Emergent bursting and synchrony in computer simulations of neuronal cultures

Experimental studies of neuronal cultures have revealed a wide variety of spiking network activity ranging from sparse, asynchronous firing to distinct, network-wide synchronous bursting. However, the functional mechanisms driving these observed firing patterns are not well understood. In this work, we develop an in silico network of cortical neurons based on known features of similar in vitro networks. The activity from these simulations is found to closely mimic experimental data. Furthermore, the strength or degree of network bursting is found to depend on a few parameters: the density of the culture, the type of synaptic connections, and the ratio of excitatory to inhibitory connections. Network bursting gradually becomes more prominent as either the density, the fraction of long range connections, or the fraction of excitatory neurons is increased. Interestingly, biologically prevalent values of parameters result in networks that are at the transition between strong bursting and sparse firing. Using principal components analysis, we show that a large fraction of the variance in firing rates is captured by the first component for bursting networks. These results have implications for understanding how information is encoded at the population level as well as for why certain network parameters are ubiquitous in cortical tissue

Emergence of Spatio-Temporal Pattern Formation and Information Processing in the Brain.

The spatio-temporal patterns of neuronal activity are thought to underlie cognitive functions, such as our thoughts, perceptions, and emotions. Neurons and glial cells, specifically astrocytes, are interconnected in complex networks, where large-scale dynamical patterns emerge from local chemical and electrical signaling between individual network components. How these emergent patterns form and encode for information is the focus of this dissertation. I investigate how various mechanisms that can coordinate collections of neurons in their patterns of activity can potentially cause the interactions across spatial and temporal scales, which are necessary for emergent macroscopic phenomena to arise. My work explores the coordination of network dynamics through pattern formation and synchrony in both experiments and simulations. I concentrate on two potential mechanisms: astrocyte signaling and neuronal resonance properties. Due to their ability to modulate neurons, we investigate the role of astrocytic networks as a potential source for coordinating neuronal assemblies. In cultured networks, I image patterns of calcium signaling between astrocytes, and reproduce observed properties of the network calcium patterning and perturbations with a simple model that incorporates the mechanisms of astrocyte communication. Understanding the modes of communication in astrocyte networks and how they form spatial temporal patterns of their calcium dynamics is important to understanding their interaction with neuronal networks. We investigate this interaction between networks and how glial cells modulate neuronal dynamics through microelectrode array measurements of neuronal network dynamics. We quantify the spontaneous electrical activity patterns of neurons and show the effect of glia on the neuronal dynamics and synchrony. Through a computational approach I investigate an entirely different theoretical mechanism for coordinating ensembles of neurons. I show in a computational model how biophysical resonance shifts in individual neurons can interact with the network topology to influence pattern formation and separation. I show that sub-threshold neuronal depolarization, potentially from astrocytic modulation among other sources, can shift neurons into and out of resonance with specific bands of existing extracellular oscillations. This can act as a dynamic readout mechanism during information storage and retrieval. Exploring these mechanisms that facilitate emergence are necessary for understanding information processing in the brain.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111493/1/lshtrah_1.pd

Long-Term Bidirectional Neuron Interfaces for Robotic Control, and In Vitro Learning Studies

There are two fundamentally different goals for neural interfacing. On the biology side, to interface living neurons to external electronics allows the observation and manipulation of neural circuits to elucidate their fundamental mechanisms. On the engineering side, neural interfaces in animals, people, or in cell culture have the potential to restore missing functionality, or someday, to enhance existing functionality. At the Laboratory for NeuroEngineering at Georgia Tech, we are developing new technologies to help make both goals attainable. We culture dissociated mammalian neurons on multi-electrode arrays, and use them as the brain of a 'Hybrot', or hybrid neural-robotic system. Distributed neural activity patterns are used to control mobile robots. We have created the hardware and software necessary to feed the robots' sensory inputs back to the cultures in real time, as electrical stimuli. By embodying cultured networks, we study learning and memory at the cellular and network level, using 2-photon laser-scanning microscopy to image plasticity while it happens. We have observed a very rich dynamical landscape of activity patterns in networks of only a few thousand cells. We can alter this landscape via electrical stimuli, and use the hybrot system to study the emergent properties of networks in vitro

A combined experimental and computational approach to investigate emergent network dynamics based on large-scale neuronal recordings

Sviluppo di un approccio integrato computazionale-sperimentale per lo studio di reti neuronali mediante registrazioni elettrofisiologich

Neural Avalanches at the Critical Point between Replay and Non-Replay of Spatiotemporal Patterns

We model spontaneous cortical activity with a network of coupled spiking units, in which multiple spatio-temporal patterns are stored as dynamical attractors. We introduce an order parameter, which measures the overlap (similarity) between the activity of the network and the stored patterns. We find that, depending on the excitability of the network, different working regimes are possible. For high excitability, the dynamical attractors are stable, and a collective activity that replays one of the stored patterns emerges spontaneously, while for low excitability, no replay is induced. Between these two regimes, there is a critical region in which the dynamical attractors are unstable, and intermittent short replays are induced by noise. At the critical spiking threshold, the order parameter goes from zero to one, and its fluctuations are maximized, as expected for a phase transition (and as observed in recent experimental results in the brain). Notably, in this critical region, the avalanche size and duration distributions follow power laws. Critical exponents are consistent with a scaling relationship observed recently in neural avalanches measurements. In conclusion, our simple model suggests that avalanche power laws in cortical spontaneous activity may be the effect of a network at the critical point between the replay and non-replay of spatio-temporal patterns

Physiological role of PRRT2 and its involvement in the pathogenesis of paroxysmal disorders

Mutations in the PRoline-Rich Transmembrane protein 2 gene (PRRT2) underlie a heterogeneous group of paroxysmal disorders encompassing infantile epilepsy, paroxysmal kinesigenic dyskinesia, a combination of these phenotypes and migraine. For the majority of the pathogenic PRRT2 variants, the mutant proteins are not expressed or not correctly targeted to the plasma membrane, resulting in a loss-of function mechanism for PRRT2-related diseases. PRRT2 is a neuron-specific, type II transmembrane protein of 340 amino acids with an important functional role in synapse formation and maintenance, as well as in the regulation of fast neurotransmitter release at both glutamatergic and GABAergic terminals. The PRRT2 knock-out (PRRT2-KO) mouse, in which PRRT2 has been constitutively inactivated, displays alterations in brain structure and a sharp paroxysmal phenotype, reminiscent of the most common clinical manifestations of the human PRRT2-linked diseases. To gain further insights on the pathogenic role of PRRT2 deficiency, I used Multi-Electrode Arrays (MEAs) to characterize neuronal activity generated by primary hippocampal cultures obtained from the PRRT2-KO mouse embryos and to assess the epileptic propensity of cortico-hippocampal slices obtained from the same animal model. This experimental approach revealed a state of heightened spontaneous activity, hyper-synchronization in population bursts of action potentials (APs) and enhanced responsiveness to external stimuli in mutant networks. A complex interplay between (i) a synaptic phenotype, with weakened spontaneous transmission and increased short-term facilitation, and (ii) a marked increase in intrinsic excitability of excitatory neurons as assessed by single-cell electrophysiology, upholds this network phenotype. Furthermore, our group has generated cortical neurons from induced pluripotent stem cells (iPSCs) derived from heterozygous and homozygous siblings carrying the most common C.649dupC mutation. Patch-clamp recordings in neurons from homozygous patients showed an increased Na+ current that was fully rescued by expression of exogenous wild-type PRRT2. A strikingly similar electrophysiological phenotype was observed in excitatory primary cortical neurons from the PRRT2-KO mouse, which was accompanied by an increased length of the axon initial segment (AIS). At the network level, mutant cortical neurons grown on MEAs also displayed a state of spontaneous and evoked hyper-excitability and elevated propensity to synchronize their activity in network bursting events

The effect of network transitions on spontaneous activity and sycnhrony in devloping neural networks

Connectivity patterns of developing neural circuits and the effects of its dynamics on network behavior, particularly the emergence of spontaneous activity and synchrony, are not clear. We attempt to quantify anatomical connectivity patterns of rat cortical cultures during different stages of development. By culturing the networks on dishes embedded with micro electrode arrays, we simultaneously record electrical activity from multiple regions of the developing network and monitor its electrical behavior, particularly its tendency to fire spontaneously and to synchronize under certain conditions. We investigate possible correlations between changes in the network connectivity patterns and spontaneous electrical activity and synchrony. Cocultures showed a higher degree of synchrony than primary cultures. Networks with cancer cells, besides failing to synchronize, produced seizure-like events. We expect these results to elucidate the effect of connectivity on network behavior and hence to provide insight into the effects of various disease states on network properties. Such information could be used to diagnose such states

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

Elucidating the Interplay of Structure, Dynamics, and Function in the Brain’s Neural Networks.

Brain’s structure, dynamics, and function are deeply intertwined. To understand how the brain functions, it is crucial to uncover the links between network structure and its dynamics. Here I examine different approaches to exploring the key connecting factors between network structure, dynamics and eventually its function. I predominantly concentrate on emergence and temporal evolution of synchronization, or coincidence of neuronal spike timings, as it has been associated with many brain functions while aberrant synchrony is implicated in many neurological disorders. Specifically, in chapter II, I investigate how the interplay of cellular properties with network coupling characteristics could affect the propensity of neural networks for synchronization. Then, in chapter III, I develop a set of measures that identify hallmarks and potentially predict autonomous network transitions from asynchronous to synchronous dynamics under various conditions. The developed metrics can be calculated in real time and therefore potentially applied in clinical situations. Finally, in chapter IV, I aim to tie the correlates of neural network dynamics to the brain function. More specifically, I elucidate dynamical underpinnings of learning and memory consolidation from in vivo recordings of mice experiencing contextual fear conditioning (CFC) and show, that the introduced notion of network stability may predict future animal performance on memory retrieval. Overall, the results presented within this dissertation underscore the importance of concurrent analysis of networks’ dynamical and structural properties. The developed approaches may prove useful beyond the specific application presented within this thesis.PhDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120768/1/mofakham_1.pd