Segmentation and Edge Detection Based on Spiking Neural Network Model

Meftah, Boudjelal; Lézoray, Olivier; Benyettou, Abdelkader

doi:10.1007/s11063-010-9149-6

Cited by 63 publications

(33 citation statements)

References 30 publications

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“…This precise temporal pattern in spiking activity is considered as a crucial coding strategy in sensory information processing areas [20], [21], [22], [23], [24] and neural motor control areas in the brain [25], [26]. SNNs have become the focus of a number of recent applications in many areas of pattern recognition such as visual processing [27], [28], [29], [30], speech recognition [31], [32], [33], [34], [35], [36], [37], and medical diagnosis [38], [39]. In recent years, a new generation of neural networks that incorporates the multilayer structure of DNNs (and the brain) and the type of information communication in SNNs has emerged.…”

Section: Introductionmentioning

confidence: 99%

Deep learning in spiking neural networks

et al. 2019

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1 In recent years, deep learning has revolutionized the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using backpropagation. Vast amounts of labeled training examples are required, but the resulting classification accuracy is truly impressive, sometimes outperforming humans. Neurons in an ANN are characterized by a single, static, continuous-valued activation. Yet biological neurons use discrete spikes to compute and transmit information, and the spike times, in addition to the spike rates, matter. Spiking neural networks (SNNs) are thus more biologically realistic than ANNs, and arguably the only viable option if one wants to understand how the brain computes. SNNs are also more hardware friendly and energy-efficient than ANNs, and are thus appealing for technology, especially for portable devices. However, training deep SNNs remains a challenge. Spiking neurons' transfer function is usually non-differentiable, which prevents using backpropagation.Here we review recent supervised and unsupervised methods to train deep SNNs, and compare them in terms of accuracy, but also computational cost and hardware friendliness. The emerging picture is that SNNs still lag behind ANNs in terms of accuracy, but the gap is decreasing, and can even vanish on some tasks, while SNNs typically require many fewer operations.

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Section: Introductionmentioning

confidence: 99%

Deep learning in spiking neural networks

et al. 2019

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“…The mammalian brain functionality is based on the specialized signal processing capabilities of massive neurons [1]. One key outcome from neuroscience research is a computing neural model of spiking neural networks (SNN) [1][2][3], which has the capability to emulate information processing of massive neurons in the brain. The neurons in the SNN exchange information via transmitting the spikes through the synapses [4].…”

Section: Introductionmentioning

confidence: 99%

An Efficient, Low-Cost Routing Architecture for Spiking Neural Network Hardware Implementations

Luo

Wan

Liu

et al. 2018

Neural Process Lett

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The basic processing units in brain are neurons and synapses that are interconnected in a complex pattern and show many surprised information processing capabilities. The researchers attempt to mimic this efficiency and build artificial neural systems in hardware device to emulate the key information processing principles of the brain. However, the neural network hardware system has a challenge of interconnecting neurons and synapses efficiently. An efficient, low-cost routing architecture (ELRA) is proposed in this paper to provide a communication infrastructure for the hardware spiking neuron networks (SNN). A dynamic traffic arbitration strategy is employed in ELRA, where the traffic status weights of input ports are calculated in real-time according to the channel traffic statuses and the port with the largest traffic status weight is given a high priority to forward packets. This strategy enables the router to serve congested ports preferentially, which can balance the overall network traffic loads. Experimental results show the feasibility of ELRA under various traffic scenarios, and the hardware synthesis result using SAED 90nm technology demonstrates it has a low hardware area overhead which maintains scalability for large-scale SNN hardware implementations. Recently, researchers proposed the networks-on-chip (NoC) interconnect paradigm [1,[4][5][6] as a promising solution to solve the inter-neuron connectivity problems and achieved a satisfactory performance [1,4]. The NoC is similar to the computer network where the processing elements (e.g. neurons in the SNN) are connected by the routers and channels [6]. For the SNN, the spikes are packetized and can be forwarded from any source node to any destination node; thus the information exchange between the neurons is established. In general, a group of neurons (e.g.∼ten in the approach of Carrillo et al. [4]) are connected to one router, but the required routers and channels increase if the number of neurons increases, which leads to the hardware area and power dissipation consumption. Thus the NoC architecture (i.e. routers and channels) constraints the system scalability [1], and it should achieve a trade-off between performance (e.g. spike throughput and communication delay etc.) and resource consumption (e.g. hardware area and power consumption). Performance: the NoC routers are responsible for transmitting highly irregular spike events. An effective router should forward as many spike events as possible in a short time period for various traffic scenarios, i.e. to provide high throughput for the communications. In addition, the irregular spike patterns occasionally introduce traffic congestions [4], which require the router have the ability to monitor the different traffic status (e.g. busy or congested etc.) and deal with various traffic patterns [5]. Resource consumption: the number of required routers increases proportionally with the number of neurons and synapses [1]. A low-cost routing architecture is very crucial for large-scale SNN hardware sys...

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“…In this line, for example, plenty of work has been done on synaptic plasticity in spiking neural networks, since modifications of the synaptic connections are traditionally considered the physiological basis of learning in the nervous system. These works are mostly related to unsupervised synaptic learning methods, such as Spike-Timing Dependent Plasticity (STDP) (Song et al, 2000; Bohte et al, 2002b; Kube et al, 2008; Meftah et al, 2010), with an increasing interest into supervised synaptic learning (Bohte et al, 2002a; Belatreche et al, 2007; Yu et al, 2013). The combination of learning rules including not only the modification of the synaptic weights, but also the parameters that affect the local discrimination of input signals can greatly contribute to enhance the spiking ANNs' computational power.…”

Section: Discussionmentioning

confidence: 99%

Implementing Signature Neural Networks with Spiking Neurons

Carrillo-Medina

Latorre

2016

Front. Comput. Neurosci.

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Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. In view of these results, we argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks.

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Segmentation and Edge Detection Based on Spiking Neural Network Model

Cited by 63 publications

References 30 publications

Deep learning in spiking neural networks

Deep learning in spiking neural networks

An Efficient, Low-Cost Routing Architecture for Spiking Neural Network Hardware Implementations

Implementing Signature Neural Networks with Spiking Neurons

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