Nonlinear brain dynamics as macroscopic manifestation of underlying many-body field dynamics

Neural activity patterns related to behavior occur at many scales in time and space from the atomic and molecular to the whole brain. Here we explore the feasibility of interpreting neurophysiological data in the context of many-body physics by using tools that physicists have devised to analyze comparable hierarchies in other fields of science. We focus on a mesoscopic level that offers a multi-step pathway between the microscopic functions of neurons and the macroscopic functions of brain systems revealed by hemodynamic imaging. We use electroencephalographic (EEG) records collected from high-density electrode arrays fixed on the epidural surfaces of primary sensory and limbic areas in rabbits and cats trained to discriminate conditioned stimuli (CS) in the various modalities. High temporal resolution of EEG signals with the Hilbert transform gives evidence for diverse intermittent spatial patterns of amplitude (AM) and phase modulations (PM) of carrier waves that repeatedly re-synchronize in the beta and gamma ranges at near zero time lags over long distances. The dominant mechanism for neural interactions by axodendritic synaptic transmission should impose distance-dependent delays on the EEG oscillations owing to finite propagation velocities. It does not. EEGs instead show evidence for anomalous dispersion: the existence in neural populations of a low velocity range of information and energy transfers, and a high velocity range of the spread of phase transitions. This distinction labels the phenomenon but does not explain it. In this report we explore the analysis of these phenomena using concepts of energy dissipation, the maintenance by cortex of multiple ground states corresponding to AM patterns, and the exclusive selection by spontaneous breakdown of symmetry (SBS) of single states in sequences.Comment: 31 page

The aesthetic experience as a characteristic feature of brain dynamics

The brain constructs within itself an understanding of its surround which constitutes its own world. This is described as its Double in the frame of the dissipative quantum model of brain, where the perception-action arc in the Merleau-Ponty’s phenomenology of perception finds its formal description. In the dialog with the Double, the continuous attempt to reach the equilibrium shows that the real goal pursued by the brain activity is the aesthetical experience, the most harmonious “to-be-in-the-world” reached through reciprocal actions, the aesthetical dimension characterized by the “pleasure” of the perception. Aesthetical pleasure unavoidably implies disclosure, to manifest “signs”, artistic communication. An interpersonal, collective level of consciousness then arises, a larger stage where the actors are mutually dependent. The coherent structure of the brain background state manifests itself in the auto-similarity properties of fractal structures. These are observed to occur also in a large number of natural phenomena and systems. The conception of Nature divided in separated domains is replaced by the vision of Nature unified by laws of form implied by the underlying quantum dynamics of the coherent vacuum, an integrated ecological vision

From mechanisms to markers: novel noninvasive EEG proxy markers of the neural excitation and inhibition system in humans.

Brain function is a product of the balance between excitatory and inhibitory (E/I) brain activity. Variation in the regulation of this activity is thought to give rise to normal variation in human traits, and disruptions are thought to potentially underlie a spectrum of neuropsychiatric conditions (e.g., Autism, Schizophrenia, Downs' Syndrome, intellectual disability). Hypotheses related to E/I dysfunction have the potential to provide cross-diagnostic explanations and to combine genetic and neurological evidence that exists within and between psychiatric conditions. However, the hypothesis has been difficult to test because: (1) it lacks specificity-an E/I dysfunction could pertain to any level in the neural system- neurotransmitters, single neurons/receptors, local networks of neurons, or global brain balance - most researchers do not define the level at which they are examining E/I function; (2) We lack validated methods for assessing E/I function at any of these neural levels in humans. As a result, it has not been possible to reliably or robustly test the E/I hypothesis of psychiatric disorders in a large cohort or longitudinal patient studies. Currently available, in vivo markers of E/I in humans either carry significant risks (e.g., deep brain electrode recordings or using Positron Emission Tomography (PET) with radioactive tracers) and/or are highly restrictive (e.g., limited spatial extent for Transcranial Magnetic Stimulation (TMS) and Magnetic Resonance Spectroscopy (MRS). More recently, a range of novel Electroencephalography (EEG) features has been described, which could serve as proxy markers for E/I at a given level of inference. Thus, in this perspective review, we survey the theories and experimental evidence underlying 6 novel EEG markers and their biological underpinnings at a specific neural level. These cheap-to-record and scalable proxy markers may offer clinical utility for identifying subgroups within and between diagnostic categories, thus directing more tailored sub-grouping and, therefore, treatment strategies. However, we argue that studies in clinical populations are premature. To maximize the potential of prospective EEG markers, we first need to understand the link between underlying E/I mechanisms and measurement techniques

Interaction dynamics and autonomy in cognitive systems

The concept of autonomy is of crucial importance for understanding life and cognition. Whereas cellular and organismic autonomy is based in the self-production of the material infrastructure sustaining the existence of living beings as such, we are interested in how biological autonomy can be expanded into forms of autonomous agency, where autonomy as a form of organization is extended into the behaviour of an agent in interaction with its environment (and not its material self-production). In this thesis, we focus on the development of operational models of sensorimotor agency, exploring the construction of a domain of interactions creating a dynamical interface between agent and environment. We present two main contributions to the study of autonomous agency: First, we contribute to the development of a modelling route for testing, comparing and validating hypotheses about neurocognitive autonomy. Through the design and analysis of specific neurodynamical models embedded in robotic agents, we explore how an agent is constituted in a sensorimotor space as an autonomous entity able to adaptively sustain its own organization. Using two simulation models and different dynamical analysis and measurement of complex patterns in their behaviour, we are able to tackle some theoretical obstacles preventing the understanding of sensorimotor autonomy, and to generate new predictions about the nature of autonomous agency in the neurocognitive domain. Second, we explore the extension of sensorimotor forms of autonomy into the social realm. We analyse two cases from an experimental perspective: the constitution of a collective subject in a sensorimotor social interactive task, and the emergence of an autonomous social identity in a large-scale technologically-mediated social system. Through the analysis of coordination mechanisms and emergent complex patterns, we are able to gather experimental evidence indicating that in some cases social autonomy might emerge based on mechanisms of coordinated sensorimotor activity and interaction, constituting forms of collective autonomous agency

Visual Cortical Traveling Waves: From Spontaneous Spiking Populations to Stimulus-Evoked Models of Short-Term Prediction

Thanks to recent advances in neurotechnology, waves of activity sweeping across entire cortical regions are now routinely observed. Moreover, these waves have been found to impact neural responses as well as perception, and the responses themselves are found to be structured as traveling waves. How exactly do these waves arise? Do they confer any computational advantages? These traveling waves represent an opportunity for an expanded theory of neural computation, in which their dynamic local network activity may complement the moment-to-moment variability of our sensory experience. This thesis aims to help uncover the origin and role of traveling waves in the visual cortex through three Works. In Work 1, by simulating a network of conductance-based spiking neurons with realistically large network size and synaptic density, distance-dependent horizontal axonal time delays were found to be important for the widespread emergence of spontaneous traveling waves consistent with those in vivo. Furthermore, these waves were found to be a dynamic mechanism of gain modulation that may explain the in-vivo result of their modulation of perception. In Work 2, the Kuramoto oscillator model was formulated in the complex domain to study a network possessing distance-dependent time delays. Like in Work 1, these delays produced traveling waves, and the eigenspectrum of the complex-valued delayed matrix, containing a delay operator, provided an analytical explanation of them. In Work 3, the model from Work 2 was adapted into a recurrent neural network for the task of forecasting the frames of videos, with the question of how such a biologically constrained model may be useful in visual computation. We found that the wave activity emergent in this network was helpful, as they were tightly linked with high forecast performance, and shuffle controls revealed simultaneous abolishment of both the waves and performance. All together, these works shed light on the possible origins and uses of traveling waves in the visual cortex. In particular, time delays profoundly shape the spatiotemporal dynamics into traveling waves. This was confirmed numerically (Work 1) and analytically (Work 2). In Work 3, these waves were found to aid in the dynamic computation of visual forecasting

Continuous attractor working memory and provenance of channel models

The brain is a complex biological system composed of a multitude of microscopic processes, which together give rise to computational abilities observed in everyday behavior. Neuronal modeling, consisting of models of single neurons and neuronal networks at varying levels of biological detail, can synthesize the gaps currently hard to constrain in experiments and provide mechanistic explanations of how these computations might arise. In this thesis, I present two parallel lines of research on neuronal modeling, situated at varying levels of biological detail. First, I assess the provenance of voltage-gated ion channel models in an integrative meta-analysis that investigates a backlog of nearly 50 years of published research. To cope with the ever-increasing volume of research produced in the field of neuroscience, we need to develop methods for the systematic assessment and comparison of published work. As we demonstrate, neuronal models offer the intriguing possibility of performing automated quantitative analyses across studies, by standardized simulated experiments. We developed protocols for the quantitative comparison of voltage-gated ion channels, and applied them to a large body of published models, allowing us to assess the variety and temporal development of different models for the same ion channels over the time scale of years of research. Beyond a systematic classification of the existing body of research made available in an online platform, we show that our approach extends to large-scale comparisons of ion channel models to experimental data, thereby facilitating field-wide standardization of experimentally-constrained modeling. Second, I investigate neuronal models of working memory (WM). How can cortical networks bridge the short time scales of their microscopic components, which operate on the order of milliseconds, to the behaviorally relevant time scales of seconds observed in WM experiments? I consider here a candidate model: continuous attractor networks. These can implement WM for a continuum of possible spatial locations over several seconds and have been proposed for the organization of prefrontal cortical networks. I first present a novel method for the efficient prediction of the network-wide steady states from the underlying microscopic network properties. The method can be applied to predict and tune the "bump" shapes of continuous attractors implemented in networks of spiking neuron models connected by nonlinear synapses, which we demonstrate for saturating synapses involving NMDA receptors. In a second part, I investigate the computational role of short-term synaptic plasticity as a synaptic nonlinearity. Continuous attractor models are sensitive to the inevitable variability of biological neurons: variable neuronal firing and heterogeneous networks decrease the time that memories are accurately retained, eventually leading to a loss of memory functionality on behaviorally relevant time scales. In theory and simulations, I show that short-term plasticity can control the time scale of memory retention, with facilitation and depression playing antagonistic roles in controlling the drift and diffusion of locations in memory. Finally, we place quantitative constraints on the combination of synaptic and network parameters under which continuous attractors networks can implement reliable WM in cortical settings