STDP Learning of Image Patches with Convolutional Spiking Neural Networks

Saunders, Daniel J.; Siegelmann, Hava T.; Kozma, Róbert; Ruszinkao, Miklaos

doi:10.1109/ijcnn.2018.8489684

Cited by 39 publications

(37 citation statements)

References 15 publications

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“…This section summarizes the applied spiking neural network structure and learning algorithms using leaky integrate-and-fire (LIF) neurons and spike-timing-dependent plasticity (STDP), as initially outlined in [14], extended in [25,32], and supplemented in [33] with inter-neuron distance-dependent inhibition strength.…”

Section: Methodsmentioning

confidence: 99%

“…There is a great potential to extend the work of [14,24] and [25] by combining STDP rules and competitive inhibitory interactions with a mechanism inspired by the self-organizing map (SOM) algorithm [26] and the properties of the The adaptive resonance theory (ART) model [27]. SOMs are able to cluster an unlabeled dataset in an unsupervised manner.…”

Section: Self-organizing Properties With Spiking Neural Networkmentioning

confidence: 99%

“…It is an important property of the architecture that once a neuron fires, it inhibits all other neurons according to the inhibition scheme described above. For further details on the basic model structure, including considerations on system size, see [14,25]. The WTA-like mechanism described above is what allows individual neurons to learn unique filters.…”

Section: Snn Architecture and Operating Principlementioning

confidence: 99%

“…Here we summarize results obtained with the LM-SNN models using MNIST data; for additional details, see [25,33]. We omit results on the increasing and growing inhibition strategies in favor of results using the simpler two-level inhibition strategy.…”

Section: Classification Of Mnist Imagesmentioning

confidence: 99%

See 3 more Smart Citations

Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data

Hazan

Saunders

Sanghavi

et al. 2019

Ann Math Artif Intell

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Spiking neural networks (SNNs) with a lattice architecture are introduced in this work, combining several desirable properties of SNNs and self-organized maps (SOMs). Networks are trained with biologically motivated, unsupervised learning rules to obtain a self-organized grid of filters via cooperative and competitive excitatory-inhibitory interactions. Several inhibition strategies are developed and tested, such as (i) incrementally increasing inhibition level over the course of network training, and (ii) switching the inhibition level from low to high (two-level ) after an initial training segment. During the labeling phase, the spiking activity generated by data with known labels is used to assign neurons to categories of data, which are then used to evaluate the network's classification ability on a held-out set of test data. Several biologically plausible evaluation rules are proposed and compared, including a population-level confidence rating, and an n-gram inspired method. The effectiveness of the proposed selforganized learning mechanism is tested using the MNIST benchmark dataset, as well as using images produced by playing the Atari Breakout game.

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Section: Methodsmentioning

confidence: 99%

Section: Self-organizing Properties With Spiking Neural Networkmentioning

confidence: 99%

Section: Snn Architecture and Operating Principlementioning

confidence: 99%

Section: Classification Of Mnist Imagesmentioning

confidence: 99%

See 2 more Smart Citations

Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data

Hazan

Saunders

Sanghavi

et al. 2019

Ann Math Artif Intell

Self Cite

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“…BindsNET provides us with significant freedom in deciding implementation details, aiming at machine learning applications. BindsNET builds on the results of [22] and [23], wherein all networks were implemented in the BRIAN spiking neural networks simulator [24]. In addition to image classification tasks addressed here, BindsNET has been used implementing SNNs playing computer games such as Atari breakout, by transferring weights from Deep Q-Learning NNs trained by reinforcement learning [25].…”

Section: Introductionmentioning

confidence: 99%

Locally connected spiking neural networks for unsupervised feature learning

et al. 2019

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In recent years, Spiking Neural Networks (SNNs) have demonstrated great successes in completing various Machine Learning tasks. We introduce a method for learning image features by locally connected layers in SNNs using spike-timingdependent plasticity (STDP) rule. In our approach, sub-networks compete via competitive inhibitory interactions to learn features from different locations of the input space. These Locally-Connected SNNs (LC-SNNs) manifest key topological features of the spatial interaction of biological neurons. We explore biologically inspired ngram classification approach allowing parallel processing over various patches of the the image space. We report the classification accuracy of simple two-layer LC-SNNs on two image datasets, which match the state-of-art performance and are the first results to date. LC-SNNs have the advantage of fast convergence to a dataset representation, and they require fewer learnable parameters than other SNN approaches with unsupervised learning. Robustness tests demonstrate that LC-SNNs exhibit graceful degradation of performance despite the random deletion of large amounts of synapses and neurons.

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