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The interplay between excitation and inhibition is crucial for neuronal circuitry in the brain. Inhibitory cell fractions in the neocortex and hippocampus are typically maintained at 15 to 30%, which is assumed to be important for stable dynamics. We have studied systematically the role of precisely controlled excitatory/inhibitory (E/I) cellular ratios on network activity using mice hippocampal cultures. Surprisingly, networks with varying E/I ratios maintain stable bursting dynamics. Interburst intervals remain constant for most ratios, except in the extremes of 0 to 10% and 90 to 100% inhibitory cells. Single-cell recordings and modeling suggest that networks adapt to chronic alterations of E/I compositions by balancing E/I connectivity. Gradual blockade of inhibition substantiates the agreement between the model and experiment and defines its limits. Combining measurements of population and single-cell activity with theoretical modeling, we provide a clearer picture of how E/I balance is preserved and where it fails in living neuronal networks.

Automatic speech recognition (ASR) and natural language processing (NLP) are expected to benefit from an effective, simple, and reliable method to automatically parse conversational speech. The ability to parse conversational speech depends crucially on the ability to identify boundaries between prosodic phrases. This is done naturally by the human ear, yet has proved surprisingly difficult to achieve reliably and simply in an automatic manner. Efforts to date have focused on detecting phrase boundaries using a variety of linguistic and acoustic cues. We propose a method which does not require model training and utilizes two prosodic cues that are based on ASR output. Boundaries are identified using discontinuities in speech rate (pre-boundary lengthening and phrase-initial acceleration) and silent pauses. The resulting phrases preserve syntactic validity, exhibit pitch reset, and compare well with manual tagging of prosodic boundaries. Collectively, our findings support the notion of prosodic phrases that represent coherent patterns across textual and acoustic parameters.

A dire lack of potent therapies for mental illness is fueling the search for novel non-invasive therapeutic methodologies. Neuromodulation is an effective option, but none of the existing non-invasive modalities can selectively target small areas deep in the brain. Ultrasound, on the other hand, can be targeted deep into the brain with high spatial resolution. Ultrasonic neuromodulation was first described in the early 20th century, but large-scale attention only came to it recently when it was shown to activate neurons at intensities and frequencies that could safely be applied non-invasively through the human skull. Since then multiple studies have demonstrated stimulation in animals and in humans, but the mechanism of action has remained elusive. This study set out to investigate that mechanisms at the single-cell level. Central limitations were identified in the existing mechanistic research. First being a lack of control over reflections in the experimental systems, limiting control over the actual pressure being applied. Second is the use of highly connected networks of neurons, making it difficult to untangle network effects from those occurring at the single-cell level. To address these issues, a novel experimental system was designed and built, whose architecture enabled application of ultrasound with minimal reflections to neuronal cultures. Synaptic transmission in these cultures was pharmacologically blocked, eliminating network effects, enabling examination of single-cell level processes. Optical methods were used to image the activity and integrity of hundreds of individual neurons simultaneously, during ultrasonic stimulation. Single short pulses of continuous-wave low-intensity ultrasound were applied to the neurons, and time-locked responses were examined. Pharmacological interventions were used to disrupt specific cellular processes, and investigate their role in the mechanism. Ultrasound successfully stimulated disconnected neurons, and the effect was shown to not depend on membrane poration, or the activity of several purinergic receptor and mechanosensitive ion channel types. Stimulation was blocked, on the other hand, by suppression of action potentials. Surprisingly, even extremely short (4µs) pulses were effective, although to a lesser degree than longer pulses. Lower-pressure pulses were also found to be less effective than higher-pressure ones. Attrition effects, at the single cell level, were observed, where increases in pressure were needed to maintain responsivity, suggesting long term effects. These results detract from mechanistic theories implicating cavitation, thermal changes, membrane poration, pre-synaptic release, or gradual effects. They implicate a post-synaptic mechanism upstream of the action potential, and narrow down the list of possible receptors and ion channels that may be involved. It is my hope that future studies can make use of these results to help find the operating mechanisms, and pave the way towards effective use of ultrasonic neuromodulation in research and in therapy.

Neuronal cultures in vitro are a versatile system for studying the fundamental properties of individual neurons and neuronal networks. Recently, this approach has gained attention as a precision medicine tool. Mature neuronal cultures in vitro exhibit synchronized collective dynamics called network bursting. If analyzed appropriately, this activity could offer insights into the network’s properties, such as its composition, topology, and developmental and pathological processes. A promising method for investigating the collective dynamics of neuronal networks is to map them onto simplified dynamical systems. This approach allows the study of dynamical regimes and the characteristics of the parameters that lead to data-consistent activity. We designed a simple biophysically inspired dynamical system and used Bayesian inference to fit it to a large number of recordings of in vitro population activity. Even with a small number of parameters, the model showed strong inter-parameter dependencies leading to invariant bursting dynamics for many parameter combinations. We further validated this observation in our analytical solution. We found that in vitro bursting can be well characterized by each of three dynamical regimes: oscillatory, bistable, and excitable. The probability of finding a data-consistent match in a particular regime changes with network composition and development. The more informative way to describe the in vitro network bursting is the effective excitability, which we analytically show to be related to the parameter-invariance of the model’s dynamics. We establish that the effective excitability can be estimated directly from the experimentally recorded data. Finally, we demonstrate that effective excitability reliably detects the differences between cultures of cortical, hippocampal, and human pluripotent stem cell-derived neurons, allowing us to map their developmental trajectories. Our results open a new avenue for the model-based description of in vitro network phenotypes emerging across different experimental conditions.

Consider the \"Number in Hand\" multiparty communication complexity model, where k players holding inputs x_1,...,x_k in {0,1}^n communicate to compute the value f(x_1,...,x_k) of a function f known to all of them. The main lower bound technique for the communication complexity of such problems is that of partition arguments: partition the k players into two disjoint sets of players and find a lower bound for the induced two-party communication complexity problem. In this paper, we study the power of partition arguments. Our two main results are very different in nature: (i) For randomized communication complexity, we show that partition arguments may yield bounds that are exponentially far from the true communication complexity. Specifically, we prove that there exists a 3-argument function f whose communication complexity is Omega(n), while partition arguments can only yield an Omega(log n) lower bound. The same holds for nondeterministic communication complexity. (ii) For deterministic communication complexity, we prove that finding significant gaps between the true communication complexity and the best lower bound that can be obtained via partition arguments, would imply progress on a generalized version of the \"log-rank conjecture\" in communication complexity. We conclude with two results on the multiparty \"fooling set technique\", another method for obtaining communication complexity lower bounds.