A Stochastic Spiking Neural Network for Virtual Screening

Morro, Antoni; Canals, Vincent; Oliver, Antoni; Alomar, Miquel L.; Galán-Prado, Fabio; Ballester, Pedro J.; Rosselló, Josep L.

doi:10.1109/tnnls.2017.2657601

Cited by 43 publications

(13 citation statements)

References 21 publications

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“…21 SNNs have a wide application potential. Modern interesting applications of SNN include, e.g., epilepsy examination, [22][23][24] medical diagnostics, 25 pattern recognition, 26,27 neurosurgery, 28 information processing, 29 and liquid-state machine circuitry. 30 SN P systems have some common features with SNNs: a neuron fires only when its potential or the number of spikes inside it reaches a specific value; the concept of time is incorporated into the information encoding and processing.…”

Section: Introductionmentioning

confidence: 99%

Spiking Neural P Systems with Communication on Request

Pan

Păun

Zhang

et al. 2017

Int. J. Neur. Syst.

172

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Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

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Section: Introductionmentioning

confidence: 99%

Spiking Neural P Systems with Communication on Request

Pan

Păun

Zhang

et al. 2017

Int. J. Neur. Syst.

172

View full text Add to dashboard Cite

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“…Moreover, the multi-threaded design of the webserver and alignment-free nature of USR method also contributed to such a high computational efficiency. A hardware implementation of USR has been shown to achieve two-fold speed gains over standard CPU based implementation of USR (Morro et al, 2018 ). In this implementation, a computing technique, Spiking Neural Networks, has been adapted utilizing Field-Programmable Gate arrays to allow highly parallelized implementation of USR.…”

Section: D Shape Similarity Methodsmentioning

confidence: 99%

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

2018

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Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.

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“…(c) Another three dimensional implementation with a layer dedicated to memory and another to the computing elements. Crossbar arrays and three-dimensional circuits are not the only trail of hardware improvement. For example, Morro et al [128] have reported replacing a part of their ASIC, initially designed with traditional digital gates, by neuromorphic hardware. This sort of mixed neuromorphic integration is probably relevant for other applications.…”

Section: Hardware Implementationsmentioning

confidence: 99%

Spiking Neural Networks Hardware Implementations and Challenges

Bouvier

Valentian

Mesquida

et al. 2019

J. Emerg. Technol. Comput. Syst.

201

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Neuromorphic computing is henceforth a major research field for both academic and industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at bringing closer the memory and the computational elements to efficiently evaluate machine-learning algorithms. Recently, Spiking Neural Networks, a generation of cognitive algorithms employing computational primitives mimicking neuron and synapse operational principles, have become an important part of deep learning. They are expected to improve the computational performance and efficiency of neural networks, but are best suited for hardware able to support their temporal dynamics. In this survey, we present the state of the art of hardware implementations of spiking neural networks and the current trends in algorithm elaboration from model selection to training mechanisms. The scope of existing solutions is extensive; we thus present the general framework and study on a case-by-case basis the relevant particularities. We describe the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level and discuss their related advantages and challenges.

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A Stochastic Spiking Neural Network for Virtual Screening

Cited by 43 publications

References 21 publications

Spiking Neural P Systems with Communication on Request

Spiking Neural P Systems with Communication on Request

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Spiking Neural Networks Hardware Implementations and Challenges

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