title: Modeling brain activation patterns for the default and cognitive states

authors: Steyn-Ross, Moira L.

subjects: brain activation pattern

abstract: We argue that spatial patterns of cortical activation observed with EEG, MEG and fMRI might arise from spontaneous self-organisation of interacting populations of excitatory and inhibitory neurons. We examine the dynamical behavior of a mean-field cortical model that includes chemical and electrical (gap-junction) synapses, focusing on two limiting cases: the “slow-soma” limit with slow voltage feedback from soma to dendrite, and the “fast-soma” limit in which the feedback action of soma voltage onto dendrite reversal potentials is instantaneous. For slow soma-dendrite feedback, we find a low-frequency (  1 Hz) dynamic Hopf instability, and a stationary Turing instability that catalyzes formation of patterned distributions of cortical firing-rate activity with pattern wavelength   2 cm. Turing instability can only be triggered when gap-junction diffusion between inhibitory neurons is strong, but patterning is destroyed if the tonic level of subcortical excitation is raised sufficiently. Interaction between the Hopf and Turing instabilities may describe the non-cognitive background or “default” state of the brain, as observed by BOLD imaging. In the fast-soma limit, the model predicts a high-frequency Hopf (  35 Hz) instability, and a traveling-wave gamma-band instability that manifests as a 2-D standing-wave pattern oscillating in place at   30 Hz. Small levels of inhibitory diffusion enhance and broaden the definition of the gamma antinodal regions by suppressing higher-frequency spatial modes, but gamma emergence is not contingent on the presence of inhibitory gap junctions; higher levels of diffusion suppress gamma activity. Fast-soma instabilities are enhanced by increased subcortical stimulation. Prompt soma-dendrite feedback may be an essential component of the genesis and large-scale cortical synchrony of gamma activity observed at the point of cognition.

text: Modeling brain activation patterns for the default and cognitive states We argue that spatial patterns of cortical activation observed with EEG, MEG and fMRI might arise from spontaneous self-organisation of interacting populations of excitatory and inhibitory neurons. We examine the dynamical behavior of a mean-field cortical model that includes chemical and electrical (gap-junction) synapses, focusing on two limiting cases: the “slow-soma” limit with slow voltage feedback from soma to dendrite, and the “fast-soma” limit in which the feedback action of soma voltage onto dendrite reversal potentials is instantaneous. For slow soma-dendrite feedback, we find a low-frequency (  1 Hz) dynamic Hopf instability, and a stationary Turing instability that catalyzes formation of patterned distributions of cortical firing-rate activity with pattern wavelength   2 cm. Turing instability can only be triggered when gap-junction diffusion between inhibitory neurons is strong, but patterning is destroyed if the tonic level of subcortical excitation is raised sufficiently. Interaction between the Hopf and Turing instabilities may describe the non-cognitive background or “default” state of the brain, as observed by BOLD imaging. In the fast-soma limit, the model predicts a high-frequency Hopf (  35 Hz) instability, and a traveling-wave gamma-band instability that manifests as a 2-D standing-wave pattern oscillating in place at   30 Hz. Small levels of inhibitory diffusion enhance and broaden the definition of the gamma antinodal regions by suppressing higher-frequency spatial modes, but gamma emergence is not contingent on the presence of inhibitory gap junctions; higher levels of diffusion suppress gamma activity. Fast-soma instabilities are enhanced by increased subcortical stimulation. Prompt soma-dendrite feedback may be an essential component of the genesis and large-scale cortical synchrony of gamma activity observed at the point of cognition.

year: 2009

title: Remote coastal monitoring of beach usage on Tairua Beach

authors: May-Stanley, Bridgette Petra

subjects: Remote Sensing

abstract: Research that gathers data from public reporting is susceptible to population bias, which arises from a lack of knowledge on the probability of a person’s ability to witness an event. Data collection based on public reporting is often used in coastal research relating to litter, stranded marine animals or bird spotting. It is biased by the probability of a person being in the vicinity, noticing and informing the appropriate organizations about the event. There is potential to improve population biased data by correcting for the probability of a person being present in a particular coastal vicinity. This thesis aims to better understand spatial and temporal beach usage at Tairua Beach in New Zealand. Building on existing literature, this research incorporates modern techniques to detect people on beaches from images taken every hour, during daylight, over a six-year period (2008 to 2013) from Tairua Beach, New Zealand. These methods include histogram matching, signal detection, machine learning-based classification, image registration and rectification, and various data cleaning techniques.
Analysis of the data showed that patterns of beach usage varied on scales of hours, days, months, seasons, and years, and variations followed general expected trends, such as there were more beach users over weekends and summer, reported in the literature. However, annual variations in beach usage did not behave as expected, having a couple of extreme values, likely caused by a change in camera equipment that improved image quality. Annual variations in beach usage were distributed spatially in a way that reflected changes in beach morphology, particularly in 2008 to 2010. Interpretation of spatial patterns in beach usage was obscured by resolution bias that required correction. Images were taken from a fixed position east of Tairua Beach with a scope of approximately 1.2 km of beach area. Naturally, the east end, which was closest to the camera, had higher resolution thus more people were detected on that end of the beach. The relative probability that someone occupied a position on the beach was derived from spatial distribution of the number of people counted in each image.

text: Remote coastal monitoring of beach usage on Tairua Beach Research that gathers data from public reporting is susceptible to population bias, which arises from a lack of knowledge on the probability of a person’s ability to witness an event. Data collection based on public reporting is often used in coastal research relating to litter, stranded marine animals or bird spotting. It is biased by the probability of a person being in the vicinity, noticing and informing the appropriate organizations about the event. There is potential to improve population biased data by correcting for the probability of a person being present in a particular coastal vicinity. This thesis aims to better understand spatial and temporal beach usage at Tairua Beach in New Zealand. Building on existing literature, this research incorporates modern techniques to detect people on beaches from images taken every hour, during daylight, over a six-year period (2008 to 2013) from Tairua Beach, New Zealand. These methods include histogram matching, signal detection, machine learning-based classification, image registration and rectification, and various data cleaning techniques.
Analysis of the data showed that patterns of beach usage varied on scales of hours, days, months, seasons, and years, and variations followed general expected trends, such as there were more beach users over weekends and summer, reported in the literature. However, annual variations in beach usage did not behave as expected, having a couple of extreme values, likely caused by a change in camera equipment that improved image quality. Annual variations in beach usage were distributed spatially in a way that reflected changes in beach morphology, particularly in 2008 to 2010. Interpretation of spatial patterns in beach usage was obscured by resolution bias that required correction. Images were taken from a fixed position east of Tairua Beach with a scope of approximately 1.2 km of beach area. Naturally, the east end, which was closest to the camera, had higher resolution thus more people were detected on that end of the beach. The relative probability that someone occupied a position on the beach was derived from spatial distribution of the number of people counted in each image.

year: 2021

title: Te kākahu whakataratara o Ngāi Tūhoe: Ko te reo ōkawa, ko te reo o te marae

authors: McFarland, Agnes

subjects: Reo Māori

abstract: Ko te reo ōkawa ara ko te reo o te marae, he reo hōhonu. Ka mutu, he tapu katoa ōna āhuatanga katoa. E rua ngā kōrero whakatau o runga i te marae. Ko te karanga me te whaikōrero. Kai roto i wēneki reo e rua, ka ahu mai te kupu nei te poroporoaki. Ka āta tirohia te poroporoaki i roto i ngā whakaaro e pā ana ki ngā tikanga o te karanga. Whai muri ka titiro ki te wāhanga o te poroporoaki i roto i te whaikōrero. Mā wēneki tikanga e rua, ka whakaputaina ngā whakaaro me te hōhonutanga o te mamae o te ngākau pōuri ka ea hoki te mamae. Ka tiro ake tātau ki ngā take i whakaputaina, ngā tohutohu ā rātau mā me ngā rongo o te wairua o te kupu kōrero. Tae atu hoki ki te tohungatanga ki te whakatakoto i te rerenga kupu mai i ngā whatumanawa o ngā kuia me ngā kaumātua ō Ngāi Tūhoe. Nō reira, ka titiro ake tātau ki ngā momo āhuatanga katoa o te karanga. Whai muri ko ngā tikanga e pā ana ki te whaikōrero. Whai muri, ko wētahi atu tūmomo tauira mō te poroporoaki.

text: Te kākahu whakataratara o Ngāi Tūhoe: Ko te reo ōkawa, ko te reo o te marae Ko te reo ōkawa ara ko te reo o te marae, he reo hōhonu. Ka mutu, he tapu katoa ōna āhuatanga katoa. E rua ngā kōrero whakatau o runga i te marae. Ko te karanga me te whaikōrero. Kai roto i wēneki reo e rua, ka ahu mai te kupu nei te poroporoaki. Ka āta tirohia te poroporoaki i roto i ngā whakaaro e pā ana ki ngā tikanga o te karanga. Whai muri ka titiro ki te wāhanga o te poroporoaki i roto i te whaikōrero. Mā wēneki tikanga e rua, ka whakaputaina ngā whakaaro me te hōhonutanga o te mamae o te ngākau pōuri ka ea hoki te mamae. Ka tiro ake tātau ki ngā take i whakaputaina, ngā tohutohu ā rātau mā me ngā rongo o te wairua o te kupu kōrero. Tae atu hoki ki te tohungatanga ki te whakatakoto i te rerenga kupu mai i ngā whatumanawa o ngā kuia me ngā kaumātua ō Ngāi Tūhoe. Nō reira, ka titiro ake tātau ki ngā momo āhuatanga katoa o te karanga. Whai muri ko ngā tikanga e pā ana ki te whaikōrero. Whai muri, ko wētahi atu tūmomo tauira mō te poroporoaki.

year: 2012-05