scholarly journals Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 385 ◽  
Author(s):  
Ali Tehrani-Saleh ◽  
Christoph Adami
Keyword(s):  
Cognitive Processing ◽  
Information Flow ◽  
Cognitive Task ◽  
Evolutionary Process ◽  
Logic Gates ◽  
Ground Truth ◽  
Transfer Entropy ◽  
Causal Connection ◽  
Directed Information ◽  
Flow Of Information

How cognitive neural systems process information is largely unknown, in part because of how difficult it is to accurately follow the flow of information from sensors via neurons to actuators. Measuring the flow of information is different from measuring correlations between firing neurons, for which several measures are available, foremost among them the Shannon information, which is an undirected measure. Several information-theoretic notions of “directed information” have been used to successfully detect the flow of information in some systems, in particular in the neuroscience community. However, recent work has shown that directed information measures such as transfer entropy can sometimes inadequately estimate information flow, or even fail to identify manifest directed influences, especially if neurons contribute in a cryptographic manner to influence the effector neuron. Because it is unclear how often such cryptic influences emerge in cognitive systems, the usefulness of transfer entropy measures to reconstruct information flow is unknown. Here, we test how often cryptographic logic emerges in an evolutionary process that generates artificial neural circuits for two fundamental cognitive tasks (motion detection and sound localization). Besides counting the frequency of problematic logic gates, we also test whether transfer entropy applied to an activity time-series recorded from behaving digital brains can infer information flow, compared to a ground-truth model of direct influence constructed from connectivity and circuit logic. Our results suggest that transfer entropy will sometimes fail to infer directed information when it exists, and sometimes suggest a causal connection when there is none. However, the extent of incorrect inference strongly depends on the cognitive task considered. These results emphasize the importance of understanding the fundamental logic processes that contribute to information flow in cognitive processing, and quantifying their relevance in any given nervous system.

DATA DISCRETIZATION FOR THE TRANSFER ENTROPY IN FINANCIAL MARKET

2013 ◽  
Vol 12 (04) ◽  
pp. 1350019 ◽  
Author(s):  
XUEJIAO WANG ◽  
PENGJIAN SHANG ◽  
JINGJING HUANG ◽  
GUOCHEN FENG
Keyword(s):  
Financial Market ◽  
Information Flow ◽  
Coarse Graining ◽  
Transfer Entropy ◽  
Linear Modeling ◽  
Information Theoretic ◽  
Model Free ◽  
Temporal Correlations ◽  
Data Discretization ◽  
Flow Of Information

Recently, an information theoretic inspired concept of transfer entropy has been introduced by Schreiber. It aims to quantify in a nonparametric and explicitly nonsymmetric way the flow of information between two time series. This model-free based on Shannon entropy approach in principle allows us to detect statistical dependencies of all types, i.e., linear and nonlinear temporal correlations. However, we always analyze the transfer entropy based on the data, which is discretized into three partitions by some coarse graining. Naturally, we are interested in investigating the effect of the data discretization of the two series on the transfer entropy. In our paper, we analyze the results based on the data which are generated by the linear modeling and the ARFIMA modeling, as well as the dataset consists of seven indices during the period 1992–2002. The results show that the higher the degree of data discretization get, the larger the value of the transfer entropy will be, besides, the direction of the information flow is unchanged along with the degree of data discretization.

THE DYNAMICS OF PRICE–VOLUME INFORMATION TRANSFER IN THE CRYPTOCURRENCY MARKETS

2020 ◽  
Vol 23 (05) ◽  
pp. 2050014
Author(s):  
JINGLAN ZHENG ◽  
CHUN-XIAO NIE
Keyword(s):  
Information Flow ◽  
Information Transfer ◽  
Transfer Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Dynamic Information ◽  
Market Values ◽  
Daily Data ◽  
Flow Of Information

This study examines the information flow between prices and transaction volumes in the cryptocurrency market, where transfer entropy is used for measurement. We selected four cryptocurrencies (Bitcoin, Ethereum, Litecoin and XRP) with large market values, and Bitcoin and BCH (Bitcoin Cash) for hard fork analysis; a hard fork is when a single cryptocurrency splits in two. By examining the real price data, we show that the long-term time series includes too much noise obscuring the local information flow; thus, a dynamic calculation is needed. The long-term and short-term sliding transfer entropy (TE) values and the corresponding [Formula: see text]-values, based on daily data, indicate that there is a dynamic information flow. The dominant direction of which is [Formula: see text]. In addition, the example based on minute Bitcoin data also shows a dynamic flow of information between price and transaction volume. The price–volume dynamics of multiple time scales helps to analyze the price mechanism in the cryptocurrency market.

scholarly journals Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy

2020 ◽  
Vol 13 ◽  
Author(s):  
Dennis Joe Harmah ◽  
Cunbo Li ◽  
Fali Li ◽  
Yuanyuan Liao ◽  
Jiuju Wang ◽  
...  
Keyword(s):  
Information Flow ◽  
Transfer Entropy ◽  
Non Linear ◽  
Directed Information

scholarly journals Eye Movements in Response to Pain-Related Feelings in the Presence of Low and High Cognitive Loads

2020 ◽  
Vol 10 (5) ◽  
pp. 92
Author(s):  
Ramtin Zargari Marandi ◽  
Camilla Ann Fjelsted ◽  
Iris Hrustanovic ◽  
Rikke Dan Olesen ◽  
Parisa Gazerani
Keyword(s):  
Eye Movements ◽  
Cognitive Load ◽  
Eye Movement ◽  
Cognitive Processing ◽  
Pain Perception ◽  
Cognitive Task ◽  
Load Level ◽  
Task Load ◽  
Pupillary Responses ◽  
Movement Characteristics

The affective dimension of pain contributes to pain perception. Cognitive load may influence pain-related feelings. Eye tracking has proven useful for detecting cognitive load effects objectively by using relevant eye movement characteristics. In this study, we investigated whether eye movement characteristics differ in response to pain-related feelings in the presence of low and high cognitive loads. A set of validated, control, and pain-related sounds were applied to provoke pain-related feelings. Twelve healthy young participants (six females) performed a cognitive task at two load levels, once with the control and once with pain-related sounds in a randomized order. During the tasks, eye movements and task performance were recorded. Afterwards, the participants were asked to fill out questionnaires on their pain perception in response to the applied cognitive loads. Our findings indicate that an increased cognitive load was associated with a decreased saccade peak velocity, saccade frequency, and fixation frequency, as well as an increased fixation duration and pupil dilation range. Among the oculometrics, pain-related feelings were reflected only in the pupillary responses to a low cognitive load. The performance and perceived cognitive load decreased and increased, respectively, with the task load level and were not influenced by the pain-related sounds. Pain-related feelings were lower when performing the task compared with when no task was being performed in an independent group of participants. This might be due to the cognitive engagement during the task. This study demonstrated that cognitive processing could moderate the feelings associated with pain perception.

Voice And Stream: Perceptual And Computational Modeling Of Voice Separation

2008 ◽  
Vol 26 (1) ◽  
pp. 75-94 ◽  
Author(s):  
Emilios Cambouropoulos
Keyword(s):  
Computational Modeling ◽  
Specific Problem ◽  
Cognitive Task ◽  
Ground Truth ◽  
Musical Score ◽  
Auditory Stream ◽  
Multiple Voices ◽  
Proposed Model ◽  
Single Voice ◽  
Small Dataset

LISTENERS ARE THOUGHT TO BE CAPABLE of perceiving multiple voices in music. This paper presents different views of what 'voice' means and how the problem of voice separation can be systematically described, with a view to understanding the problem better and developing a systematic description of the cognitive task of segregating voices in music. Well-established perceptual principles of auditory streaming are examined and then tailored to the more specific problem of voice separation in timbrally undifferentiated music. Adopting a perceptual view of musical voice, a computational prototype is developed that splits a musical score (symbolic musical data) into different voices. A single 'voice' may consist of one or more synchronous notes that are perceived as belonging to the same auditory stream. The proposed model is tested against a small dataset that acts as ground truth. The results support the theoretical viewpoint adopted in the paper.

scholarly journals Applying transfer entropy approach to investigate the information flow among ASEAN stock markets

2020 ◽  
Vol 4 (1) ◽  
pp. First
Author(s):  
Tran Thi Tuan Anh
Keyword(s):  
Information Flow ◽  
Stock Markets ◽  
Investment Strategy ◽  
The Philippines ◽  
Transfer Entropy ◽  
The Other ◽  
Information Flows ◽  
Research Results ◽  
Region Data ◽  
Asean Countries

This paper uses transfer entropy to measure and identify the information flows between stock markets in the ASEAN region. Data on daily closing stock indices, including Vietnam, the Philippines, Malaysia, Indonesia, Thailand, and Singapore, are collected for the period from March 2012 to October 2019 to calculate these transfer entropies. The research results of this article can be considered in two aspects: one is, how information flow originating from one market will be accepted by other markets and secondly, information flow that markets receive. From the perspective of incoming transfer entropy, Vietnam is the country most affected by information from the other ASEAN markets while Indonesia and Malaysia are the least affected. In terms of outgoing entropy, Thailand is the largest source of information flow to the ASEAN markets. Malaysia and the Philippines are the two countries that receive minor information impact from other countries. The research also reveals that the Singapore stock market is rather separate from the other ASEAN countries. The research results also imply that, for investors and policymakers, defining the information flows among ASEAN stock markets can help to predict market movements, thereby developing a suitable investment strategy or establishing appropriate management policies.

scholarly journals Prefrontal D1 dopamine-receptor neurons and delta resonance in interval timing

2017 ◽  
Author(s):  
Young-Cho Kim ◽  
Nandakumar S. Narayanan
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Processing ◽  
Cognitive Task ◽  
Interval Timing ◽  
Time Dependent ◽  
Performance Deficits ◽  
Timing Task ◽  
Memory Flexibility ◽  
Receptor Neurons

AbstractConsiderable evidence has shown that prefrontal neurons expressing D1-type dopamine receptors (D1DRs) are critical for working memory, flexibility, and timing. This line of work predicts that frontal neurons expressing D1DRs mediate cognitive processing. During timing tasks, one form this cognitive processing might take is time-dependent ramping activity — monotonic changes in firing rate over time. Thus, we hypothesized the prefrontal D1DR+ neurons would strongly exhibited time-dependent ramping during interval timing. We tested this idea using an interval-timing task in which we used optogenetics to tag D1DR+ neurons in the mouse medial frontal cortex (MFC). While 23% of MFC D1DR+ neurons exhibited ramping, this was significantly less than untagged MFC D1DR+ neurons. By contrast, MFC D1DR+ neurons had strong delta-frequency (1-4 Hz) coherence with other MFC ramping neurons. This coherence was phase-locked to cue onset and was strongest early in the interval. To test the significance of these interactions, we optogenetically stimulated MFC D1DR+ neurons early vs. late in the interval. We found that 2-Hz stimulation early in the interval was particularly effective in rescuing timing-related behavioral performance deficits in dopamine-depleted animals. These findings provide insight into MFC networks and have relevance for disorders such as Parkinson’s disease and schizophrenia.Significance StatementPrefrontal D1DRs are involved in cognitive processing and cognitive dysfunction in human diseases such as Parkinson’s disease and schizophrenia. We use optogenetics to identify these neurons, as well as neurons that are putatively connected to MFC D1DR+ neurons. We study these neurons in detail during an elementary cognitive task. These data could have relevance for cognitive deficits for Parkinson’s disease, schizophrenia, and other diseases involving frontal dopamine.

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