scholarly journals A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain

2021 ◽  
Author(s):  
Anna-Maria Jürgensen ◽  
Afshin Khalili ◽  
Elisabetta Chicca ◽  
Giacomo Indiveri ◽  
Martin Paul Nawrot
Keyword(s):  
Sparse Coding ◽  
Sensory Input ◽  
Feedback Inhibition ◽  
Fruit Fly ◽  
Olfactory Pathway ◽  
Computing Paradigm ◽  
Temporal Domain ◽  
Neuromorphic Hardware ◽  
Receptor Input ◽  
Computational Resources

Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of similar mechanism to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous system of the fruit fly larva. With its strongly limited computational resources of <200 neurons and <1.000 synapses the larval olfactory pathway employs fundamental computations to transform broadly tuned receptor input at the periphery into an energy efficient sparse code in the central brain. We show how this approach allows us to achieve sparse coding and increased separability of stimulus patterns in a spiking neural network, validated with both software simulation and hardware emulation on mixed-signal real-time neuromorphic hardware. We verify that feedback inhibition is the central motif to support sparseness in the spatial domain, across the neuron population, while the combination of spike frequency adaptation and feedback inhibition determines sparseness in the temporal domain. Our experiments demonstrate that such small-sized, biologically realistic neural networks, efficiently implemented on neuromorphic hardware, can achieve parallel processing and efficient encoding of sensory input at full temporal resolution.

scholarly journals A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain

2021 ◽  
Author(s):  
Anna-Maria Jürgensen ◽  
Afshin Khalili ◽  
Elisabetta Chicca ◽  
Giacomo Indiveri ◽  
Martin Paul Nawrot
Keyword(s):  
Neural Network ◽  
Sparse Coding ◽  
Sensory Input ◽  
Feedback Inhibition ◽  
Fruit Fly ◽  
Olfactory Pathway ◽  
Computing Paradigm ◽  
Neuromorphic Hardware ◽  
Receptor Input ◽  
Computational Resources

Abstract Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of neural network computations to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous system of the fruit fly larva. With its strongly limited computational resources of <200 neurons and <1.000 synapses the larval olfactory pathway employs fundamental computations to transform broadly tuned receptor input at the periphery into an energy efficient sparse code in the central brain. We show how this approach allows us to achieve sparse coding and increased separability of stimulus patterns in a spiking neural network, validated with both software simulation and hardware emulation on mixed-signal real-time neuromorphic hardware. We verify that feedback inhibition is the central motif to support sparseness in the spatial domain, across the neuron population, while the combination of spike frequency adaptation and feedback inhibition determines sparseness in the temporal domain. Our experiments demonstrate that such small-sized, biologically realistic neural networks, efficiently implemented on neuromorphic hardware, can achieve parallel processing and efficient encoding of sensory input at full temporal resolution.

Grid Computing in 3D Electron Microscopy Reconstruction

2012 ◽  
pp. 881-898
Author(s):  
J.R. Bilbao-Castro ◽  
I. García ◽  
J.J. Fernández
Keyword(s):  
Electron Microscopy ◽  
Grid Computing ◽  
Three Dimensional ◽  
Dimensional Electron ◽  
Reconstruction Process ◽  
Computing Paradigm ◽  
Structural Conformation ◽  
Reconstructed Volume ◽  
Dimensional Reconstruction ◽  
Computational Resources

Three-dimensional electron microscopy allows scientists to study biological specimens and to understand how they behave and interact with each other depending on their structural conformation. Electron microscopy projections of the specimens are taken from different angles and are processed to obtain a virtual three-dimensional reconstruction for further studies. Nevertheless, the whole reconstruction process, which is composed of many different subtasks from the microscope to the reconstructed volume, is not straightforward nor cheap in terms of computational costs. Different computing paradigms have been applied in order to overcome such high costs. While classic parallel computing using mainframes and clusters of workstations is usually enough for average requirements, there are some tasks which would fit better into a different computing paradigm – such as grid computing. Such tasks can be split up into a myriad of subtasks, which can then be run independently using as many computational resources as are available. This chapter explores two of these tasks present in a typical three-dimensional electron microscopy reconstruction process. In addition, important aspects like fault-tolerance are widely covered; given that the distributed nature of a grid infrastructure makes it inherently unstable and difficult to predict.

Coding of Stimulus Frequency by Latency in Thalamic Networks Through the Interplay of GABAB-Mediated Feedback and Stimulus Shape

2006 ◽  
Vol 95 (3) ◽  
pp. 1735-1750 ◽  
Author(s):  
David Golomb ◽  
Ehud Ahissar ◽  
David Kleinfeld
Keyword(s):  
Brain Stem ◽  
Sensory Input ◽  
Feedback Inhibition ◽  
Stimulus Frequency ◽  
Period Doubling ◽  
Spike Rate ◽  
Temporal Coding ◽  
Normal Brain ◽  
Normal Brain Function ◽  
Sensory Code

A temporal sensory code occurs in posterior medial (POm) thalamus of the rat vibrissa system, where the latency for the spike rate to peak is observed to increase with increasing frequency of stimulation between 2 and 11 Hz. In contrast, the latency of the spike rate in the ventroposterior medial (VPm) thalamus is constant in this frequency range. We consider the hypothesis that two factors are essential for latency coding in the POm. The first is GABAB-mediated feedback inhibition from the reticular thalamic (Rt) nucleus, which provides delayed and prolonged input to thalamic structures. The second is sensory input that leads to an accelerating spike rate in brain stem nuclei. Essential aspects of the experimental observations are replicated by the analytical solution of a rate-based model with a minimal architecture that includes only the POm and Rt nuclei, i.e., an increase in stimulus frequency will increase the level of inhibitory output from Rt thalamus and lead to a longer latency in the activation of POm thalamus. This architecture, however, admits period-doubling at high levels of GABAB-mediated conductance. A full architecture that incorporates the VPm nucleus suppresses period-doubling. A clear match between the experimentally measured spike rates and the numerically calculated rates for the full model occurs when VPm thalamus receives stronger brain stem input and weaker GABAB-mediated inhibition than POm thalamus. Our analysis leads to the prediction that the latency code will disappear if GABAB-mediated transmission is blocked in POm thalamus or if the onset of sensory input is too abrupt. We suggest that GABAB-mediated inhibition is a substrate of temporal coding in normal brain function.

Grid Computing in 3D Electron Microscopy Reconstruction

2011 ◽  
pp. 392-409
Author(s):  
J.R. Bilbao Castro ◽  
I. Garcia Fernandez ◽  
J. Fernandez
Keyword(s):  
Electron Microscopy ◽  
Grid Computing ◽  
Three Dimensional ◽  
Dimensional Electron ◽  
Reconstruction Process ◽  
Computing Paradigm ◽  
Structural Conformation ◽  
Reconstructed Volume ◽  
Dimensional Reconstruction ◽  
Computational Resources

Three-dimensional electron microscopy allows scientists to study biological specimens and to understand how they behave and interact with each other depending on their structural conformation. Electron microscopy projections of the specimens are taken from different angles and are processed to obtain a virtual three-dimensional reconstruction for further studies. Nevertheless, the whole reconstruction process, which is composed of many different subtasks from the microscope to the reconstructed volume, is not straightforward nor cheap in terms of computational costs. Different computing paradigms have been applied in order to overcome such high costs. While classic parallel computing using mainframes and clusters of workstations is usually enough for average requirements, there are some tasks which would fit better into a different computing paradigm – such as grid computing. Such tasks can be split up into a myriad of subtasks, which can then be run independently using as many computational resources as are available. This chapter explores two of these tasks present in a typical three-dimensional electron microscopy reconstruction process. In addition, important aspects like fault-tolerance are widely covered; given that the distributed nature of a grid infrastructure makes it inherently unstable and difficult to predict.

scholarly journals Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 440
Author(s):  
Anup Vanarse ◽  
Adam Osseiran ◽  
Alexander Rassau ◽  
Peter van der Made
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Olfactory Pathway ◽  
Ultra Low Power ◽  
Real Time Processing ◽  
Power Efficient ◽  
Neuromorphic Hardware ◽  
Neurophysiological Studies

Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-rich sensor data. These advancements are complemented by the application of bioinspired algorithms and architectures based on findings from neurophysiological studies focusing on the biological olfactory pathway. The application of spiking neural networks (SNNs), and concepts from neuromorphic engineering in general, are one of the key factors that has led to the design and development of efficient bioinspired e-nose systems. However, only a limited number of studies have focused on deploying these models on a natively event-driven hardware platform that exploits the benefits of neuromorphic implementation, such as ultra-low-power consumption and real-time processing, for simplified integration in a portable e-nose system. In this paper, we extend our previously reported neuromorphic encoding and classification approach to a real-world dataset that consists of sensor responses from a commercial e-nose system when exposed to eight different types of malts. We show that the proposed SNN-based classifier was able to deliver 97% accurate classification results at a maximum latency of 0.4 ms per inference with a power consumption of less than 1 mW when deployed on neuromorphic hardware. One of the key advantages of the proposed neuromorphic architecture is that the entire functionality, including pre-processing, event encoding, and classification, can be mapped on the neuromorphic system-on-a-chip (NSoC) to develop power-efficient and highly-accurate real-time e-nose systems.

scholarly journals An Intelligent Approach to Allocating Resources within an Agent-Based Cloud Computing Platform

2020 ◽  
Vol 10 (12) ◽  
pp. 4361
Author(s):  
Fernando De la Prieta ◽  
Sara Rodríguez-González ◽  
Pablo Chamoso ◽  
Yves Demazeau ◽  
Juan Manuel Corchado
Keyword(s):  
Cloud Computing ◽  
New Technologies ◽  
Cloud Services ◽  
Case Based Reasoning ◽  
Computing Platform ◽  
Reasoning System ◽  
Computing Paradigm ◽  
Cloud Computing Platform ◽  
Intelligent Approach ◽  
Computational Resources

The cloud computing paradigm has the ability to adapt to new technologies and provide consistent cloud services. These features have led to the widespread use of the paradigm, making it necessary for the underlying computer infrastructure to cope with the increased demand and the high number of end users. Platforms often use classical mathematical models for this purpose, helping assign computational resources to the services provided to the final user. Although this kind of model is valid and widespread, it can be refined through intelligent techniques. Therefore, this research presents a novel system consisting of a multi-agent system, which integrates a case-based reasoning system. The resulting system dynamically allocates resources within a cloud computing platform. This approach, which is distributed and scalable, can learn from previous experiences and produce better results in each resource allocation. A model of the system has been implemented and tested on a real cloud platform with successful results.

scholarly journals Feedback inhibition and its control in an insect olfactory circuit

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Subhasis Ray ◽  
Zane N Aldworth ◽  
Mark A Stopfer
Keyword(s):  
Mushroom Body ◽  
Large Scale ◽  
Feedback Inhibition ◽  
Temporal Patterns ◽  
Inhibitory Neurons ◽  
Biophysical Model ◽  
Olfactory Pathway ◽  
Olfactory Coding ◽  
Olfactory Responses ◽  
Electrophysiological Recordings

Inhibitory neurons play critical roles in regulating and shaping olfactory responses in vertebrates and invertebrates. In insects, these roles are performed by relatively few neurons, which can be interrogated efficiently, revealing fundamental principles of olfactory coding. Here, with electrophysiological recordings from the locust and a large-scale biophysical model, we analyzed the properties and functions of GGN, a unique giant GABAergic neuron that plays a central role in structuring olfactory codes in the locust mushroom body. Our simulations suggest that depolarizing GGN at its input branch can globally inhibit KCs several hundred microns away. Our in vivorecordings show that GGN responds to odors with complex temporal patterns of depolarization and hyperpolarization that can vary with odors and across animals, leading our model to predict the existence of a yet-undiscovered olfactory pathway. Our analysis reveals basic new features of GGN and the olfactory network surrounding it.

Understanding Decentralized and Dynamic Brokerage in Federated Cloud Environments

2012 ◽  
pp. 36-56 ◽  
Author(s):  
Nicolò Maria Calcavecchia ◽  
Antonio Celesti ◽  
Elisabetta Di Nitto
Keyword(s):  
Cloud Computing ◽  
Alternative Scenario ◽  
Advantages And Disadvantages ◽  
Computing Paradigm ◽  
Business Opportunities ◽  
Cloud Federation ◽  
Cloud Providers ◽  
Cloud Environments ◽  
Computational Resources ◽  
And Storage

The advent of the cloud computing paradigm offers different ways both to sell services and to exploit external computational resources according to a pay-per-use economic model. Nowadays, cloud computing clients can buy various form of IaaS, PaaS, and SaaS from cloud providers. Besides this form of pay-per-use, the perspective of cloud federation offers further business opportunities for small/medium providers which hold physical datacenters. Considering the cloud computing ecosystem, besides large cloud providers, smaller ones are also becoming popular even though their own virtualization infrastructures (i.e., deployed in their datacenters) cannot directly compete with the bigger market leaders. The result is that often small/medium clouds have to exploit the services of mega-providers in order to develop their business logic and their cloud-based services. To this regard, a possible future alternative scenario is represented by the promotion of cooperation among small/medium cloud providers, thus enabling the sharing of computational and storage resources. However, in order to achieve such an environment, several issues have to be addressed. One of these challenges is how to plan brokerage strategies allowing cloud providers to discover other providers for partnership establishment. This chapter focuses on different possible centralized and decentralized approaches in designing a brokerage strategy for cloud federation, analyzing their features, advantages and disadvantages.

scholarly journals PB: A Product-Bitmatrix Construction to Reduce the Complexity of XOR Operations of PM-MSR and PM-MBR Codes over GF 2 w

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Chuqiao Xiao ◽  
Xueqing Gong ◽  
Yefeng Xia ◽  
Qian Zhang
Keyword(s):  
Data Storage ◽  
Finite Domain ◽  
Privacy And Security ◽  
Theoretical Derivation ◽  
Erasure Code ◽  
Rs Codes ◽  
Network Bandwidth ◽  
Computing Paradigm ◽  
Bandwidth Overhead ◽  
Computational Resources

Edge computing, as an emerging computing paradigm, aims to reduce network bandwidth transmission overhead while storing and processing data on edge nodes. However, the storage strategies required for edge nodes are different from those for existing data centers. Erasure code (EC) strategies have been applied in some decentralized storage systems to ensure the privacy and security of data storage. Product-matrix (PM) regenerating codes (RGCs) as a state-of-the-art EC family are designed to minimize the repair bandwidth overhead or minimize the storage overhead. Nevertheless, the high complexity of the PM framework contains more finite-domain multiplication operations than classical ECs, which heavily consumes computational resources at the edge nodes. In this paper, a theoretical derivation of each step of the PM minimum storage regeneration (PM-MSR) and PM minimum bandwidth regeneration (PM-MBR) codes is performed and the XOR complexity over finite fields is analyzed. On this basis, a new construct called product bitmatrix (PB) is designed to reduce the complexity of XOR operations in the PM framework, and two heuristics are used to further reduce the XOR numbers of the PB-MSR and PB-MBR codes, respectively. The evaluation results show that the PB construction significantly reduces the XOR number compared to the PM-MSR, PM-MBR, Reed–Solomon (RS), and Cauchy RS codes while retaining optimal performance and reliability.

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