An Optimizing Method for Performance and ResourceUtilization in Quantum Machine Learning Circuits

Quantum computing is a new and advanced topic that refers to calculations based on the principles of quantum mechanics. Itmakes certain kinds of problems be solved easier compared to classical computers. This advantage of quantum computingcan be used to implement many existing problems in different fields incredibly effectively. One important field that quantumcomputing has shown great results in machine learning. Until now, many different quantum algorithms have been presented toperform different machine learning approaches. In some special cases, the execution time of these quantum algorithms will bereduced exponentially compared to the classical ones. But at the same time, with increasing data volume and computationtime, taking care of systems to prevent unwanted interactions with the environment can be a daunting task and since thesealgorithms work on machine learning problems, which usually includes big data, their implementation is very costly in terms ofquantum resources. Here, in this paper, we have proposed an approach to reduce the cost of quantum circuits and to optimizequantum machine learning circuits in particular. To reduce the number of resources used, in this paper an approach includingdifferent optimization algorithms is considered. Our approach is used to optimize quantum machine learning algorithms forbig data. In this case, the optimized circuits run quantum machine learning algorithms in less time than the original onesand by preserving the original functionality. Our approach improves the number of quantum gates by 10.7% and 14.9% indifferent circuits and the number of time steps is reduced by three and 15 units, respectively. This is the amount of reduction forone iteration of a given sub-circuit U in the main circuit. For cases where this sub-circuit is repeated more times in the maincircuit, the optimization rate is increased. Therefore, by applying the proposed method to circuits with big data, both cost andperformance are improved.

Serotonergic neurons mediate operant conditioning in Drosophila larvae

Observed across species, operant conditioning facilitates learned associations between behaviours and outcomes, biasing future action selection to maximise reward and avoid punishment. To elucidate the underlying neural mechanisms, we built a high-throughput tracker for Drosophila melanogaster, combining real-time behaviour detection with closed-loop optogenetic and thermogenetic stimulation capabilities. We demonstrate operant conditioning in Drosophila larvae by inducing a bend direction preference through optogenetic activation of reward-encoding serotonergic neurons. Specifically, we establish that the ventral nerve cord is necessary for this memory formation. Our results extend the role of serotonergic neurons for learning in insects as well as the existence of learning circuits outside the mushroom body. This work supports future studies on the function of serotonin and the mechanisms underlying operant conditioning at both circuit and cellular levels.

Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor

Associative learning, a critical learning principle to improve an individual’s adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.

Molecular specializations of deep cortical layer analogs in songbirds

How the evolution of complex behavioral traits is associated with the emergence of novel brain pathways is largely unknown. Songbirds, like humans, learn vocalizations via tutor imitation and possess a specialized brain circuitry to support this behavior. In a comprehensive in situ hybridization effort, we show that the zebra finch vocal robust nucleus of the arcopallium (RA) shares numerous markers (e.g. SNCA, PVALB) with the adjacent dorsal intermediate arcopallium (AId), an avian analog of mammalian deep cortical layers with involvement in motor function. We also identify markers truly unique to RA and thus likely linked to modulation of vocal motor function (e.g. KCNC1, GABRE), including a subset of the known shared markers between RA and human laryngeal motor cortex (e.g. SLIT1, RTN4R, LINGO1, PLXNC1). The data provide novel insights into molecular features unique to vocal learning circuits, and lend support for the motor theory for vocal learning origin.

Executive modulation of brain reward systems endows goals with value

ABSTRACTRecent evidence suggests that executive processes shape reinforcement learning (RL) computations. Here, we extend this idea to the processing of choice outcomes, asking if executive function and RL interact during learning from novel goals. We designed a task where people learned from familiar rewards or abstract instructed goals. We hypothesized that learning from these goals would produce reliable responses in canonical reward circuits, and would do so by leveraging executive function. Behavioral results pointed to qualitatively similar learning processes when subjects learned from achieving goals versus familiar rewards. Goal learning was robustly and selectively correlated with performance on an independent executive function task. Neuroimaging revealed comparable appetitive responses and computational signatures in reinforcement learning circuits for both goal-based and familiar learning contexts. During goal learning, we observed enhanced correlations between prefrontal cortex and canonical reward-sensitive regions, including hippocampus, striatum, and the midbrain. These findings demonstrate that attaining novel goals produces reliable reward signals in dopaminergic circuits. We propose that learning from goal-directed behavior is mediated by top-down input that primes the reward system to endow value to cues signaling goal attainment.

Proximal threats promote enhanced acquisition and persistence of reactive fear-learning circuits

Physical proximity to a traumatic event increases the severity of accompanying stress symptoms, an effect that is reminiscent of evolutionarily configured fear responses based on threat imminence. Despite being widely adopted as a model system for stress and anxiety disorders, fear-conditioning research has not yet characterized how threat proximity impacts the mechanisms of fear acquisition and extinction in the human brain. We used three-dimensional (3D) virtual reality technology to manipulate the egocentric distance of conspecific threats while healthy adult participants navigated virtual worlds during functional magnetic resonance imaging (fMRI). Consistent with theoretical predictions, proximal threats enhanced fear acquisition by shifting conditioned learning from cognitive to reactive fear circuits in the brain and reducing amygdala–cortical connectivity during both fear acquisition and extinction. With an analysis of representational pattern similarity between the acquisition and extinction phases, we further demonstrate that proximal threats impaired extinction efficacy via persistent multivariate representations of conditioned learning in the cerebellum, which predicted susceptibility to later fear reinstatement. These results show that conditioned threats encountered in close proximity are more resistant to extinction learning and suggest that the canonical neural circuitry typically associated with fear learning requires additional consideration of a more reactive neural fear system to fully account for this effect.

Fast retrograde access to projection neuron circuits underlying vocal learning in songbirds

SummaryUnderstanding the structure and function of neural circuits underlying speech and language is a vital step towards better treatments for diseases of these systems. Songbirds, among the few animal orders that share with humans the ability to learn vocalizations from a conspecific, have provided many insights into the neural mechanisms of vocal development. However, research into vocal learning circuits has been hindered by a lack of tools for rapid genetic targeting of specific neuron populations to meet the quick pace of developmental learning. Here, we present a new viral tool that enables fast and efficient retrograde access to projection neuron populations. In zebra finches, Bengalese finches, canaries, and mice, we demonstrate fast retrograde labeling of cortical or dopaminergic neurons. We further demonstrate the suitability of our construct for detailed morphological analysis, for in vivo imaging of calcium activity, and for multicolor brainbow labeling.

Curiosity from the Perspective of Systems Neuroscience

Curiosity refers to a demand for information that has no instrumental benefit. Because of its critical role in development and in the regulation of learning, curiosity has long fascinated psychologists. However, it has been difficult to study curiosity from the perspective of the single neuron, the circuit, and systems neuroscience. Recent advances, however, have made doing so more feasible. These include theoretical advances in defining curiosity in animal models, the development of tasks that manipulate curiosity, and the preliminary identification of circuits responsible for curiosity-motivated learning. Taken together, resulting scholarship demonstrates the key roles of executive control, reward, and learning circuits in driving curiosity; and has helped us to understand how curiosity relates to information-seeking more broadly. This work has implications for mechanisms of reward-based decisions in general. Here we summarize these results and highlight important remaining questions for the future of curiosity studies.