Assessing Neuronal Interactions of Cell Assemblies during General Anesthesia

Understanding the way in which groups of cortical neurons change their individual and mutual firing activity during the induction of general anesthesia may improve the safe usage of many anesthetic agents. Assessing neuronal interactions within cell assemblies during anesthesia may be useful for understanding the neural mechanisms of general anesthesia. Here, a point process generalized linear model (PPGLM) was applied to infer the functional connectivity of neuronal ensembles during both baseline and anesthesia, in which neuronal firing rates and network connectivity might change dramatically. A hierarchical Bayesian modeling approach combined with a variational Bayes (VB) algorithm is used for statistical inference. The effectiveness of our approach is evaluated with synthetic spike train data drawn from small and medium-size networks (consisting of up to 200 neurons), which are simulated using biophysical voltage-gated conductance models. We further apply the analysis to experimental spike train data recorded from rats' barrel cortex during both active behavior and isoflurane anesthesia conditions. Our results suggest that that neuronal interactions of both putative excitatory and inhibitory connections are reduced after the induction of isoflurane anesthesia.National Institutes of Health (U.S.) (NIH Grants DP1-OD003646

Spike Avalanches Exhibit Universal Dynamics across the Sleep-Wake Cycle

Scale-invariant neuronal avalanches have been observed in cell cultures and slices as well as anesthetized and awake brains, suggesting that the brain operates near criticality, i.e. within a narrow margin between avalanche propagation and extinction. In theory, criticality provides many desirable features for the behaving brain, optimizing computational capabilities, information transmission, sensitivity to sensory stimuli and size of memory repertoires. However, a thorough characterization of neuronal avalanches in freely-behaving (FB) animals is still missing, thus raising doubts about their relevance for brain function. To address this issue, we employed chronically implanted multielectrode arrays (MEA) to record avalanches of spikes from the cerebral cortex (V1 and S1) and hippocampus (HP) of 14 rats, as they spontaneously traversed the wake-sleep cycle, explored novel objects or were subjected to anesthesia (AN). We then modeled spike avalanches to evaluate the impact of sparse MEA sampling on their statistics. We found that the size distribution of spike avalanches are well fit by lognormal distributions in FB animals, and by truncated power laws in the AN group. The FB data are also characterized by multiple key features compatible with criticality in the temporal domain, such as 1/f spectra and long-term correlations as measured by detrended fluctuation analysis. These signatures are very stable across waking, slow-wave sleep and rapid-eye-movement sleep, but collapse during anesthesia. Likewise, waiting time distributions obey a single scaling function during all natural behavioral states, but not during anesthesia. Results are equivalent for neuronal ensembles recorded from V1, S1 and HP. Altogether, the data provide a comprehensive link between behavior and brain criticality, revealing a unique scale-invariant regime of spike avalanches across all major behaviors.Comment: 14 pages, 9 figures, supporting material included (published in Plos One

Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States

UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process.National Institutes of Health (U.S.) (Grant R01-DA015644)National Institutes of Health (U.S.) (Director Pioneer Award DP1- OD003646)National Institutes of Health (U.S.) (NIH/NHLBI grant R01-HL084502)National Institutes of Health (U.S.) (NIH institutional NRSA grant T32 HL07901

Generation and modulation of network oscillations on the rodent prefrontal cortex in vitro

PhD ThesisFast network oscillations (~12-80 Hz) are recorded extensively in the mammalian cerebral cortex in vivo which local and distant neuronal populations orchestrate their firing activity to process cognitive-related information. The rat medial prefrontal cortex (mPFC) is considered to be functionally and anatomically homologous to the primate in vitro studies have demonstrated that the mPFC can sustain carbachol-induced persistent beta1 or kainate-induced transient low gamma frequency oscillations. We wished to establish an in vitro paradigm of carbachol (10 μM) / kainate (200 objective to investigate the distribution patterns and the mechanisms of these oscillations. Then we assessed the modulatory effects of the ascending catecholamine systems on fast network oscillations with exogenous application of Persistent fast network oscillations in the ventral mPFC were stronger, more rhythmic but slower (~25 Hz) than oscillations in the dorsal mPFC (~28 Hz). The regional difference in the oscillation amplitude was correlated to the strong regions in the mPFC, oscillations were stronger in layer 5. Oscillations relied on GABA, kainate but not AMPA receptors. In the ventral mPFC, network oscillations A were also dependent on NMDA receptor-mediated synaptic transmission. μM) reduced the oscillation strength and rhythmicity in the ventral mPFC. Instead, dopamine increased the power and rhythmicity of network oscillations in the dorsal mPFC. The region-dependent dopamine effect was correlated to the induced effects on synaptic inhibition and neuronal firing. μM) reduced the osc caused no effect on the dorsal mPFC

Adaptation to changes in higher-order stimulus statistics in the salamander retina

Adaptation in the retina is thought to optimize the encoding of natural light signals into sequences of spikes sent to the brain. While adaptive changes in retinal processing to the variations of the mean luminance level and second-order stimulus statistics have been documented before, no such measurements have been performed when higher-order moments of the light distribution change. We therefore measured the ganglion cell responses in the tiger salamander retina to controlled changes in the second (contrast), third (skew) and fourth (kurtosis) moments of the light intensity distribution of spatially uniform temporally independent stimuli. The skew and kurtosis of the stimuli were chosen to cover the range observed in natural scenes. We quantified adaptation in ganglion cells by studying linear-nonlinear models that capture well the retinal encoding properties across all stimuli. We found that the encoding properties of retinal ganglion cells change only marginally when higher-order statistics change, compared to the changes observed in response to the variation in contrast. By analyzing optimal coding in LN-type models, we showed that neurons can maintain a high information rate without large dynamic adaptation to changes in skew or kurtosis. This is because, for uncorrelated stimuli, spatio-temporal summation within the receptive field averages away non-gaussian aspects of the light intensity distribution

Biophysical Mechanism for Neural Spiking Dynamics

abstract: In the honey bee antennal lobe, uniglomerular projection neurons (uPNs) transiently spike to odor sensory stimuli with odor-specific response latencies, i.e., delays to first spike after odor stimulation onset. Recent calcium imaging studies show that the spatio-temporal response profile of the activated uPNs are dynamic and changes as a result of associative conditioning, facilitating odor-detection of learned odors. Moreover, odor-representation in the antennal lobe undergo reward-mediated plasticity processes that increase response delay variations in the activated ensemble of uniglomerular projection neurons. Octopamine is necessarily involved in these plasticity processes. Yet, the cellular mechanisms are not well understood. I hypothesize that octopamine modulates cholinergic transmission to uPNs by triggering translation and upregulation of nicotinic receptors, which are more permeable to calcium. Consequently, this increased calcium-influx signals transcription factors that upregulate potassium channels in the dendritic cortex of glomeruli, similar to synaptic plasticity mechanisms recently shown in various insect species. A biophysical model of the antennal lobe circuit is developed in order to test the hypothesis that increased potassium channel expression in uPNs mediate response delays to first spike, dynamically tuning odor-representations to facilitate odor-detection of learned odors.Dissertation/ThesisDoctoral Dissertation Applied Mathematics for the Life and Social Sciences 201