PRINCIPLES OF INFORMATION PROCESSING IN NEURONAL AVALANCHES

How the brain processes information is poorly understood. It has been suggested that the imbalance of excitation and inhibition (E/I) can significantly affect information processing in the brain. Neuronal avalanches, a type of spontaneous activity recently discovered, have been ubiquitously observed in vitro and in vivo when the cortical network is in the E/I balanced state. In this dissertation, I experimentally demonstrate that several properties regarding information processing in the cortex, i.e. the entropy of spontaneous activity, the information transmission between stimulus and response, the diversity of synchronized states and the discrimination of external stimuli, are optimized when the cortical network is in the E/I balanced state, exhibiting neuronal avalanche dynamics. These experimental studies not only support the hypothesis that the cortex operates in the critical state, but also suggest that criticality is a potential principle of information processing in the cortex. Further, we study the interaction structure in population neuronal dynamics, and discovered a special structure of higher order interactions that are inherent in the neuronal dynamics

Partial Information Decomposition as a Unified Approach to the Specification of Neural Goal Functions

In many neural systems anatomical motifs are present repeatedly, but despite their structural similarity they can serve very different tasks. A prime example for such a motif is the canonical microcircuit of six-layered neo-cortex, which is repeated across cortical areas, and is involved in a number of different tasks (e.g.sensory, cognitive, or motor tasks). This observation has spawned interest in finding a common underlying principle, a 'goal function', of information processing implemented in this structure. By definition such a goal function, if universal, cannot be cast in processing-domain specific language (e.g. 'edge filtering', 'working memory'). Thus, to formulate such a principle, we have to use a domain-independent framework. Information theory offers such a framework. However, while the classical framework of information theory focuses on the relation between one input and one output (Shannon's mutual information), we argue that neural information processing crucially depends on the combination of \textit{multiple} inputs to create the output of a processor. To account for this, we use a very recent extension of Shannon Information theory, called partial information decomposition (PID). PID allows to quantify the information that several inputs provide individually (unique information), redundantly (shared information) or only jointly (synergistic information) about the output. First, we review the framework of PID. Then we apply it to reevaluate and analyze several earlier proposals of information theoretic neural goal functions (predictive coding, infomax, coherent infomax, efficient coding). We find that PID allows to compare these goal functions in a common framework, and also provides a versatile approach to design new goal functions from first principles. Building on this, we design and analyze a novel goal function, called 'coding with synergy'. [...]Comment: 21 pages, 4 figures, appendi

Neuronal avalanches, epileptic quakes and other transient forms of neurodynamics

Abstract Power-law behaviors in brain activity in healthy animals, in the form of neuronal avalanches, potentially benefit the computational activities of the brain, including information storage, transmission and processing. In contrast, power-law behaviors associated with seizures, in the form of epileptic quakes, potentially interfere with the brain's computational activities. This review draws attention to the potential roles played by homeostatic mechanisms and multistable time-delayed recurrent inhibitory loops in the generation of power-law phenomena. Moreover, it is suggested that distinctions between health and disease are scale-dependent. In other words, what is abnormal and defines disease it is not the propagation of neural activity but the propagation of activity in a neural population that is large enough to interfere with the normal activities of the brain. From this point of view, epilepsy is a disease that results from a failure of mechanisms, possibly located in part in the cortex itself or in the deep brain nuclei and brainstem, which truncate or otherwise confine the spatiotemporal scales of these power-law phenomena

２次元メッシュニューラルネットワークにおけるスパイク波伝播と発信源の識別 －シミュレーションによる解析検証－

兵庫県立大学大学院201

Critical bistability and large-scale synchrony in human brain dynamics

Neurophysiological dynamics of the brain, overt behaviours, and private experiences of the mind are co-emergent and co-evolving phenomena. An adult human brain contains ~100 billion neurons that are hierarchically organized into intricate networks of functional units comprised of interconnected neurons. It has been hypothesized that neurons within a functional unit communicate with each other or neurons from other units via synchronized activity. At any moment, cascades of synchronized activity from millions of neurons propagate through networks of all sizes, and the levels of synchronization wax and wane. How to understand cognitive functions or diseases from such rich dynamics poses a great challenge. The brain criticality hypothesis proposes that the brain, like many complex systems, optimize its performance by operating near a critical point of phase transition between disorder and order, which suggests complex brain dynamics be effectively studied by combining computational and empirical approaches. Hence, the brain criticality framework requires both classic reductionist and reconstructionist approaches. Reconstructionism in the current context refers to addressing the “Wholeness” of macro-level emergence due to fundamental mechanisms such as synchrony between neurons in the brain. This thesis includes five studies and aims to advance theory, empirical evidence, and methodology in the research of neuronal criticality and large-scale synchrony in the human brain. Study I: The classic criticality theory is based on the hypothesis that the brain operates near a continuous, second order phase transition between order and disorder in resource-conserving systems. This idea, however, cannot explain why the brain, a non-conserving system, often shows bistability, a hallmark of first order, discontinuous phase transition. We used computational modeling and found that bistability may occur exclusively within the critical regime so that the first-order phase transition emerged progressively with increasing local resource demands. We observed that in human resting-state brain activity, moderate α-band (11 Hz) bistability during rest predicts cognitive performance, but excessive resting-state bistability in fast (> 80 Hz) oscillations characterizes epileptogenic zones in patients’ brain. These findings expand the framework of brain criticality and show that near-critical neuronal dynamics involve both first- and second-order phase transitions in a frequency-, neuroanatomy-, and state-dependent manner. Study II: Long-range synchrony between cortical oscillations below ~100 Hz is pervasive in brain networks, whereas oscillations and broad-band activities above ~100 Hz have been considered to be strictly local phenomena. We showed with human intracerebral recordings that high-frequency oscillations (HFOs, 100−400 Hz) may be synchronized between brain regions separated by several centimeters. We discovered subject-specific frequency peaks of HFO synchrony and found the group-level HFO synchrony to exhibit laminar-specific connectivity and robust community structures. Importantly, the HFO synchrony was both transiently enhanced and suppressed in separate sub-bands during tasks. These findings showed that HFO synchrony constitutes a functionally significant form of neuronal spike-timing relationships in brain activity and thus a new mesoscopic indication of neuronal communication per se. Studies III: Signal linear mixing in magneto- (MEG) and electro-encephalography (EEG) artificially introduces linear correlations between sources and confounds the separability of cortical current estimates. This linear mixing effect in turn introduces false positives into synchrony estimates between MEG/EEG sources. Several connectivity metrics have been proposed to supress the linear mixing effects. We show that, although these metrics can remove false positives caused by instantaneous mixing effects, all of them discover false positive ghost interactions (SIs). We also presented major difficulties and technical concerns in mapping brain functional connectivity when using the most popular pairwise correlational metrics. Study IV and V: We developed a novel approach as a solution to the SIs problem. Our approach is to bundle observed raw edges, i.e., true interactions or SIs, into hyperedges by raw edges’ adjacency in signal mixing. We showed that this bundling approach yields hyperedges with optimal separability between true interactions while suffers little loss in the true positive rate. This bundling approach thus significantly decreases the noise in connectivity graphs by minimizing the false-positive to true-positive ratio. Furthermore, we demonstrated the advantage of hyperedge bundling in visualizing connectivity graphs derived from MEG experimental data. Hence, the hyperedges represent well the true cortical interactions that are detectable and dissociable in MEG/EEG sources. Taken together, these studies have advanced theory, empirical evidence, and methodology in the research of neuronal criticality and large-scale synchrony in the human brain. Study I provided modeling and empirical evidence for linking bistable criticality and the classic criticality hypothesis into a unified framework. Study II was the first to reveal HFO phase synchrony in large-scale neocortical networks, which was a fundamental discovery of long-range neuronal interactions on fast time-scale per se. Study III raised awareness of the ghost interaction (SI) problem for a critical view on reliable interpretation of MEG/EEG connectivity, and for the development of novel approaches to address the SI problem. Study IV offered a practical solution to the SI problem and opened a new avenue for mapping reliable MEG/EEG connectivity. Study V described the technical details of the hyperedge bundling approach, shared the source code and specified the simulation parameters used in Study IV.Ihmisaivojen neurofysiologinen dynamiikka, ihmisen käyttäytyminen, sekä yksityiset mielen kokemukset syntyvät ja kehittyvät rinnakkaisina ilmiöinä. Ihmisen aivot koostuvat ~100 miljardista hierarkisesti järjestäytyneestä hermosolusta, jotka toisiinsa kytkeytyneinä muodostavat monimutkaisen verkoston toiminnallisia yksiköitä. Hermosolujen aktiivisuuden synkronoitumisen on esitetty mahdollistavan neuronien välisen kommunikoinnin toiminnallisten yksiköiden sisällä sekä niiden välillä. Hetkenä minä hyvänsä, synkronoidun aktiivisuuden kaskadit etenevät aivojen erikokoisissa verkostoissa jatkuvasti heikentyen ja voimistuen. Kognitiivisten funktioiden ja erilaisten aivosairauksien ymmärtäminen tulkitsemalla aivojen rikasta dynamiikkaa on suuri haaste. Kriittiset aivot -hypoteesi ehdottaa aivojen, kuten monien muidenkin kompleksisten systeemien, optimoivan suorituskykyään operoimalla lähellä kriittistä pistettä järjestyksen ja epäjärjestyksen välissä, puoltaen sitä, että aivojen kompleksisia dynamiikoita voitaisiin tutkia yhdistämällä laskennallisia ja empiirisiä lähestymistapoja. Aivojen kriittisyyden viitekehys edellyttää perinteistä reduktionismia ja rekonstruktionismia. Erityisesti, rekonstruktionismi tähtää kuvaamaan aivojen makrotason “yhteneväisyyden” syntymistä perustavanlaatuisten mekaniikoiden, kuten aivojen toiminnallisten yksiköiden välisen synkronian avulla. Tämä väitöskirja sisältää viisi tutkimusta, jotka edistävät teoriaa, empiirisiä todisteita ja metodologiaa aivojen kriittisyyden ja laajamittaisen synkronian tutkimuksessa. Tutkimus I tarjosi mallinnuksia ja empiirisiä todisteita bistabiilin kriittisyyden ja klassisen kriittisyyden hypoteesien yhdistämiseksi yhdeksi viitekehykseksi. Tutkimus II oli ensimmäinen laatuaan paljastaen korkeataajuisten oskillaatioiden (high-frequency oscillation, HFO) vaihesynkronian laajamittaisissa neokortikaalisissa verkostoissa, mikä oli perustavanlaatuinen löytö pitkän matkan neuronaalisista vuorovaikutuksista nopeilla aikaskaaloilla. Tutkimus III lisäsi tietoisuutta aave-vuorovaikutuksien (spurious interactions, SI) ongelmasta MEG/EEG kytkeytyvyyden luotettavassa tulkinnassa sekä uudenlaisten menetelmien kehityksessä SI-ongelman ratkaisemiseksi. Tutkimus IV tarjosi käytännöllisen “hyperedge bundling” -ratkaisun SI-ongelmaan ja avasi uudenlaisen tien luotettavaan MEG/EEG kytkeytyvyyden kartoittamiseen. Tutkimus V kuvasi teknisiä yksityiskohtia hyperedge bundling -menetelmästä, jakoi menetelmän lähdekoodin ja täsmensi tutkimuksessa IV käytettyjä simulaatioparametreja. Yhdessä nämä tutkimukset ovat edistäneet teoriaa, empiirisiä todisteita ja metodologiaa neuronaalisen kriittisyyden sekä laajamittaisen synkronian hyödyntämisessä ihmisaivojen tutkimuksessa