A Platform for Spatiotemporal “Matrix” Stimulation in Brain Networks Reveals Novel Forms of Circuit Plasticity

Here we demonstrate a facile method by which to deliver complex spatiotemporal stimulation to neural networks in fast patterns, to trigger interesting forms of circuit-level plasticity in cortical areas. We present a complete platform by which patterns of electricity can be arbitrarily defined and distributed across a brain circuit, either simultaneously, asynchronously, or in complex patterns that can be easily designed and orchestrated with precise timing. Interfacing with acute slices of mouse cortex, we show that our system can be used to activate neurons at many locations and drive synaptic transmission in distributed patterns, and that this elicits new forms of plasticity that may not be observable via traditional methods, including interesting measurements of associational and sequence plasticity. Finally, we introduce an automated “network assay” for imaging activation and plasticity across a circuit. Spatiotemporal stimulation opens the door for high-throughput explorations of plasticity at the circuit level, and may provide a basis for new types of adaptive neural prosthetics.

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Precise timing of neuronal discharges within and across cortical areas: Implications for synaptic transmission

Journal of Physiology-Paris ◽

10.1016/s0928-4257(97)81427-1 ◽

1996 ◽

Vol 90 (3-4) ◽

pp. 221-222 ◽

Cited By ~ 10

Author(s):

W Singer ◽

AK Kreiter ◽

AK Engel ◽

P Fries ◽

PR Roelfsema ◽

...

Keyword(s):

Synaptic Transmission ◽

Cortical Areas ◽

Precise Timing

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High-throughput phenotyping of two plant-size traits of Eucalyptus species using neural networks

Journal of Forestry Research ◽

10.1007/s11676-021-01360-6 ◽

2021 ◽

Author(s):

Marcus Vinicius Vieira Borges ◽

Janielle de Oliveira Garcia ◽

Tays Silva Batista ◽

Alexsandra Nogueira Martins Silva ◽

Fabio Henrique Rojo Baio ◽

...

Keyword(s):

Neural Networks ◽

High Throughput ◽

Mean Squared Error ◽

Vegetation Indices ◽

Plant Size ◽

Eucalyptus Species ◽

Forest Inventories ◽

Spectral Bands ◽

High Throughput Phenotyping ◽

Input Variables

AbstractIn forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.

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Reconfigurable Binary Neural Network Accelerator with Adaptive Parallelism Scheme

Electronics ◽

10.3390/electronics10030230 ◽

2021 ◽

Vol 10 (3) ◽

pp. 230

Author(s):

Jaechan Cho ◽

Yongchul Jung ◽

Seongjoo Lee ◽

Yunho Jung

Keyword(s):

Neural Networks ◽

High Throughput ◽

Deep Neural Networks ◽

State Of The Art ◽

Throughput Performance ◽

Adaptive Parallelism ◽

Sensor Applications ◽

Binary Neural Network ◽

Target Layer ◽

Network Topologies

Binary neural networks (BNNs) have attracted significant interest for the implementation of deep neural networks (DNNs) on resource-constrained edge devices, and various BNN accelerator architectures have been proposed to achieve higher efficiency. BNN accelerators can be divided into two categories: streaming and layer accelerators. Although streaming accelerators designed for a specific BNN network topology provide high throughput, they are infeasible for various sensor applications in edge AI because of their complexity and inflexibility. In contrast, layer accelerators with reasonable resources can support various network topologies, but they operate with the same parallelism for all the layers of the BNN, which degrades throughput performance at certain layers. To overcome this problem, we propose a BNN accelerator with adaptive parallelism that offers high throughput performance in all layers. The proposed accelerator analyzes target layer parameters and operates with optimal parallelism using reasonable resources. In addition, this architecture is able to fully compute all types of BNN layers thanks to its reconfigurability, and it can achieve a higher area–speed efficiency than existing accelerators. In performance evaluation using state-of-the-art BNN topologies, the designed BNN accelerator achieved an area–speed efficiency 9.69 times higher than previous FPGA implementations and 24% higher than existing VLSI implementations for BNNs.

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Neural network based stochastic design charts for settlement prediction

Canadian Geotechnical Journal ◽

10.1139/t04-096 ◽

2005 ◽

Vol 42 (1) ◽

pp. 110-120 ◽

Cited By ~ 17

Author(s):

M A Shahin ◽

M B Jaksa ◽

H R Maier

Keyword(s):

Neural Networks ◽

Monte Carlo ◽

Artificial Neural Networks ◽

Shallow Foundations ◽

Granular Soils ◽

Traditional Methods ◽

Settlement Prediction ◽

Design Charts ◽

Artificial Neural ◽

Level Of Risk

Traditional methods of settlement prediction of shallow foundations on granular soils are far from accurate and consistent. This can be attributed to the fact that the problem of estimating the settlement of shallow foundations on granular soils is very complex and not yet entirely understood. Recently, artificial neural networks (ANNs) have been shown to outperform the most commonly used traditional methods for predicting the settlement of shallow foundations on granular soils. However, despite the relative advantage of the ANN based approach, it does not take into account the uncertainty that may affect the magnitude of the predicted settlement. Artificial neural networks, like more traditional methods of settlement prediction, are based on deterministic approaches that ignore this uncertainty and thus provide single values of settlement with no indication of the level of risk associated with these values. An alternative stochastic approach is essential to provide more rational estimation of settlement. In this paper, the likely distribution of predicted settlements, given the uncertainties associated with settlement prediction, is obtained by combining Monte Carlo simulation with a deterministic ANN model. A set of stochastic design charts, which incorporate the uncertainty associated with the ANN method, is developed. The charts are considered to be useful in the sense that they enable the designer to make informed decisions regarding the level of risk associated with predicted settlements and consequently provide a more realistic indication of what the actual settlement might be.Key words: settlement prediction, shallow foundations, neural networks, Monte Carlo, stochastic simulation.

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Alzheimer disease neuropathology:understanding autonomic dysfunction

Dementia & Neuropsychologia ◽

10.1590/s1980-57642009dn20300004 ◽

2008 ◽

Vol 2 (3) ◽

pp. 183-191 ◽

Cited By ~ 15

Author(s):

Eliasz Engelhardt ◽

Jerson Laks

Keyword(s):

Alzheimer’S Disease ◽

Neural Networks ◽

Alzheimer's Disease ◽

Autonomic Dysfunction ◽

Disease Process ◽

Functional Impairments ◽

Cortical Areas ◽

Manual Search ◽

Neurodegenerative Process ◽

Vital Functions

Abstract Alzheimer's disease is a widely studied disorder with research focusing on cognitive and functional impairments, behavioral and psychological symptoms, and on abnormal motor manifestations. Despite the importance of autonomic dysfunctions they have received less attention in systematic studies. The underlying neurodegenerative process of AD, mainly affecting cortical areas, has been studied for more than one century. However, autonomic-related structures have not been studied neuropathologically with the same intensity. The autonomic nervous system governs normal visceral functions, and its activity is expressed in relation to homeostatic needs of the organism's current physical and mental activities. The disease process leads to autonomic dysfunction or dysautonomy possibly linked to increased rates of morbidity and mortality. Objective: The aim of this review was to analyze the cortical, subcortical, and more caudal autonomic-related regions, and the specific neurodegenerative process in Alzheimer's disease that affects these structures. Methods: A search for papers addressing autonomic related-structures affected by Alzheimer's degeneration, and under normal condition was performed through MedLine, PsycInfo and Lilacs, on the bibliographical references of papers of interest, together with a manual search for classic studies in older journals and books, spanning over a century of publications. Results: The main central autonomic-related structures are described, including cortical areas, subcortical structures (amygdala, thalamus, hypothalamus, brainstem, cerebellum) and spinal cord. They constitute autonomic neural networks that underpin vital functions. These same structures, affected by specific Alzheimer's disease neurodegeneration, were also described in detail. The autonomic-related structures present variable neurodegenerative changes that develop progressively according to the degenerative stages described by Braak and Braak. Conclusion: The neural networks constituted by the central autonomic-related structures, when damaged by progressive neurodegeneration, represent the neuropathological substrate of autonomic dysfunction. The presence of this dysfunction and its possible relationship with higher rates of morbidity, and perhaps of mortality, in affected subjects must be kept in mind when managing Alzheimer's patients.

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DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput

Nature Methods ◽

10.1038/s41592-019-0638-x ◽

2019 ◽

Vol 17 (1) ◽

pp. 41-44 ◽

Cited By ~ 42

Author(s):

Vadim Demichev ◽

Christoph B. Messner ◽

Spyros I. Vernardis ◽

Kathryn S. Lilley ◽

Markus Ralser

Keyword(s):

Neural Networks ◽

High Throughput ◽

Proteome Coverage ◽

Interference Correction

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The splicing regulator PTBP2 controls a program of embryonic splicing required for neuronal maturation

eLife ◽

10.7554/elife.01201 ◽

2014 ◽

Vol 3 ◽

Cited By ~ 82

Author(s):

Qin Li ◽

Sika Zheng ◽

Areum Han ◽

Chia-Ho Lin ◽

Peter Stoilov ◽

...

Keyword(s):

Synaptic Transmission ◽

Neurite Growth ◽

Protein Isoforms ◽

Neuronal Maturation ◽

Wild Type ◽

Mouse Cortex ◽

Final Maturation ◽

Splicing Regulator ◽

Mutant Cells ◽

Normal Cortex

We show that the splicing regulator PTBP2 controls a genetic program essential for neuronal maturation. Depletion of PTBP2 in developing mouse cortex leads to degeneration of these tissues over the first three postnatal weeks, a time when the normal cortex expands and develops mature circuits. Cultured Ptbp2−/− neurons exhibit the same initial viability as wild type, with proper neurite outgrowth and marker expression. However, these mutant cells subsequently fail to mature and die after a week in culture. Transcriptome-wide analyses identify many exons that share a pattern of mis-regulation in the mutant brains, where isoforms normally found in adults are precociously expressed in the developing embryo. These transcripts encode proteins affecting neurite growth, pre- and post-synaptic assembly, and synaptic transmission. Our results define a new genetic regulatory program, where PTBP2 acts to temporarily repress expression of adult protein isoforms until the final maturation of the neuron.

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