Dynamical Latent State Computation in the Posterior Parietal Cortex

Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. To understand the underlying neural computations, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state -- monkey's displacement from the goal -- was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that neural interactions in PPC embody the world model to consolidate information and track task-relevant hidden states.

The Myth in the Literature of Peoples of the North: Book Review: Zhuleva, A.S. (2019). Mifopoeticheskaya model' mira v nenetskoi literature [Mythopoetic World Model in the Nenets Literature]. Moscow: IMLI RAS

The analysis of A.S. Zhulevas monograph is carried out. The author investigates Nenets mythopoetic world model on the basis of pieces of fiction and attracts various sources to fulfill this task: myths, folklore, the epos, data from linguistics, ethnography and ethnology, history of people and also materials about traditional representations in consciousness of the modern person. In the course of the research the author considered works of the different periods, including such writers as Tyko Vylka, I. Nogo and also their followers: V. Ledkov, L. Laptsuy, L. Nenyang, Yu. Vella, A. Nerkagi, etc.

Deep Reinforcement Learning For Trading—A Critical Survey

Deep reinforcement learning (DRL) has achieved significant results in many machine learning (ML) benchmarks. In this short survey, we provide an overview of DRL applied to trading on financial markets with the purpose of unravelling common structures used in the trading community using DRL, as well as discovering common issues and limitations of such approaches. We include also a short corpus summarization using Google Scholar. Moreover, we discuss how one can use hierarchy for dividing the problem space, as well as using model-based RL to learn a world model of the trading environment which can be used for prediction. In addition, multiple risk measures are defined and discussed, which not only provide a way of quantifying the performance of various algorithms, but they can also act as (dense) reward-shaping mechanisms for the agent. We discuss in detail the various state representations used for financial markets, which we consider critical for the success and efficiency of such DRL agents. The market in focus for this survey is the cryptocurrency market; the results of this survey are two-fold: firstly, to find the most promising directions for further research and secondly, to show how a lack of consistency in the community can significantly impede research and the development of DRL agents for trading.

On Declarative and Procedural Knowledge Representation

Рассматриваются преимущества и недостатки стандартных в математике и информатике способов задания множеств и представления знаний. В развитие классификации сигнализации, по Ю. В. Кнорозову, знания разделяются на невербальные (иррациональные) и вербальные (рациональные), последние, в свою очередь, подразделяются на фактуальные и фасцинационные. Доказывается, что в теории Языка как системы фигур и знаков для декодирования модели мира и сознательного управления интеллектуальной деятельностью релевантными оказываются лишь те языковые категории, которые выделены процедурно, закреплены декларативно и подтверждены комбинáторно. The article discusses the advantages and disadvantages of standard methods in mathematics and computer science for setting sets and representing knowledge. Developing the classification of signaling by Yu. V. Knorozov, we divide knowledge into non-verbal (irrational) and verbal (rational), with the latter, in turn, subdivided into factual and fascinating. The approach proves that in the theory of Language as a system of figures and signs for decoding the World Model and for conscious management of intellectual activity, only those linguistic categories that are distinguished procedurally, fixed declaratively and confirmed combinatorially can be determined as relevant.

Deep Reinforcement Learning For Trading - A Critical Survey

Deep reinforcement learning (DRL) has achieved significant results in many Machine Learning (ML) benchmarks. In this short survey we provide an overview of DRL applied to trading on financial markets, including a short meta-analysis using Google Scholar, with an emphasis on using hierarchy for dividing the problem space as well as using model-based RL to learn a world model of the trading environment which can be used for prediction. In addition, multiple risk measures are defined and discussed, which not only provide a way of quantifying the performance of various algorithms, but they can also act as (dense) reward-shaping mechanisms for the agent. We discuss in detail the various state representations used for financial markets, which we consider critical for the success and efficiency of such DRL agents. The market in focus for this survey is the cryptocurrency market.

Water Bodies in the Besserman Mythology and Rituals

Springs and rivers determine the formation of the sacral landscape of settlements and play a part in the rituals, mythology, traditional world model and space of the Besserman. They serve as essential resources of subsistence, are used domestically and are taken into account when zoning settlements. Besserman villages are located on hills near rivers and large freshwater springs. In the traditional world model, rivers connect different parts of the space, i.e. the upper and lower worlds, upper and lower reaches, sky and earth. Rivers simultaneously serve as natural and mythological borders, functioning as roads both metaphorically and literally. According to popular belief, water from rivers and springs travels to the sky via rainbows, and then falls to earth and into rivers by way of rain and snow. Historically prayers were said and rituals held near rivers from the start of the floating of the ice to the autumn. In summer, they were held only in emergency cases (drought or wet summers). Water from springs had healing properties and was used in folk medicine. Alongside traditional perceptions, the Besserman also have local rituals related to the veneration of springs that are connected with Orthodox and Muslim beliefs and revered saints.