Multiple firing coherence resonances in excitatory and inhibitory coupled neurons

The impact of inhibitory and excitatory synapses in delay-coupled Hodgkin--Huxley neurons that are driven by noise is studied. If both synaptic types are used for coupling, appropriately tuned delays in the inhibition feedback induce multiple firing coherence resonances at sufficiently strong coupling strengths, thus giving rise to tongues of coherency in the corresponding delay-strength parameter plane. If only inhibitory synapses are used, however, appropriately tuned delays also give rise to multiresonant responses, yet the successive delays warranting an optimal coherence of excitations obey different relations with regards to the inherent time scales of neuronal dynamics. This leads to denser coherence resonance patterns in the delay-strength parameter plane. The robustness of these findings to the introduction of delay in the excitatory feedback, to noise, and to the number of coupled neurons is determined. Mechanisms underlying our observations are revealed, and it is suggested that the regularity of spiking across neuronal networks can be optimized in an unexpectedly rich variety of ways, depending on the type of coupling and the duration of delays.Comment: 7 two-column pages, 6 figures; accepted for publication in Communications in Nonlinear Science and Numerical Simulatio

Computational study of resting state network dynamics

Lo scopo di questa tesi è quello di mostrare, attraverso una simulazione con il software The Virtual Brain, le più importanti proprietà della dinamica cerebrale durante il resting state, ovvero quando non si è coinvolti in nessun compito preciso e non si è sottoposti a nessuno stimolo particolare. Si comincia con lo spiegare cos’è il resting state attraverso una breve revisione storica della sua scoperta, quindi si passano in rassegna alcuni metodi sperimentali utilizzati nell’analisi dell’attività cerebrale, per poi evidenziare la differenza tra connettività strutturale e funzionale. In seguito, si riassumono brevemente i concetti dei sistemi dinamici, teoria indispensabile per capire un sistema complesso come il cervello. Nel capitolo successivo, attraverso un approccio ‘bottom-up’, si illustrano sotto il profilo biologico le principali strutture del sistema nervoso, dal neurone alla corteccia cerebrale. Tutto ciò viene spiegato anche dal punto di vista dei sistemi dinamici, illustrando il pionieristico modello di Hodgkin-Huxley e poi il concetto di dinamica di popolazione. Dopo questa prima parte preliminare si entra nel dettaglio della simulazione. Prima di tutto si danno maggiori informazioni sul software The Virtual Brain, si definisce il modello di network del resting state utilizzato nella simulazione e si descrive il ‘connettoma’ adoperato. Successivamente vengono mostrati i risultati dell’analisi svolta sui dati ricavati, dai quali si mostra come la criticità e il rumore svolgano un ruolo chiave nell'emergenza di questa attività di fondo del cervello. Questi risultati vengono poi confrontati con le più importanti e recenti ricerche in questo ambito, le quali confermano i risultati del nostro lavoro. Infine, si riportano brevemente le conseguenze che porterebbe in campo medico e clinico una piena comprensione del fenomeno del resting state e la possibilità di virtualizzare l’attività cerebrale

Sense and Sensitivity: Spatial Structure of conspecific signals during social interaction

Organisms rely on sensory systems to gather information about their environment. Localizing the source of a signal is key in guiding the behavior of the animal successfully. Localization mechanisms must cope with the challenges of representing the spatial information of weak, noisy signals. In this dissertation, I investigate the spatial dynamics of natural stimuli and explore how the electrosensory system of weakly electric fish encodes these realistic spatial signals. To do so In Chapter 2, I develop a model that examines the strength of the signal as it reaches the sensory array and simulates the responses of the receptors. The results demonstrate that beyond distances of 20 cm, the signal strength is only a fraction of the self-generated signal, often measuring less than a few percent. Chapter 2 also focuses on modeling a heterogeneous population of receptors to gain insights into the encoding of the spatial signal perceived by the fish. The findings reveal a significant decrease in signal detection beyond 40 cm, with a corresponding decrease in localization accuracy at 30 cm. Additionally, I investigate the impact of receptor density differences between the front and back on both signal detection and resolution accuracy. In Chapter 3, I analyze distinct movement patterns observed during agonistic encounters and their correlation with the estimated range of receptor sensitivity. Furthermore, I uncover that these agonistic interactions follow a classical pattern of cumulative assessment of competitors\u27 abilities. The outcome of this research is a comprehensive understanding of the spatial dynamics of social interactions and how this information is captured by the sensory system. Moreover, the research contributed to the development of a range of tools and models that will play crucial roles in future investigations of sensory processing within this system

Concomitant progressive supranuclear palsy and chronic traumatic encephalopathy in a boxer.

We report the case of a 75-year-old ex-professional boxer who developed diplopia and eye movement abnormalities in his 60's followed by memory impairment, low mood and recurrent falls. Examination shortly before death revealed hypomimia, dysarthria, vertical supranuclear gaze palsy and impaired postural reflexes. Pathological examination demonstrated 4-repeat tau neuronal and glial lesions, including tufted astrocytes, consistent with a diagnosis of progressive supranuclear palsy. In addition, neurofibrillary tangles composed of mixed 3-repeat and 4-repeat tau and astrocytic tangles in a distribution highly suggestive of chronic traumatic encephalopathy were observed together with limbic TDP-43 pathology. Possible mechanisms for the co-occurrence of these two tau pathologies are discussed

Fetal functional imaging portrays heterogeneous development of emerging human brain networks

The functional connectivity architecture of the adult human brain enables complex cognitive processes, and exhibits a remarkably complex structure shared across individuals. We are only beginning to understand its heterogeneous structure, ranging from a strongly hierarchical organization in sensorimotor areas to widely distributed networks in areas such as the parieto-frontal cortex. Our study relied on the functional magnetic resonance imaging (fMRI) data of 32 fetuses with no detectable morphological abnormalities. After adapting functional magnetic resonance acquisition, motion correction, and nuisance signal reduction procedures of resting-state functional data analysis to fetuses, we extracted neural activity information for major cortical and subcortical structures. Resting fMRI networks were observed for increasing regional functional connectivity from 21st to 38th gestational weeks (GWs) with a network-based statistical inference approach. The overall connectivity network, short range, and interhemispheric connections showed sigmoid expansion curve peaking at the 26–29 GW. In contrast, long-range connections exhibited linear increase with no periods of peaking development. Region-specific increase of functional signal synchrony followed a sequence of occipital (peak: 24.8 GW), temporal (peak: 26 GW), frontal (peak: 26.4 GW), and parietal expansion (peak: 27.5 GW). We successfully adapted functional neuroimaging and image post-processing approaches to correlate macroscopical scale activations in the fetal brain with gestational age. This in vivo study reflects the fact that the mid-fetal period hosts events that cause the architecture of the brain circuitry to mature, which presumably manifests in increasing strength of intra- and interhemispheric functional macro connectivity.Seventh Framework Programme (European Commission) (KHRESMOI Project

Amazon Nights II: Electric Boogaloo-Neural Adaptations for Communication in Three Species of Weakly Electric FIsh

Sensory systems have to extract useful information from environments awash in noise and confounding input. Studying how salient signals are encoded and filtered from these natural backgrounds is a key problem in neuroscience. Communication is a particularly tractable tool for studying this problem, as it is a ubiquitous task that all organisms must accomplish, easily compared across species, and is of significant ethological relevance. In this chapter I describe the current knowledge of what is both known and still unknown about how sensory systems are adapted for the challenges of encoding conspecific signals, particularly in environments complicated by conspecific-generated noise. The second half of this chapter describes why weakly electric fish are particularly suited to investigating how communication can shape the nervous system to accomplish this task

Dynamical consequences of regional heterogeneity in the brain’s transcriptional landscape

Brain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles or MRI-derived estimates of myeloarchitecture. We further show that regional transcriptional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional-activity time scales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptomic data to constrain models of large-scale brain function