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1. Subthreshold moment analysis of neuronal populations driven by synchronous synaptic inputs

   https://doi.org/10.1371/journal.pcbi.1013645  

   Becker, Logan A; Baccelli, Francois; Taillefumier, Thibaud (October 2025, PLOS Computational Biology)

   Jolivet, Renaud Blaise (Ed.)

   Even when driven by the same stimulus, neuronal responses are well-known to exhibit a striking level of spiking variability. In-vivo electrophysiological recordings also reveal a surprisingly large degree of variability at the subthreshold level. In prior work, we considered biophysically relevant neuronal models to account for the observed magnitude of membrane voltage fluctuations. We found that accounting for these fluctuations requires weak but nonzero synchrony in the spiking activity, in amount that are consistent with experimentally measured spiking correlations. Here we investigate whether such synchrony can explain additional statistical features of the measured neural activity, including neuronal voltage covariability and voltage skewness. Addressing this question involves conducting a generalized moment analysis of conductance-based neurons in response to input drives modeled as correlated jump processes. Technically, we perform such an analysis using fixed-point techniques from queuing theory that are applicable in the stationary regime of activity. We found that weak but nonzero synchrony can consistently explain the experimentally reported voltage covariance and skewness. This confirms the role of synchrony as a primary driver of cortical variability and supports that physiological neural activity emerges as a population-level phenomenon, especially in the spontaneous regime. 

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2. Creation of Neuronal Ensembles and Cell-Specific Homeostatic Plasticity through Chronic Sparse Optogenetic Stimulation

   https://doi.org/10.1523/JNEUROSCI.1104-22.2022  

   Liu, Benjamin; Seay, Michael J.; Buonomano, Dean V. (November 2022, The Journal of Neuroscience)

   Cortical computations emerge from the dynamics of neurons embedded in complex cortical circuits. Within these circuits, neuronal ensembles, which represent subnetworks with shared functional connectivity, emerge in an experience-dependent manner. Here we induced ensembles inex vivocortical circuits from mice of either sex by differentially activating subpopulations through chronic optogenetic stimulation. We observed a decrease in voltage correlation, and importantly a synaptic decoupling between the stimulated and nonstimulated populations. We also observed a decrease in firing rate during Up-states in the stimulated population. These ensemble-specific changes were accompanied by decreases in intrinsic excitability in the stimulated population, and a decrease in connectivity between stimulated and nonstimulated pyramidal neurons. By incorporating the empirically observed changes in intrinsic excitability and connectivity into a spiking neural network model, we were able to demonstrate that changes in both intrinsic excitability and connectivity accounted for the decreased firing rate, but only changes in connectivity accounted for the observed decorrelation. Our findings help ascertain the mechanisms underlying the ability of chronic patterned stimulation to create ensembles within cortical circuits and, importantly, show that while Up-states are a global network-wide phenomenon, functionally distinct ensembles can preserve their identity during Up-states through differential firing rates and correlations. SIGNIFICANCE STATEMENTThe connectivity and activity patterns of local cortical circuits are shaped by experience. This experience-dependent reorganization of cortical circuits is driven by complex interactions between different local learning rules, external input, and reciprocal feedback between many distinct brain areas. Here we used anex vivoapproach to demonstrate how simple forms of chronic external stimulation can shape local cortical circuits in terms of their correlated activity and functional connectivity. The absence of feedback between different brain areas and full control of external input allowed for a tractable system to study the underlying mechanisms and development of a computational model. Results show that differential stimulation of subpopulations of neurons significantly reshapes cortical circuits and forms subnetworks referred to as neuronal ensembles. 

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3. The dependence of synchrony on timescale and geography in freshwater plankton

   https://doi.org/10.1002/lno.11054  

   Anderson, Thomas L.; Sheppard, Lawrence W.; Walter, Jonathan A.; Hendricks, Susan P.; Levine, Todd D.; White, David S.; Reuman, Daniel C. (November 2018, Limnology and Oceanography)

   Abstract Spatial synchrony is defined by related fluctuations through time in population abundances measured at different locations. The degree of relatedness typically declines with increasing distance between sampling locations. Standard approaches for assessing synchrony assume isotropy in space and uniformity across timescales of analysis, but it is now known that spatial variability and timescale structure in population dynamics are common features. We tested for spatial and timescale structure in the patterns of synchrony of freshwater plankton in Kentucky Lake, U.S.A. We also evaluated whether different mechanisms may drive synchrony and its spatial structure on different timescales. Using wavelet techniques and matrix regression, we analyzed phytoplankton biomass and abundances of seven zooplankton taxa at 16 locations sampled from 1990 to 2015. We found that zooplankton abundances and phytoplankton biomass exhibited synchrony at multiple timescales. Timescale structure in the potential mechanisms of synchrony was revealed primarily through networks of relationships among zooplankton taxa, which differed by timescale. We found substantial interspecific variability in geographic structures of synchrony and their causes: all mechanisms we considered strongly explained geographic structure in synchrony for at least one species, while Euclidean distance between sampling locations was generally less well supported than more mechanistic explanations. Geographic structure in synchrony and its underlying mechanisms also depended on timescale for a minority of the taxa tested. Overall, our results show substantial and complex but interpretable variation in structures of synchrony across three variables: space, timescale, and taxon. It seems likely these aspects of synchrony are important general features of freshwater systems. 

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4. State-dependent activity dynamics of hypothalamic stress effector neurons

   https://doi.org/10.7554/eLife.76832  

   Ichiyama, Aoi; Mestern, Samuel; Benigno, Gabriel B; Scott, Kaela E; Allman, Brian L; Muller, Lyle; Inoue, Wataru (June 2022, eLife)

   The stress response necessitates an immediate boost in vital physiological functions from their homeostatic operation to an elevated emergency response. However, the neural mechanisms underlying this state-dependent change remain largely unknown. Using a combination of in vivo and ex vivo electrophysiology with computational modeling, we report that corticotropin releasing hormone (CRH) neurons in the paraventricular nucleus of the hypothalamus (PVN), the effector neurons of hormonal stress response, rapidly transition between distinct activity states through recurrent inhibition. Specifically, in vivo optrode recording shows that under non-stress conditions, CRH PVN neurons often fire with rhythmic brief bursts (RB), which, somewhat counterintuitively, constrains firing rate due to long (~2 s) interburst intervals. Stressful stimuli rapidly switch RB to continuous single spiking (SS), permitting a large increase in firing rate. A spiking network model shows that recurrent inhibition can control this activity-state switch, and more broadly the gain of spiking responses to excitatory inputs. In biological CRH PVN neurons ex vivo, the injection of whole-cell currents derived from our computational model recreates the in vivo-like switch between RB and SS, providing direct evidence that physiologically relevant network inputs enable state-dependent computation in single neurons. Together, we present a novel mechanism for state-dependent activity dynamics in CRH PVN neurons. 

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5. Hybrid spin-CMOS stochastic spiking neuron for high-speed emulation of In vivo neuron dynamics

   https://doi.org/10.1049/iet-cdt.2017.0145  

   Pyle, Steven D.; Camsari, Kerem Y.; DeMara, Ronald F. (February 2018, IET Computers & Digital Techniques)

   The spintronic stochastic spiking neuron (S3N) developed herein realizes biologically mimetic stochastic spiking characteristics observed within in vivo cortical neurons, while operating several orders of magnitude more rapidly and exhibiting a favorable energy profile. This work leverages a novel probabilistic spintronic switching element device that provides thermally-driven and current-controlled tunable stochasticity in a compact, low-energy, and high-speed package. Simulation program with integrated circuit emphasis (SPICE) simulation results indicate that the equivalent of 1 second of in vivo neuronal spiking characteristics can be generated on the order of nanoseconds, enabling the feasibility of extremely rapid emulation of in vivo neuronal behaviors for future statistical models of cortical information processing. Their results also indicate that the S3N can generate spikes on the order of ten picoseconds while dissipating only 0.6–9.6 μW, depending on the spiking rate. Additionally, they demonstrate that an S3N can implement perceptron functionality, such as AND-gate- and OR-gate-based logic processing, and provide future extensions of the work to more advanced stochastic neuromorphic architectures. 

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