Neuron particles capture network topology and behavior from single units

AbstractWhile 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.

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Dehydroepiandrosterone (DHEA) and its Sulphate (DHEAS) in Alzheimer’s Disease

Current Alzheimer Research ◽

10.2174/1567205017666200317092310 ◽

2020 ◽

Vol 17 (2) ◽

pp. 141-157 ◽

Cited By ~ 2

Author(s):

Dubravka S. Strac ◽

Marcela Konjevod ◽

Matea N. Perkovic ◽

Lucija Tudor ◽

Gordana N. Erjavec ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Large Scale ◽

Therapeutic Potential ◽

Relevant Literature ◽

Comprehensive Overview ◽

Brain Functions ◽

Specific Effects ◽

And Behavior

Background: Neurosteroids Dehydroepiandrosterone (DHEA) and Dehydroepiandrosterone Sulphate (DHEAS) are involved in many important brain functions, including neuronal plasticity and survival, cognition and behavior, demonstrating preventive and therapeutic potential in different neuropsychiatric and neurodegenerative disorders, including Alzheimer’s disease. Objective: The aim of the article was to provide a comprehensive overview of the literature on the involvement of DHEA and DHEAS in Alzheimer’s disease. Method: PubMed and MEDLINE databases were searched for relevant literature. The articles were selected considering their titles and abstracts. In the selected full texts, lists of references were searched manually for additional articles. Results: We performed a systematic review of the studies investigating the role of DHEA and DHEAS in various in vitro and animal models, as well as in patients with Alzheimer’s disease, and provided a comprehensive discussion on their potential preventive and therapeutic applications. Conclusion: Despite mixed results, the findings of various preclinical studies are generally supportive of the involvement of DHEA and DHEAS in the pathophysiology of Alzheimer’s disease, showing some promise for potential benefits of these neurosteroids in the prevention and treatment. However, so far small clinical trials brought little evidence to support their therapy in AD. Therefore, large-scale human studies are needed to elucidate the specific effects of DHEA and DHEAS and their mechanisms of action, prior to their applications in clinical practice.

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Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior

Psychoradiology ◽

10.1093/psyrad/kkab002 ◽

2021 ◽

Vol 1 (1) ◽

pp. 23-41

Author(s):

Xi Jiang ◽

Tuo Zhang ◽

Shu Zhang ◽

Keith M Kendrick ◽

Tianming Liu

Keyword(s):

Brain Function ◽

Cell Biology ◽

Functional Anatomy ◽

Building Blocks ◽

Functional Differentiation ◽

Cortical Folding ◽

Brain Functions ◽

Functional Relationships ◽

Structural Architecture ◽

And Behavior

Abstract Folding of the cerebral cortex is a prominent characteristic of mammalian brains. Alterations or deficits in cortical folding are strongly correlated with abnormal brain function, cognition, and behavior. Therefore, a precise mapping between the anatomy and function of the brain is critical to our understanding of the mechanisms of brain structural architecture in both health and diseases. Gyri and sulci, the standard nomenclature for cortical anatomy, serve as building blocks to make up complex folding patterns, providing a window to decipher cortical anatomy and its relation with brain functions. Huge efforts have been devoted to this research topic from a variety of disciplines including genetics, cell biology, anatomy, neuroimaging, and neurology, as well as involving computational approaches based on machine learning and artificial intelligence algorithms. However, despite increasing progress, our understanding of the functional anatomy of gyro-sulcal patterns is still in its infancy. In this review, we present the current state of this field and provide our perspectives of the methodologies and conclusions concerning functional differentiation between gyri and sulci, as well as the supporting information from genetic, cell biology, and brain structure research. In particular, we will further present a proposed framework for attempting to interpret the dynamic mechanisms of the functional interplay between gyri and sulci. Hopefully, this review will provide a comprehensive summary of anatomo-functional relationships in the cortical gyro-sulcal system together with a consideration of how these contribute to brain function, cognition, and behavior, as well as to mental disorders.

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A convolutional neural network for estimating synaptic connectivity from spike trains

Scientific Reports ◽

10.1038/s41598-021-91244-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Daisuke Endo ◽

Ryota Kobayashi ◽

Ramon Bartolo ◽

Bruno B. Averbeck ◽

Yasuko Sugase-Miyamoto ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Generalized Linear Model ◽

Simulated Data ◽

Spike Trains ◽

Synaptic Connectivity ◽

Neuronal Circuits ◽

Cortical Areas ◽

Neuronal Spike Trains ◽

Individual Dataset

AbstractThe recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains. After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.

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Evolutionary Modularity Optimization Clustering of Neuronal Spike Trains

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-319-70093-9_55 ◽

2017 ◽

pp. 525-532

Author(s):

Chaojie Yu ◽

Yuquan Zhu ◽

Yuqing Song ◽

Hu Lu

Keyword(s):

Spike Trains ◽

Neuronal Spike ◽

Neuronal Spike Trains

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Special Issue: Neural Bases of Cognitive Processing

Brain Sciences ◽

10.3390/brainsci11101286 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1286

Author(s):

Francesco Di Russo ◽

Stefania Lucia

Keyword(s):

Cognitive Processing ◽

Cognitive Neuroscience ◽

Special Issue ◽

Mental Processes ◽

Brain Functions ◽

Neural Bases ◽

And Behavior

The main aim of Cognitive Neuroscience is investigating how brain functions lead to mental processes and behavior [...]

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A Computational Model as Neurodecoder Based on Synchronous Oscillation in the Visual Cortex

Neural Computation ◽

10.1162/089976603322362419 ◽

2003 ◽

Vol 15 (10) ◽

pp. 2399-2418 ◽

Cited By ~ 5

Author(s):

Zhao Songnian ◽

Xiong Xiaoyun ◽

Yao Guozheng ◽

Fu Zhi

Keyword(s):

Visual Cortex ◽

Computational Model ◽

Visual Information ◽

Spike Trains ◽

Spike Timing ◽

Contour Detection ◽

System Level ◽

Neuronal Spike ◽

Neuronal Spike Trains ◽

External Stimuli

Based on synchronized responses of neuronal populations in the visual cortex to external stimuli, we proposed a computational model consisting primarily of a neuronal phase-locked loop (NPLL) and multiscaled operator. The former reveals the function of synchronous oscillations in the visual cortex. Regardless of which of these patterns of the spike trains may be an average firing-rate code, a spike-timing code, or a rate-time code, the NPLL can decode original visual information from neuronal spike trains modulated with patterns of external stimuli, because a voltage-controlled oscillator (VCO), which is included in the NPLL, can precisely track neuronal spike trains and instantaneous variations, that is, VCO can make a copy of an external stimulus pattern. The latter, however, describes multi-scaled properties of visual information processing, but not merely edge and contour detection. In this study, in which we combined NPLL with a multiscaled operator and maximum likelihood estimation, we proved that the model, as a neurodecoder, implements optimum algorithm decoding visual information from neuronal spike trains at the system level. At the same time, the model also obtains increasingly important supports, which come from a series of experimental results of neurobiology on stimulus-specific neuronal oscillations or synchronized responses of the neuronal population in the visual cortex. In addition, the problem of how to describe visual acuity and multiresolution of vision by wavelet transform is also discussed. The results indicate that the model provides a deeper understanding of the role of synchronized responses in decoding visual information.

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Studying the coherence of studentsʼ portrayed representations of the atomic structure - Connections with conceptions and misconceptions

Science Education International ◽

10.33828/sei.v32.i2.10 ◽

2021 ◽

Vol 32 (2) ◽

pp. 164-171

Author(s):

Nikolaos Zarkadis ◽

◽

Dimitrios Stamovlasis ◽

George Papageorgiou ◽

◽

...

Keyword(s):

Latent Class Analysis ◽

Atomic Structure ◽

Latent Class ◽

Theory And Practice ◽

Particle Model ◽

Nuclear Model ◽

Education Students ◽

Psychometric Method ◽

Drawing Tasks ◽

And Behavior

The present study investigated the association of students’ fundamental ideas and misconceptions about ontological features of atom identity and behavior with the formation of their portrayed representations of the atomic structure. Participants (n = 421) were secondary education students in the eighth, tenth, and twelfth grades. Students’ portrayed representations of the atomic structure were accessed through drawing tasks, while their understanding of the ontological features of atom was measured through a specially designed questionnaire. Latent Class Analysis (LCA), a psychometric method, was applied to the elementary features of the portrayed representations to classify them and test the potential coherence of their representations regarding atomic structure. The LCA revealed three latent classes, which showed a relative coherence in three of the anticipated models, “Particle model,” “Nuclear model,” and “Bohrʼs model.” Moreover, students’ conceptions and misconception about the ontological features of atom were used as covariates in the LCA and their effects on the above-mentioned class-memberships were estimated. Results indicated a significant effect of students’ conceptions of the atomic ontological features on their portrayed representations of the atomic structure. Implications for theory and practice are discussed.

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A Neuromarketing Perspective for Assessing the Role and Impact of Typefaces on Consumer Purchase Decision

Advances in Marketing, Customer Relationship Management, and E-Services - Analyzing the Strategic Role of Neuromarketing and Consumer Neuroscience ◽

10.4018/978-1-7998-3126-6.ch011 ◽

2020 ◽

pp. 208-228

Author(s):

Nihal Toros Ntapiapis ◽

Çağla Özkardeşler

Keyword(s):

Consumer Behavior ◽

Literature Review ◽

Human Cognition ◽

Purchase Decision ◽

Consumer Behaviors ◽

Brain Functions ◽

New Methods ◽

Consumer Neuroscience ◽

And Behavior ◽

The Brain

Given increasing knowledge about how consumers communicate with texts, our understanding of how brain processes information remains relatively limited. Besides that, in today's world, advancing neuroscience-related technology and developments have changed the understanding of consumer behavior. In this regard, in the 1990s, consumer neuroscience and neuromarketing concepts were revealed. This new concept has brought a multi-disciplinary approach and new perceptions of human cognition and behavior. For measuring consumer behaviors through a new alternative method, research has started combining traditional marketing researches with these new methods. This chapter explores how typeface knowledge from the brain functions using neuroscience technology and the importance neurosciences methodologies have for readability research. Moreover, this chapter will evaluate how typefaces affect the purchase decision of the consumers and offer an integrative literature review.

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