scholarly journals Using a Large-scale Neural Model of Cortical Object Processing to Investigate the Neural Substrate for Managing Multiple Items in Short-term Memory

2017 ◽  
Vol 29 (11) ◽  
pp. 1860-1876 ◽  
Author(s):  
Qin Liu ◽  
Antonio Ulloa ◽  
Barry Horwitz
Keyword(s):  
Working Memory ◽  
Large Scale ◽  
Computational Models ◽  
Short Term Memory ◽  
Imaging Study ◽  
Neural Mechanism ◽  
Neural Model ◽  
Scale Model ◽  
Object Processing ◽  
Experimental Findings

Many cognitive and computational models have been proposed to help understand working memory. In this article, we present a simulation study of cortical processing of visual objects during several working memory tasks using an extended version of a previously constructed large-scale neural model [Tagamets, M. A., & Horwitz, B. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cerebral Cortex, 8, 310–320, 1998]. The original model consisted of arrays of Wilson–Cowan type of neuronal populations representing primary and secondary visual cortices, inferotemporal (IT) cortex, and pFC. We added a module representing entorhinal cortex, which functions as a gating module. We successfully implemented multiple working memory tasks using the same model and produced neuronal patterns in visual cortex, IT cortex, and pFC that match experimental findings. These working memory tasks can include distractor stimuli or can require that multiple items be retained in mind during a delay period (Sternberg's task). Besides electrophysiology data and behavioral data, we also generated fMRI BOLD time series from our simulation. Our results support the involvement of IT cortex in working memory maintenance and suggest the cortical architecture underlying the neural mechanisms mediating particular working memory tasks. Furthermore, we noticed that, during simulations of memorizing a list of objects, the first and last items in the sequence were recalled best, which may implicate the neural mechanism behind this important psychological effect (i.e., the primacy and recency effect).

scholarly journals On the structural connectivity of large-scale models of brain networks at cellular level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giuseppe Giacopelli ◽  
Domenico Tegolo ◽  
Emiliano Spera ◽  
Michele Migliore
Keyword(s):  
Large Scale ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cellular Level ◽  
Scale Model ◽  
Mathematical Framework ◽  
Underlying Mechanisms ◽  
Experimental Findings

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

Children's Speech Perception in Noise: Evidence for Dissociation From Language and Working Memory

2018 ◽  
Vol 61 (5) ◽  
pp. 1294-1305 ◽  
Author(s):  
Beula M. Magimairaj ◽  
Naveen K. Nagaraj ◽  
Natalie J. Benafield
Keyword(s):  
Working Memory ◽  
Speech Perception ◽  
Large Scale ◽  
Short Term Memory ◽  
School Age Children ◽  
School Age ◽  
Normal Range ◽  
Language Abilities ◽  
Speech Perception In Noise ◽  
Attention Memory

Purpose We examined the association between speech perception in noise (SPIN), language abilities, and working memory (WM) capacity in school-age children. Existing studies supporting the Ease of Language Understanding (ELU) model suggest that WM capacity plays a significant role in adverse listening situations. Method Eighty-three children between the ages of 7 to 11 years participated. The sample represented a continuum of individual differences in attention, memory, and language abilities. All children had normal-range hearing and normal-range nonverbal IQ. Children completed the Bamford–Kowal–Bench Speech-in-Noise Test (BKB-SIN; Etymotic Research, 2005), a selective auditory attention task, and multiple measures of language and WM. Results Partial correlations (controlling for age) showed significant positive associations among attention, memory, and language measures. However, BKB-SIN did not correlate significantly with any of the other measures. Principal component analysis revealed a distinct WM factor and a distinct language factor. BKB-SIN loaded robustly as a distinct 3rd factor with minimal secondary loading from sentence recall and short-term memory. Nonverbal IQ loaded as a 4th factor. Conclusions Results did not support an association between SPIN and WM capacity in children. However, in this study, a single SPIN measure was used. Future studies using multiple SPIN measures are warranted. Evidence from the current study supports the use of BKB-SIN as clinical measure of speech perception ability because it was not influenced by variation in children's language and memory abilities. More large-scale studies in school-age children are needed to replicate the proposed role played by WM in adverse listening situations.

scholarly journals Link Between Topographic Memory and the Combined Presentation of ADHD (ADHD-C): A Pilot Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Noemi Faedda ◽  
Cecilia Guariglia ◽  
Laura Piccardi ◽  
Giulia Natalucci ◽  
Serena Rossetti ◽  
...  
Keyword(s):  
Working Memory ◽  
Cognitive Abilities ◽  
Large Scale ◽  
Short Term Memory ◽  
Digit Span ◽  
Attention And Memory ◽  
Perseverative Errors ◽  
Spatial Layouts ◽  
Repetition Number ◽  
Visual Materials

Background: Topographic memory is the ability to reach various places by recognizing spatial layouts and getting oriented in familiar environments. It involves several different cognitive abilities, in particular executive functions (EF), such as attention, working memory, and planning. Children with attention deficit hyperactivity disorder (ADHD) show impairments in inhibitory control, regulation of attention, planning, and working memory.Aim: The aim of this study was to evaluate the topographic memory in children with ADHD-combined subtype (ADHD-C).Method: Fifteen children (8–10 years) with a diagnosis of ADHD-C (DSM-5) (ADHD-C group) were compared to 15 children with typical development (TD group) of the same age. All children performed Raven's colored progressive matrices (CPM) test to obtain a measure related with cognitive functioning. The walking Corsi test (WalCT), a large-scale version of the Corsi block-tapping test, was used to assess topographic memory in experimental environment.Results: A higher impairment was observed in ADHD-C than TD with significant differences in the WalCT, in particular on the topographic short-term memory (TSTM) task, on the topographic learning (TL) task, and on the repetition number (RN) task during the TL task. Perseverative errors were reported in performing the square-sequence in the WalCT. Zero-order correlations showed a positive correlation between TSTM and auditory attention, and memory of design of NEPSY-II and digit span of WISC-IV. No statistically significant differences were found between the ADHD-C group and TD group in the TL task in the WalCT condition.Conclusion: In ADHD-C, initial topographic learning was compromised whereas the long-term retention of learned topographical material seemed to not be impaired. In particular, these impairments seem to be linked with difficulties in sustained attention, in spatial memory for novel visual materials, in a poor working memory, and in perseverative behaviors.

scholarly journals Data Management and Modeling in Plant Biology

2021 ◽  
Vol 12 ◽  
Author(s):  
Maria Krantz ◽  
David Zimmer ◽  
Stephan O. Adler ◽  
Anastasia Kitashova ◽  
Edda Klipp ◽  
...  
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Computational Models ◽  
Quantitative Model ◽  
Plant Biology ◽  
Complex Data ◽  
Computational Statistics ◽  
Plant Environment ◽  
Experimental Findings ◽  
Statistical Trends

The study of plant-environment interactions is a multidisciplinary research field. With the emergence of quantitative large-scale and high-throughput techniques, amount and dimensionality of experimental data have strongly increased. Appropriate strategies for data storage, management, and evaluation are needed to make efficient use of experimental findings. Computational approaches of data mining are essential for deriving statistical trends and signatures contained in data matrices. Although, current biology is challenged by high data dimensionality in general, this is particularly true for plant biology. Plants as sessile organisms have to cope with environmental fluctuations. This typically results in strong dynamics of metabolite and protein concentrations which are often challenging to quantify. Summarizing experimental output results in complex data arrays, which need computational statistics and numerical methods for building quantitative models. Experimental findings need to be combined by computational models to gain a mechanistic understanding of plant metabolism. For this, bioinformatics and mathematics need to be combined with experimental setups in physiology, biochemistry, and molecular biology. This review presents and discusses concepts at the interface of experiment and computation, which are likely to shape current and future plant biology. Finally, this interface is discussed with regard to its capabilities and limitations to develop a quantitative model of plant-environment interactions.

scholarly journals Connectivity and dynamics in the olfactory bulb

2021 ◽  
Author(s):  
David EC Kersen ◽  
Gaia Tavoni ◽  
Vijay Balasubramanian
Keyword(s):  
Olfactory Bulb ◽  
Lateral Inhibition ◽  
Large Scale ◽  
Computational Models ◽  
Granule Cells ◽  
Cell Activity ◽  
Gamma Oscillations ◽  
Scale Model ◽  
Mitral Cells ◽  
Cortical Feedback

Dendrodendritic interactions between excitatory mitral cells and inhibitory granule cells in the olfactory bulb create a dense interaction network, reorganizing sensory representations of odors and, consequently, perception. Large-scale computational models are needed for revealing how the collective behavior of this network emerges from its global architecture. We propose an approach where we summarize anatomical information through dendritic geometry and density distributions which we use to calculate the probability of synapse between mitral and granule cells, while capturing activity patterns of each cell type in the neural dynamical systems theory of Izhikevich. In this way, we generate an efficient, anatomically and physiologically realistic large-scale model of the olfactory bulb network. Our model reproduces known connectivity between sister vs. non-sister mitral cells; measured patterns of lateral inhibition; and theta, beta, and gamma oscillations. It in turn predicts testable relations between network structure, lateral inhibition, and odor pattern decorrelation; between the density of granule cell activity and LFP oscillation frequency; how cortical feedback to granule cells affects mitral cell activity; and how cortical feedback to mitral cells is modulated by the network embedding. Additionally, the methodology we describe here provides a tractable tool for other researchers.

scholarly journals Towards a general framework for modeling large-scale biophysical neuronal networks: a full-scale computational model of the rat dentate gyrus

2021 ◽  
Author(s):  
Ivan Georgiev Raikov ◽  
Aaron D Milstein ◽  
Prannath Moolchand ◽  
Gergely G Szabo ◽  
Calvin J Schneider ◽  
...  
Keyword(s):  
Dentate Gyrus ◽  
Open Source ◽  
Large Scale ◽  
Computational Models ◽  
Hippocampal Formation ◽  
Full Scale ◽  
Scale Model ◽  
Population Activity ◽  
Data Movement

Large-scale computational models of the brain are necessary to accurately represent anatomical and functional variability in neuronal biophysics across brain regions and also to capture and study local and global interactions between neuronal populations on a behaviorally-relevant temporal scale. We present the methodology behind and an initial implementation of a novel open-source computational framework for construction, simulation, and analysis of models consisting of millions of neurons on high-performance computing systems, based on the NEURON and CoreNEURON simulators. This framework includes an HDF5-based data format for storing morphological, synaptic, and connectivity information of large neuronal network models, and an accompanying open source software library that provides efficient, scalable parallel storage and MPI-based data movement capabilities. We outline our approaches for constructing detailed large-scale biophysical models with topographical connectivity and input stimuli, and present simulation results obtained with a full-scale model of the dentate gyrus constructed with our framework. The model generates sparse and spatially selective population activity that fits well with in-vivo experimental data. Moreover, our approach is fully general and can be applied to modeling other regions of the hippocampal formation in order to rapidly evaluate specific hypotheses about large-scale neural architectural features.

scholarly journals Mechanisms of distributed working memory in a large-scale model of macaque neocortex

2019 ◽  
Author(s):  
Jorge F. Mejias ◽  
Xiao-Jing Wang
Keyword(s):  
Working Memory ◽  
Cognitive Processes ◽  
Large Scale ◽  
Activity Patterns ◽  
Brain Regions ◽  
Scale Model ◽  
Persistent Activity ◽  
Space Model ◽  
Cortical Areas ◽  
Large Scale Model

Recent evidence suggests that persistent neural activity underlying working memory is not a local phenomenon but distributed across multiple brain regions. To elucidate the circuit mechanism of such distributed activity, we developed an anatomically constrained computational model of large-scale macaque cortex. We found that inter-areal reverberation can support the emergence of persistent activity, even when the model operates in a regime where none of the isolated areas is capable of generating persistent activity. Persistent activity exhibits a gap of firing rate at particular cortical areas, indicating a robust bifurcation in space. Model analysis uncovered a host of spatially distinct attractor states, and yielded novel experimentally testable predictions. In the model, distributed activity patterns are resilient against simultaneous inactivation of multiple cortical areas, while dependent on a structural core. This work provides a theoretical framework for identifying large-scale brain mechanisms and computational principles of distributed cognitive processes.

scholarly journals The effect of bilingualism on lexical learning and memory across two language modalities: Some evidence for a domain-specific, but not general, advantage

2019 ◽  
Author(s):  
David Martinez
Keyword(s):  
Working Memory ◽  
Young Adults ◽  
Word Learning ◽  
Large Scale ◽  
Short Term Memory ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Short Term ◽  
Term Memory ◽  
Bilingual Advantage

The present study was conducted to replicate bilingual advantages in short-term memory for language-like material and word learning in young adults and extend this research to the sign domain, ultimately with the goal of investigating the domain specificity of bilingual advantages in cognition. Data from 112 monolingual hearing non-signers and 78 bilingual hearing non-signers were analysed for this study. Participants completed a battery of tasks assessing sign and word learning, short-term memory, working memory capacity, intelligence, and a language and demographic questionnaire. Overall, the results of this study suggested a bilingual advantage in memory for speech-like material—no other advantage (or disadvantage) was found. Results are discussed within the context of recent large-scale experimental and meta-analytic studies that have failed to find bilingual advantages in domain-general abilities such as attention control and working memory capacity in young adults.

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