Neuron particles capture network topology and behavior from single units

While networks of neurons, glia and vascular systems enable and support brain functions, to date, mathematical tools to decode network dynamics and structure from very scarce and partially observed neuronal spiking behavior remain underdeveloped. Large neuronal networks contribute to the intrinsic neuron transfer function and observed neuronal spike trains encoding complex causal information processing, yet how this emerging causal fractal memory in the spike trains relates to the network topology is not fully understood. Towards this end, we propose a novel statistical physics inspired neuron particle model that captures the causal information flow and processing features of neuronal spiking activity. Relying on synthetic comprehensive simulations and real-world neuronal spiking activity analysis, the proposed fractional order operators governing the neuronal spiking dynamics provide insights into the memory and scale of the spike trains as well as information about the topological properties of the underlying neuronal networks. Lastly, the proposed model exhibits superior predictions of animal behavior during multiple cognitive tasks.

Does Emotion Talk and Emotion Knowledge Effect Children's Recall of a Staged Event?

<p>The current study compared children's memory for information accompanied by emotional or non-emotional talk, and also investigated the utility of emotion knowledge in prediction of recall. Seventy-five children aged 5-6 years participated in a staged event that involved visiting separate stations containing connected, causal information of an emotional or non-emotional theme. Children were assessed with a memory interview one week later. Children reported significantly more correct information from stations with an emotional focus. Children's emotion knowledge did not predict recall, however. Results show children better recall emotion-related information even when causality and connectedness is controlled for. Implications of the finding are discussed.</p>

Does Emotion Talk and Emotion Knowledge Effect Children's Recall of a Staged Event?

<p>The current study compared children's memory for information accompanied by emotional or non-emotional talk, and also investigated the utility of emotion knowledge in prediction of recall. Seventy-five children aged 5-6 years participated in a staged event that involved visiting separate stations containing connected, causal information of an emotional or non-emotional theme. Children were assessed with a memory interview one week later. Children reported significantly more correct information from stations with an emotional focus. Children's emotion knowledge did not predict recall, however. Results show children better recall emotion-related information even when causality and connectedness is controlled for. Implications of the finding are discussed.</p>

No guts, no glory: underestimating the benefits of providing children with mechanistic details

Previous research shows that children effectively extract and utilize causal information, yet we find that adults doubt children’s ability to understand complex mechanisms. Since adults themselves struggle to explain how everyday objects work, why expect more from children? Although remembering details may prove difficult, we argue that exposure to mechanism benefits children via the formation of abstract causal knowledge that supports epistemic evaluation. We tested 240 6–9 year-olds’ memory for concrete details and the ability to distinguish expertise before, immediately after, or a week after viewing a video about how combustion engines work. By around age 8, children who saw the video remembered mechanistic details and were better able to detect car-engine experts. Beyond detailed knowledge, the current results suggest that children also acquired an abstracted sense of how systems work that can facilitate epistemic reasoning.

Slow oscillations provide the spatio-temporal framework for long-range neural communication during sleep

Slow oscillations (SOs, <1Hz) during non-rapid eye movement sleep are thought to reflect sleep homeostasis and support memory consolidation. Yet, the fundamental properties of SOs and their impact on neural network communication are not understood. We used effective connectivity to estimate causal information flow across the electrode manifold during SOs and found two peak of information flow in specific phases of the SO. We show causal communication during non-rapid eye movement sleep peaks during specific phases of the SO, but only across long distances. We confirmed this prediction by cluster analysis demonstrating greater flow in global, compared with local, SOs. Finally, we tested the functional significance of these results by examining which SO properties supported overnight episodic memory improvement, with the underlying assumption that memory consolidation would engage global, long-range communication. Indeed, episodic memory improvement was predicted only by the SO properties with greatest causal information flow, i.e., longest distances between sinks and sources and global, but not local, SOs. These findings explain how NREM sleep (characterized as a state of low brain connectivity) leverages SO-induced selective information flow to coordinate a wide network of brain regions during memory formation.

Causal Information Rate

Information processing is common in complex systems, and information geometric theory provides a useful tool to elucidate the characteristics of non-equilibrium processes, such as rare, extreme events, from the perspective of geometry. In particular, their time-evolutions can be viewed by the rate (information rate) at which new information is revealed (a new statistical state is accessed). In this paper, we extend this concept and develop a new information-geometric measure of causality by calculating the effect of one variable on the information rate of the other variable. We apply the proposed causal information rate to the Kramers equation and compare it with the entropy-based causality measure (information flow). Overall, the causal information rate is a sensitive method for identifying causal relations.

Building the Foundation of Robot Explanation Generation Using Behavior Trees

As autonomous robots continue to be deployed near people, robots need to be able to explain their actions. In this article, we focus on organizing and representing complex tasks in a way that makes them readily explainable. Many actions consist of sub-actions, each of which may have several sub-actions of their own, and the robot must be able to represent these complex actions before it can explain them. To generate explanations for robot behavior, we propose using Behavior Trees (BTs), which are a powerful and rich tool for robot task specification and execution. However, for BTs to be used for robot explanations, their free-form, static structure must be adapted. In this work, we add structure to previously free-form BTs by framing them as a set of semantic sets {goal, subgoals, steps, actions} and subsequently build explanation generation algorithms that answer questions seeking causal information about robot behavior. We make BTs less static with an algorithm that inserts a subgoal that satisfies all dependencies. We evaluate our BTs for robot explanation generation in two domains: a kitting task to assemble a gearbox, and a taxi simulation. Code for the behavior trees (in XML) and all the algorithms is available at github.com/uml-robotics/robot-explanation-BTs.

Confounding Ghost Channels and Causality: A New Approach to Causal Information Flows

Information theory provides a fundamental framework for the quantification of information flows through channels, formally Markov kernels. However, quantities such as mutual information and conditional mutual information do not necessarily reflect the causal nature of such flows. We argue that this is often the result of conditioning based on σ-algebras that are not associated with the given channels. We propose a version of the (conditional) mutual information based on families of σ-algebras that are coupled with the underlying channel. This leads to filtrations which allow us to prove a corresponding causal chain rule as a basic requirement within the presented approach.

Information Theoretic Causality Detection between Financial and Sentiment Data

The interaction between the flow of sentiment expressed on blogs and media and the dynamics of the stock market prices are analyzed through an information-theoretic measure, the transfer entropy, to quantify causality relations. We analyzed daily stock price and daily social media sentiment for the top 50 companies in the Standard & Poor (S&P) index during the period from November 2018 to November 2020. We also analyzed news mentioning these companies during the same period. We found that there is a causal flux of information that links those companies. The largest fraction of significant causal links is between prices and between sentiments, but there is also significant causal information which goes both ways from sentiment to prices and from prices to sentiment. We observe that the strongest causal signal between sentiment and prices is associated with the Tech sector.