Constructive Preference Elicitation over Hybrid Combinatorial Spaces

Preference elicitation is the task of suggesting a highly preferred configuration to a decision maker. The preferences are typically learned by querying the user for choice feedback over pairs or sets of objects. In its constructive variant, new objects are synthesized "from scratch" by maximizing an estimate of the user utility over a combinatorial (possibly infinite) space of candidates. In the constructive setting, most existing elicitation techniques fail because they rely on exhaustive enumeration of the candidates. A previous solution explicitly designed for constructive tasks comes with no formal performance guarantees, and can be very expensive in (or unapplicable to) problems with non-Boolean attributes. We propose the Choice Perceptron, a Perceptron-like algorithm for learning user preferences from set-wise choice feedback over constructive domains and hybrid Boolean-numeric feature spaces. We provide a theoretical analysis on the attained regret that holds for a large class of query selection strategies, and devise a heuristic strategy that aims at optimizing the regret in practice. Finally, we demonstrate its effectiveness by empirical evaluation against existing competitors on constructive scenarios of increasing complexity.Comment: AAAI 2018, computing methodologies, machine learning, learning paradigms, supervised learning, structured output

Branch-specific plasticity enables self-organization of nonlinear computation in single neurons

It has been conjectured that nonlinear processing in dendritic branches endows individual neurons with the capability to perform complex computational operations that are needed in order to solve for example the binding problem. However, it is not clear how single neurons could acquire such functionality in a self-organized manner, since most theoretical studies of synaptic plasticity and learning concentrate on neuron models without nonlinear dendritic properties. In the meantime, a complex picture of information processing with dendritic spikes and a variety of plasticity mechanisms in single neurons has emerged from experiments. In particular, new experimental data on dendritic branch strength potentiation in rat hippocampus have not yet been incorporated into such models. In this article, we investigate how experimentally observed plasticity mechanisms, such as depolarization-dependent STDP and branch-strength potentiation could be integrated to self-organize nonlinear neural computations with dendritic spikes. We provide a mathematical proof that in a simplified setup these plasticity mechanisms induce a competition between dendritic branches, a novel concept in the analysis of single neuron adaptivity. We show via computer simulations that such dendritic competition enables a single neuron to become member of several neuronal ensembles, and to acquire nonlinear computational capabilities, such as for example the capability to bind multiple input features. Hence our results suggest that nonlinear neural computation may self-organize in single neurons through the interaction of local synaptic and dendritic plasticity mechanisms

Diel transcriptional response of a California Current plankton microbiome to light, low iron, and enduring viral infection.

Phytoplankton and associated microbial communities provide organic carbon to oceanic food webs and drive ecosystem dynamics. However, capturing those dynamics is challenging. Here, an in situ, semi-Lagrangian, robotic sampler profiled pelagic microbes at 4 h intervals over ~2.6 days in North Pacific high-nutrient, low-chlorophyll waters. We report on the community structure and transcriptional dynamics of microbes in an operationally large size class (>5 μm) predominantly populated by dinoflagellates, ciliates, haptophytes, pelagophytes, diatoms, cyanobacteria (chiefly Synechococcus), prasinophytes (chiefly Ostreococcus), fungi, archaea, and proteobacteria. Apart from fungi and archaea, all groups exhibited 24-h periodicity in some transcripts, but larger portions of the transcriptome oscillated in phototrophs. Periodic photosynthesis-related transcripts exhibited a temporal cascade across the morning hours, conserved across diverse phototrophic lineages. Pronounced silica:nitrate drawdown, a high flavodoxin to ferredoxin transcript ratio, and elevated expression of other Fe-stress markers indicated Fe-limitation. Fe-stress markers peaked during a photoperiodically adaptive time window that could modulate phytoplankton response to seasonal Fe-limitation. Remarkably, we observed viruses that infect the majority of abundant taxa, often with total transcriptional activity synchronized with putative hosts. Taken together, these data reveal a microbial plankton community that is shaped by recycled production and tightly controlled by Fe-limitation and viral activity

Coactive learning for multi-robot search and coverage

We investigate a search and coverage planning problem, where an area of interest has to be explored by a number of vehicles, given a fixed time budget. A good coverage plan has a low probability of a target remaining unobserved. We introduce a formal problem statement, suggest a greedy algorithm to solve the problem, and show experimental results on a number of simulated coverage problems. Our work offers three main contributions. First, we propose an offline planning algorithm that, given some prior knowledge about the target probability in an environment, surveys the area to find the targets as fast as possible while minimizing the energy used. The planning algorithm plans targets to visit and paths to follow for multiple robots, which may have different performance characteristics such as speed, power, and sensor quality. Our second main contribution is to integrate our planning algorithm in the framework of coactive learning, where the system learns the cost function of an in situ human expert, who edits and improves the solutions generated by the system. Our third contribution is an empirical evaluation of the system and a comparison to a state-of-the-art system with provable performance gaurantees on a simulator. The results show that our system yields comparable performance to the state-of-the-art system while respecting hard budget constraints and running orders of magnitude faster.Keywords: search and coverage planning problem, Multi-robot Search and Coverage, Coactive LearningKeywords: search and coverage planning problem, Multi-robot Search and Coverage, Coactive Learnin

Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives

In this paper we review the works related to muscle synergies that have been carried-out in neuroscience and control engineering. In particular, we refer to the hypothesis that the central nervous system (CNS) generates desired muscle contractions by combining a small number of predefined modules, called muscle synergies. We provide an overview of the methods that have been employed to test the validity of this scheme, and we show how the concept of muscle synergy has been generalized for the control of artificial agents. The comparison between these two lines of research, in particular their different goals and approaches, is instrumental to explain the computational implications of the hypothesized modular organization. Moreover, it clarifies the importance of assessing the functional role of muscle synergies: although these basic modules are defined at the level of muscle activations (input-space), they should result in the effective accomplishment of the desired task. This requirement is not always explicitly considered in experimental neuroscience, as muscle synergies are often estimated solely by analyzing recorded muscle activities. We suggest that synergy extraction methods should explicitly take into account task execution variables, thus moving from a perspective purely based on input-space to one grounded on task-space as well

Structural Plasticity Controlled by Calcium Based Correlation Detection

Hebbian learning in cortical networks during development and adulthood relies on the presence of a mechanism to detect correlation between the presynaptic and the postsynaptic spiking activity. Recently, the calcium concentration in spines was experimentally shown to be a correlation sensitive signal with the necessary properties: it is confined to the spine volume, it depends on the relative timing of pre- and postsynaptic action potentials, and it is independent of the spine's location along the dendrite. NMDA receptors are a candidate mediator for the correlation dependent calcium signal. Here, we present a quantitative model of correlation detection in synapses based on the calcium influx through NMDA receptors under realistic conditions of irregular pre- and postsynaptic spiking activity with pairwise correlation. Our analytical framework captures the interaction of the learning rule and the correlation dynamics of the neurons. We find that a simple thresholding mechanism can act as a sensitive and reliable correlation detector at physiological firing rates. Furthermore, the mechanism is sensitive to correlation among afferent synapses by cooperation and competition. In our model this mechanism controls synapse formation and elimination. We explain how synapse elimination leads to firing rate homeostasis and show that the connectivity structure is shaped by the correlations between neighboring inputs

Modeling projection neuron and neuromodulatory effects on a rhythmic neuronal network

Projection neurons shape the activity of many neural networks. In particular, neuromodulatory substances, which are often released by projection neurons, alter the cellular and/or synaptic properties within a target network. However, neural networks in turn influence projection neuron input via synaptic feedback. This dissertation uses mathematical and biophysically-realistic modeling to investigate these issues in the gastric mill (chewing) motor network of the crab, Cancer borealis. The projection neuron MCN1 elicits a gastric mill rhythm in which the LG neuron and INTl burst in anti-phase due to their reciprocal inhibition. However, bath application of the neuromodulator PK elicits a similar gastric mill rhythm in the absence of MCN 1 participation; yet, the mechanism that underlies the PK-elicited rhythm is unknown. This dissertation develops a 2-dimensional model that is used to propose three potential mechanisms by which PK can elicit a similar gastric mill rhythm. The network dynamics of the MCN 1-elicited and PK-elicited rhythms are also compared using geometrical properties in the phase plane. Next, the two gastric mill rhythms are compared using a more biophysically-realistic model. Presynaptic inhibition of MCN 1 is necessary for coordinating network activity during the MCN 1-elicited rhythm. In contrast, the PK-elicited rhythm is shown to be coordinated by a synapse that is not functional during the MCN 1-elicited rhythm. Next, the gastric mill rhythm that is elicited by two coactive projection neurons (MCNl and CPN2) is studied. A 2-dimensional model is used to compare the network dynamics of the MCN 1-elicited and MCN 1 /CPN2-elicited gastric mill rhythms via geometrical properties in the phase plane. While the MCN 1-elicited rhythm requires the presence of reciprocal inhibition between INTl and the LG neuron, the MCN I /CPN2-elicited rhythm persists in the absence of this reciprocal inhibition, due to an inhibitory feedback synapse from INTl to CPN2 that changes the locus of coordination in the gastric mill rhythm. Next, the effect of a second feedback synapse, from the AB neuron to MCN 1, is shown to change the motor pattern of the MCN 1- and MCN1/CPN2-elicited rhythms. Finally, a third MCNI/CPN2-elicited rhythm is studied where the AB to MCN 1 feedback synapse only affects the LG burst phase of the rhythm