Competitive STDP Learning of Overlapping Spatial Patterns

2015 ◽  
Vol 27 (8) ◽  
pp. 1673-1685 ◽  
Author(s):  
Dalius Krunglevicius
Keyword(s):  
Neural Network ◽  
Euclidean Distance ◽  
Hebbian Learning ◽  
Spike Timing ◽  
Spiking Neural Network ◽  
Size Dependent ◽  
Learning Rules ◽  
Pattern Size ◽  
Dependent Parameter ◽  
Simple Neural Network

Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules firmly based on biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning spatiotemporal patterns. When multiple neurons are organized in a simple competitive spiking neural network, this network is capable of learning multiple distinct patterns. If patterns overlap significantly (i.e., patterns are mutually inclusive), however, competition would not preclude trained neuron’s responding to a new pattern and adjusting synaptic weights accordingly. This letter presents a simple neural network that combines vertical inhibition and Euclidean distance-dependent synaptic strength factor. This approach helps to solve the problem of pattern size-dependent parameter optimality and significantly reduces the probability of a neuron’s forgetting an already learned pattern. For demonstration purposes, the network was trained for the first ten letters of the Braille alphabet.

scholarly journals A Heterogeneous Spiking Neural Network for Unsupervised Learning of Spatiotemporal Patterns

2021 ◽  
Vol 14 ◽  
Author(s):  
Xueyuan She ◽  
Saurabh Dash ◽  
Daehyun Kim ◽  
Saibal Mukhopadhyay
Keyword(s):  
Neural Network ◽  
Moving Objects ◽  
Short Term Memory ◽  
Back Propagation ◽  
Spike Timing ◽  
Spatiotemporal Patterns ◽  
Spiking Neural Network ◽  
Spatial And Temporal Patterns ◽  
Unsupervised Training ◽  
Improved Performance

This paper introduces a heterogeneous spiking neural network (H-SNN) as a novel, feedforward SNN structure capable of learning complex spatiotemporal patterns with spike-timing-dependent plasticity (STDP) based unsupervised training. Within H-SNN, hierarchical spatial and temporal patterns are constructed with convolution connections and memory pathways containing spiking neurons with different dynamics. We demonstrate analytically the formation of long and short term memory in H-SNN and distinct response functions of memory pathways. In simulation, the network is tested on visual input of moving objects to simultaneously predict for object class and motion dynamics. Results show that H-SNN achieves prediction accuracy on similar or higher level than supervised deep neural networks (DNN). Compared to SNN trained with back-propagation, H-SNN effectively utilizes STDP to learn spatiotemporal patterns that have better generalizability to unknown motion and/or object classes encountered during inference. In addition, the improved performance is achieved with 6x fewer parameters than complex DNNs, showing H-SNN as an efficient approach for applications with constrained computation resources.

STDP Provides the Substrate for Igniting Synfire Chains by Spatiotemporal Input Patterns

2008 ◽  
Vol 20 (2) ◽  
pp. 415-435 ◽  
Author(s):  
Ryosuke Hosaka ◽  
Osamu Araki ◽  
Tohru Ikeguchi
Keyword(s):  
Neural Network ◽  
Experimental Studies ◽  
Spike Timing ◽  
Inhibitory Neurons ◽  
Spatiotemporal Pattern ◽  
Spiking Neural Network ◽  
Synfire Chains ◽  
The Neural Network ◽  
Stdp Rule ◽  
Neural Network Structure

Spike-timing-dependent synaptic plasticity (STDP), which depends on the temporal difference between pre- and postsynaptic action potentials, is observed in the cortices and hippocampus. Although several theoretical and experimental studies have revealed its fundamental aspects, its functional role remains unclear. To examine how an input spatiotemporal spike pattern is altered by STDP, we observed the output spike patterns of a spiking neural network model with an asymmetrical STDP rule when the input spatiotemporal pattern is repeatedly applied. The spiking neural network comprises excitatory and inhibitory neurons that exhibit local interactions. Numerical experiments show that the spiking neural network generates a single global synchrony whose relative timing depends on the input spatiotemporal pattern and the neural network structure. This result implies that the spiking neural network learns the transformation from spatiotemporal to temporal information. In the literature, the origin of the synfire chain has not been sufficiently focused on. Our results indicate that spiking neural networks with STDP can ignite synfire chains in the cortices.

scholarly journals Reconciling the STDP and BCM Models of Synaptic Plasticity in a Spiking Recurrent Neural Network

2010 ◽  
Vol 22 (8) ◽  
pp. 2059-2085 ◽  
Author(s):  
Daniel Bush ◽  
Andrew Philippides ◽  
Phil Husbands ◽  
Michael O'Shea
Keyword(s):  
Neural Network ◽  
Synaptic Plasticity ◽  
Recurrent Neural Network ◽  
Hebbian Learning ◽  
Spike Timing ◽  
Synaptic Competition ◽  
Firing Rates ◽  
Plasticity Rule ◽  
Asymmetric Learning

Rate-coded Hebbian learning, as characterized by the BCM formulation, is an established computational model of synaptic plasticity. Recently it has been demonstrated that changes in the strength of synapses in vivo can also depend explicitly on the relative timing of pre- and postsynaptic firing. Computational modeling of this spike-timing-dependent plasticity (STDP) has demonstrated that it can provide inherent stability or competition based on local synaptic variables. However, it has also been demonstrated that these properties rely on synaptic weights being either depressed or unchanged by an increase in mean stochastic firing rates, which directly contradicts empirical data. Several analytical studies have addressed this apparent dichotomy and identified conditions under which distinct and disparate STDP rules can be reconciled with rate-coded Hebbian learning. The aim of this research is to verify, unify, and expand on these previous findings by manipulating each element of a standard computational STDP model in turn. This allows us to identify the conditions under which this plasticity rule can replicate experimental data obtained using both rate and temporal stimulation protocols in a spiking recurrent neural network. Our results describe how the relative scale of mean synaptic weights and their dependence on stochastic pre- or postsynaptic firing rates can be manipulated by adjusting the exact profile of the asymmetric learning window and temporal restrictions on spike pair interactions respectively. These findings imply that previously disparate models of rate-coded autoassociative learning and temporally coded heteroassociative learning, mediated by symmetric and asymmetric connections respectively, can be implemented in a single network using a single plasticity rule. However, we also demonstrate that forms of STDP that can be reconciled with rate-coded Hebbian learning do not generate inherent synaptic competition, and thus some additional mechanism is required to guarantee long-term input-output selectivity.

scholarly journals A Neural Network Model Can Explain Ventriloquism Aftereffect and Its Generalization across Sound Frequencies

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Elisa Magosso ◽  
Filippo Cona ◽  
Mauro Ursino
Keyword(s):  
Neural Network ◽  
Visual Stimulus ◽  
Hebbian Learning ◽  
Experimental Studies ◽  
Auditory Stimuli ◽  
Spectral Features ◽  
Auditory Neurons ◽  
Learning Rules ◽  
Perceptual Shift ◽  
Ventriloquism Aftereffect

Exposure to synchronous but spatially disparate auditory and visual stimuli produces a perceptual shift of sound location towards the visual stimulus (ventriloquism effect). After adaptation to a ventriloquism situation, enduring sound shift is observed in the absence of the visual stimulus (ventriloquism aftereffect). Experimental studies report opposing results as to aftereffect generalization across sound frequencies varying from aftereffect being confined to the frequency used during adaptation to aftereffect generalizing across some octaves. Here, we present an extension of a model of visual-auditory interaction we previously developed. The new model is able to simulate the ventriloquism effect and, via Hebbian learning rules, the ventriloquism aftereffect and can be used to investigate aftereffect generalization across frequencies. The model includes auditory neurons coding both for the spatial and spectral features of the auditory stimuli and mimicking properties of biological auditory neurons. The model suggests that different extent of aftereffect generalization across frequencies can be obtained by changing the intensity of the auditory stimulus that induces different amounts of activation in the auditory layer. The model provides a coherent theoretical framework to explain the apparently contradictory results found in the literature. Model mechanisms and hypotheses are discussed in relation to neurophysiological and psychophysical data.

scholarly journals Traces of semantization - from episodic to semantic memory in a spiking cortical network model

2021 ◽  
Author(s):  
Nikolaos Chrysanthidis ◽  
Florian Fiebig ◽  
Anders Lansner ◽  
Pawel Herman
Keyword(s):  
Neural Network ◽  
Episodic Memory ◽  
Semantic Memory ◽  
Network Model ◽  
Hebbian Learning ◽  
Contextual Information ◽  
Memory Task ◽  
Learning Rule ◽  
Mechanistic Explanation ◽  
Spike Timing

Episodic memory is the recollection of past personal experiences associated with particular times and places. This kind of memory is commonly subject to loss of contextual information or "semantization", which gradually decouples the encoded memory items from their associated contexts while transforming them into semantic or gist-like representations. Novel extensions to the classical Remember/Know behavioral paradigm attribute the loss of episodicity to multiple exposures of an item in different contexts. Despite recent advancements explaining semantization at a behavioral level, the underlying neural mechanisms remain poorly understood. In this study, we suggest and evaluate a novel hypothesis proposing that Bayesian-Hebbian synaptic plasticity mechanisms might cause semantization of episodic memory. We implement a cortical spiking neural network model with a Bayesian-Hebbian learning rule called Bayesian Confidence Propagation Neural Network (BCPNN), which captures the semantization phenomenon and offers a mechanistic explanation for it. Encoding items across multiple contexts leads to item-context decoupling akin to semantization. We compare BCPNN plasticity with the more commonly used spike-timing dependent plasticity (STDP) learning rule in the same episodic memory task. Unlike BCPNN, STDP does not explain the decontextualization process. We also examine how selective plasticity modulation of isolated salient events may enhance preferential retention and resistance to semantization. Our model reproduces important features of episodicity on behavioral timescales under various biological constraints whilst also offering a novel neural and synaptic explanation for semantization, thereby casting new light on the interplay between episodic and semantic memory processes.

scholarly journals Attractor dynamics in networks with learning rules inferred from in vivo data

2017 ◽  
Author(s):  
Ulises Pereira ◽  
Nicolas Brunel
Keyword(s):  
Neural Network ◽  
Hebbian Learning ◽  
Temporal Cortex ◽  
Memory Storage ◽  
Inferior Temporal Cortex ◽  
Association Cortex ◽  
Visual Responses ◽  
Neural Network Models ◽  
Firing Rates ◽  
Learning Rules

AbstractThe attractor neural network scenario is a popular scenario for memory storage in association cortex, but there is still a large gap between models based on this scenario and experimental data. We study a recurrent network model in which both learning rules and distribution of stored patterns are inferred from distributions of visual responses for novel and familiar images in inferior temporal cortex (ITC). Unlike classical attractor neural network models, our model exhibits graded activity in retrieval states, with distributions of firing rates that are close to lognormal. Inferred learning rules are close to maximizing the number of stored patterns within a family of unsupervised Hebbian learning rules, suggesting learning rules in ITC are optimized to store a large number of attractor states. Finally, we show that there exists two types of retrieval states: one in which firing rates are constant in time, another in which firing rates fluctuate chaotically.

scholarly journals An integrate-and-fire spiking neural network model simulating artificially induced cortical plasticity

2020 ◽  
Author(s):  
Larry Shupe ◽  
Eberhard E. Fetz
Keyword(s):  
Neural Network ◽  
Cortical Neurons ◽  
Closed Loop ◽  
Cortical Plasticity ◽  
Spike Timing ◽  
Emg Activity ◽  
Beta Activity ◽  
Spiking Neural Network ◽  
Integrate And Fire ◽  
Closed Loop Stimulation

AbstractWe describe an integrate-and-fire (IF) spiking neural network that incorporates spike-timing dependent plasticity (STDP) and simulates the experimental outcomes of four different conditioning protocols that produce cortical plasticity. The original conditioning experiments were performed in freely moving non-human primates with an autonomous head-fixed bidirectional brain-computer interface. Three protocols involved closed-loop stimulation triggered from (a) spike activity of single cortical neurons, (b) EMG activity from forearm muscles, and (c) cycles of spontaneous cortical beta activity. A fourth protocol involved open-loop delivery of pairs of stimuli at neighboring cortical sites. The IF network that replicates the experimental results consists of 360 units with simulated membrane potentials produced by synaptic inputs and triggering a spike when reaching threshold. The 240 cortical units produce either excitatory or inhibitory post-synaptic potentials in their target units. In addition to the experimentally observed conditioning effects, the model also allows computation of underlying network behavior not originally documented. Furthermore, the model makes predictions about outcomes from protocols not yet investigated, including spike-triggered inhibition, gamma-triggered stimulation and disynaptic conditioning. The success of the simulations suggests that a simple voltage-based IF model incorporating STDP can capture the essential mechanisms mediating targeted plasticity with closed-loop stimulation.

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