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The processes leading to the N2+ lasing are rather complex and even the population distribution after the pump laser excitation is unknown. In this paper, we study the population distribution at electronic and vibrational levels in N2+ driven by ultra-short laser pulse at the wavelengths of 800 nm and 400 nm by using the quantum-mechanical time-domain incoherent superposition model based on the time-dependent Schrödinger equation and the quasi-classical model assuming instantaneous ionization injection described by density matrix. It is shown that while both models provide qualitatively similar results, the quasi-classical instantaneous ionization injection model underestimates the population inversions corresponding to the optical transitions at 391 nm, 423 nm and 428 nm due to the assumption of quantum mixed states at the ionization time. A fast and accurate correction to this error is proposed. This work solidifies the theoretical models for population at vibrational states in N2+ and paves the way to uncover the mechanism of the N2+ lasing.

To understand the brain we must relate neurons’ functional responses to the circuit architecture that shapes them. Here, we present a large functional connectomics dataset with dense calcium imaging of a millimeter scale volume. We recorded activity from approximately 75,000 neurons in primary visual cortex (VISp) and three higher visual areas (VISrl, VISal and VISlm) in an awake mouse viewing natural movies and synthetic stimuli. The functional data were co-registered with a volumetric electron microscopy (EM) reconstruction containing more than 200,000 cells and 0.5 billion synapses. Subsequent proofreading of a subset of neurons in this volume yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Here, we release this dataset as an open-access resource to the scientific community including a set of tools that facilitate data retrieval and downstream analysis. In accompanying papers we describe our findings using the dataset to provide a comprehensive structural characterization of cortical cell types1–3and the most detailed synaptic level connectivity diagram of a cortical column to date2, uncovering unique cell-type specific inhibitory motifs that can be linked to gene expression data4. Functionally, we identify new computational principles of how information is integrated across visual space5, characterize novel types of neuronal invariances6and bring structure and function together to decipher a general principle that wires excitatory neurons within and across areas7, 8.

How much can connectomes with synaptic resolution help us understand brain function? An optimistic view is that a connectome is a major determinant of brain function and a key substrate for simulating a brain. Here we investigate the explanatory power of connectomics using a wiring diagram reconstructed from a larval zebrafish brainstem. We identify modules of strongly connected neurons that turn out to be specialized for different behavioral functions, the control of eye and body movements. We then build a neural network model using a synaptic weight matrix based on the reconstructed wiring diagram. This leads to predictions that statistically match the neural coding of eye position as observed by calcium imaging. Our work shows the promise of connectome-based brain modeling to yield experimentally testable predictions of neural activity and behavior, as well as mechanistic explanations of low-dimensional neural dynamics, a widely observed phenomenon in nervous systems.

SummaryWe present a semi-automated reconstruction of L2/3 mouse primary visual cortex from 3 million cubic microns of electron microscopic images, including pyramidal and inhibitory neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are being made publicly available, along with tools for programmatic and 3D interactive access. The density of synaptic inputs onto inhibitory neurons varies across cell classes and compartments. We uncover a compartment-specific correlation between mitochondrial coverage and synapse density. Frequencies of connectivity motifs in the graph of pyramidal cells are predicted quite accurately from node degrees using the configuration model of random graphs. Cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. These example findings illustrate the resource’s utility for relating structure and function of cortical circuits as well as for neuronal cell biology.

We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ~250×140×90 μm(3) of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.

In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent's high-level strategy in negotiation tasks. Inspired by the idea of incorporating a theory of mind (ToM) into machines, we introduce a probabilistic formulation to encapsulate the opponent's personality type during both learning and inference. We test our approach on the CraigslistBargain dataset and show that our method using ToM inference achieves a 20% higher dialog agreement rate compared to baselines on a mixed population of opponents. We also find that our model displays diverse negotiation behavior with different types of opponents., Comment: ACL 2021. 12 pages, 3 figures

Neural networks with Hebbian excitation and anti-Hebbian inhibition form an interesting class of biologically plausible unsupervised learning algorithms. It has recently been shown that such networks can be regarded as online gradient descent-ascent algorithms for solving min-max problems that are dual to unsupervised learning principles formulated with no explicit reference to neural networks. Here we generalize one such formulation, the correlation game, by replacing a hard constraint with a soft penalty function. Our "softened" correlation game contains the nonnegative similarity matching principle as a special case. For solving the primal problem, we derive a projected gradient ascent algorithm that achieves speed through sorting. For solving the dual problem, we derive a projected gradient descent-ascent algorithm, the stochastic online variant of which can be interpreted as a neural network algorithm. We prove strong duality when the inhibitory connection matrix is positive definite, a condition that also prohibits multistability of neural activity dynamics. We show empirically that the neural net algorithm can converge when inhibitory plasticity is faster than excitatory plasticity, and may fail to converge in the opposing case. This is intuitively interpreted using the structure of the min-max problem.

On-line dialogue policy learning is the key for building evolvable conversational agent in real world scenarios. Poor initial policy can easily lead to bad user experience and consequently fail to attract sufficient users for policy training. A novel framework, companion teaching, is proposed to include a human teacher in the dialogue policy training loop to address the cold start problem. Here, dialogue policy is trained using not only user’s reward, but also teacher’s example action as well as estimated immediate reward at turn level. Simulation experiments showed that, with small number of human teaching dialogues, the proposed approach can effectively improve user experience at the beginning and smoothly lead to good performance with more user interaction data.

Hand-crafted rules and reinforcement learning (RL) are two popular choices to obtain dialogue policy. The rule-based policy is often reliable within predefined scope but not self-adaptable, whereas RL is evolvable with data but often suffers from a bad initial performance. We employ a companion learning framework to integrate the two approaches for on-line dialogue policy learning, in which a pre-defined rule-based policy acts as a “teacher” and guides a data-driven RL system by giving example actions as well as additional rewards. A novel agent-aware dropout Deep Q-Network (AAD-DQN) is proposed to address the problem of when to consult the teacher and how to learn from the teacher’s experiences. AAD-DQN, as a data-driven student policy, provides (1) two separate experience memories for student and teacher, (2) an uncertainty estimated by dropout to control the timing of consultation and learning. Simulation experiments showed that the proposed approach can significantly improve both safetyand efficiency of on-line policy optimization compared to other companion learning approaches as well as supervised pre-training using static dialogue corpus.