The interplay of intrinsic excitability and network topology in spatiotemporal pattern generation in neural networks

http://deepblue.lib.umich.edu/bitstream/2027.42/109555/1/12868_2014_Article_3550.pd

Network topology and intrinsic excitability of the existing network drive integration patterns in a model of adult neurogenesis

http://deepblue.lib.umich.edu/bitstream/2027.42/112379/1/12868_2013_Article_3276.pd

Odorant specificity of three oscillations and the DC signal in the turtle olfactory bulb

Author Posting. © The Author(s), 2003. This is the author's version of the work. It is posted here by permission of Blackwell Publishing for personal use, not for redistribution. The definitive version was published in European Journal of Neuroscience 17 (2003): 436-446, doi:10.1046/j.1460-9568.2003.02457.x.The odour-induced population response in the in vivo turtle (Terepene sp.) olfactory bulb consists of three oscillatory components (rostral, middle and caudal) that ride on top of a DC signal. In an initial step to determine the functional role of these four signals, we compared the signals elicited by different odorants. Most experiments compared isoamyl acetate and cineole, odorants which have very different maps of input to olfactory bulb glomeruli in the turtle and a different perceptual quality for humans. We found substantial differences in the response to the two odours in the rise-time of the DC signal and in the latency of the middle oscillation. The rate of rise for cineole was twice as fast as that for isoamyl acetate. Similarly, the latency for the middle oscillation was about twice as long for isoamyl acetate as it was for cineole. On the other hand, a number of characteristics of the signals were not substantially different for the two odorants. These included the latency of the rostral and caudal oscillation, the frequency and envelope of all three oscillations and their locations and spatial extents. A smaller number of experiments were carried out with hexanone and hexanal; the oscillations elicited by these odorants did not appear to be different from those elicited by isoamyl acetate and cineole. Qualitative differences between the oscillations in the turtle and those in two invertebrate phyla suggest that different odour processing strategies may be used.Supported in part by NIH grant DC05259 and a Brown-Coxe fellowship from the Yale University School of Medicine

Modeling the formation and dynamics of cortical waves induced by cholinergic modulation

http://deepblue.lib.umich.edu/bitstream/2027.42/134563/1/12868_2015_Article_4163.pd

Structural network heterogeneities and network dynamics: a possible dynamical mechanism for hippocampal memory reactivation

The hippocampus has the capacity for reactivating recently acquired memories [1-3] and it is hypothesized that one of the functions of sleep reactivation is the facilitation of consolidation of novel memory traces [4-11]. The dynamic and network processes underlying such a reactivation remain, however, unknown. We show that such a reactivation characterized by local, self-sustained activity of a network region may be an inherent property of the recurrent excitatory-inhibitory network with a heterogeneous structure. The entry into the reactivation phase is mediated through a physiologically feasible regulation of global excitability and external input sources, while the reactivated component of the network is formed through induced network heterogeneities during learning. We show that structural changes needed for robust reactivation of a given network region are well within known physiological parameters [12,13].Comment: 16 pages, 5 figure

From network structure to network reorganization: implications for adult neurogenesis

Networks can be dynamical systems that undergo functional and structural reorganization. One example of such a process is adult hippocampal neurogenesis, in which new cells are continuously born and incorporate into the existing network of the dentate gyrus region of the hippocampus. Many of these introduced cells mature and become indistinguishable from established neurons, joining the existing network. Activity in the network environment is known to promote birth, survival and incorporation of new cells. However, after epileptogenic injury, changes to the connectivity structure around the neurogenic niche are known to correlate with aberrant neurogenesis. The possible role of network-level changes in the development of epilepsy is not well understood. In this paper, we use a computational model to investigate how the structural and functional outcomes of network reorganization, driven by addition of new cells during neurogenesis, depend on the original network structure. We find that there is a stable network topology that allows the network to incorporate new neurons in a manner that enhances activity of the persistently active region, but maintains global network properties. In networks having other connectivity structures, new cells can greatly alter the distribution of firing activity and destroy the initial activity patterns. We thus find that new cells are able to provide focused enhancement of network only for small-world networks with sufficient inhibition. Network-level deviations from this topology, such as those caused by epileptogenic injury, can set the network down a path that develops toward pathological dynamics and aberrant structural integration of new cells.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85406/1/ph10_4_046008.pd

Connexin 43 gap junctions contribute to brain endothelial barrier hyperpermeability in familial cerebral cavernous malformations type III by modulating tight junction structure

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154657/1/fsb2fj201700699r-sup-0003.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154657/2/fsb2fj201700699r.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154657/3/fsb2fj201700699r-sup-0002.pd

Cellularly-Driven Differences in Network Synchronization Propensity Are Differentially Modulated by Firing Frequency

Spatiotemporal pattern formation in neuronal networks depends on the interplay between cellular and network synchronization properties. The neuronal phase response curve (PRC) is an experimentally obtainable measure that characterizes the cellular response to small perturbations, and can serve as an indicator of cellular propensity for synchronization. Two broad classes of PRCs have been identified for neurons: Type I, in which small excitatory perturbations induce only advances in firing, and Type II, in which small excitatory perturbations can induce both advances and delays in firing. Interestingly, neuronal PRCs are usually attenuated with increased spiking frequency, and Type II PRCs typically exhibit a greater attenuation of the phase delay region than of the phase advance region. We found that this phenomenon arises from an interplay between the time constants of active ionic currents and the interspike interval. As a result, excitatory networks consisting of neurons with Type I PRCs responded very differently to frequency modulation compared to excitatory networks composed of neurons with Type II PRCs. Specifically, increased frequency induced a sharp decrease in synchrony of networks of Type II neurons, while frequency increases only minimally affected synchrony in networks of Type I neurons. These results are demonstrated in networks in which both types of neurons were modeled generically with the Morris-Lecar model, as well as in networks consisting of Hodgkin-Huxley-based model cortical pyramidal cells in which simulated effects of acetylcholine changed PRC type. These results are robust to different network structures, synaptic strengths and modes of driving neuronal activity, and they indicate that Type I and Type II excitatory networks may display two distinct modes of processing information

Statistical Analyses Support Power Law Distributions Found in Neuronal Avalanches

The size distribution of neuronal avalanches in cortical networks has been reported to follow a power law distribution with exponent close to −1.5, which is a reflection of long-range spatial correlations in spontaneous neuronal activity. However, identifying power law scaling in empirical data can be difficult and sometimes controversial. In the present study, we tested the power law hypothesis for neuronal avalanches by using more stringent statistical analyses. In particular, we performed the following steps: (i) analysis of finite-size scaling to identify scale-free dynamics in neuronal avalanches, (ii) model parameter estimation to determine the specific exponent of the power law, and (iii) comparison of the power law to alternative model distributions. Consistent with critical state dynamics, avalanche size distributions exhibited robust scaling behavior in which the maximum avalanche size was limited only by the spatial extent of sampling (“finite size” effect). This scale-free dynamics suggests the power law as a model for the distribution of avalanche sizes. Using both the Kolmogorov-Smirnov statistic and a maximum likelihood approach, we found the slope to be close to −1.5, which is in line with previous reports. Finally, the power law model for neuronal avalanches was compared to the exponential and to various heavy-tail distributions based on the Kolmogorov-Smirnov distance and by using a log-likelihood ratio test. Both the power law distribution without and with exponential cut-off provided significantly better fits to the cluster size distributions in neuronal avalanches than the exponential, the lognormal and the gamma distribution. In summary, our findings strongly support the power law scaling in neuronal avalanches, providing further evidence for critical state dynamics in superficial layers of cortex