activity is evoked or induced by different sensory stimuli or cognitive tasks. Thus, it is argued that gamma-band synchronization is an elementary and fundamental process in whole-brain operation. In conclusion, reasoning and suggestions for understanding gamma activity are highlighted.

Three-state network design for robust loop-searching systems

Self-recovery of function is one of the remarkable properties of biological systems, and its implementation in autonomous distributed systems is highly desirable. In this study, we propose an autonomous distributed system which is capable of searching for closed loops in a network in which nodes are connected by unidirectional paths. A closed loop is defined as a phase synchronization of a group of oscillators belonging to the corresponding nodes. In addition, to develop a system capable of self-recovery (the ability to find a loop when one of the connections in the existing loop is suddenly removed), we have developed regulatory rules for the interaction between nodes. These rules are used to construct a regulation network in which the stability of nodes and that of loops are mutually sustained, allowing the system to function as a loop-searching system with self-recovery properties.

Letting the brain speak for itself

Metaphors of Computation and Information tended to detract attention from the intrinsic modes of neural system functions, uncontaminated by the observer's role in collection, and interpretation of experimental data. Recognizing the self-referential mode of function, and the propensity for self-organization to critical states requires a fundamentally new orientation, based on Complex System Dynamics as non-ergodic, non-stationary processes with inverse-power-law statistical distributions. Accordingly, local cooperative processes, intrinsic to neural structures, and of fractal nature, call for applying Fractional Calculus and models of Random Walks with long-term memory in Theoretical Neuroscience studies.

Functions for adult neurogenesis in memory: an introduction to the neurocomputational approach and to its contribution

Until recently, it was believed that the introduction of new neurons in neuronal networks was incompatible with memory function. Since the rediscovery of adult hippocampal neurogenesis, behavioral data demonstrate that adult neurogenesis is required for memory processing. We examine neurocomputational studies to identify which basic mechanisms involved in memory might be mediated by adult neurogenesis. Mainly, adult neurogenesis might be involved in the reduction of catastrophic interference and in a time-related pattern separation function. Artificial neuronal networks suggest that the selective recruitment of new-born or old neurons is not stochastic, but depends on environmental requirements. This leads us to propose the novel concept of "soft-supervision". Soft-supervision would be a biologically plausible process, by which the environment is able to influence activation and learning rules of neurons differentially.

Consciousness, explanatory inversion, and cognitive science

Cognitive science typically postulates unconscious mental phenomena, computational or otherwise, to explain cognitive capacities. The mental phenomena in question are supposed to be inaccessible in principle to consciousness. I try to show that this is a mistake, because all unconscious intentionality must be accessible in principle to consciousness; we have no notion of intrinsic intentionality except in terms of its accessibility to consciousness. I call this claim the “Connection Principle.” The argument for it proceeds in six steps. The essential point is that intrinsic intentionality has aspectual shape: Our mental representations represent the world under specific aspects, and these aspectual features are essential to a mental state's being the state that it is.

Once we recognize the Connection Principle, we see that it is necessary to perform an inversion on the explanatory models of cognitive science, an inversion analogous to the one evolutionary biology imposes on preDarwinian animistic modes of explanation. In place of the original intentionalistic explanations we have a combination of hardware and functional explanations. This radically alters the structure of explanation, because instead of a mental representation (such as a rule) causing the pattern of behavior it represents (such as rule-governed behavior), there is a neurophysiological cause of a pattern (such as a pattern of behavior), and the pattern plays a functional role in the life of the organism. What we mistakenly thought were descriptions of underlying mental principles in, for example, theories of vision and language were in fact descriptions of functional aspects of systems, which will have to be explained by underlying neurophysiological mechanisms. In such cases, what looks like mentalistic psychology is sometimes better construed as speculative neurophysiology. The moral is that the big mistake in cognitive science is not the overestimation of the computer metaphor (though that is indeed a mistake) but the neglect of consciousness.

Ways of coloring: Comparative color vision as a case study for cognitive science

Different explanations of color vision favor different philosophical positions: Computational vision is more compatible with objectivism (the color is in the object), psychophysics and neurophysiology with subjectivism (the color is in the head). Comparative research suggests that an explanation of color must be both experientialist (unlike objectivism) and ecological (unlike subjectivism). Computational vision's emphasis on optimally “recovering” prespecified features of the environment (i.e., distal properties, independent of the sensory-motor capacities of the animal) is unsatisfactory. Conceiving of visual perception instead as the visual guidance of activity in an environment that is determined largely by that very activity suggests new directions for research.