scholarly journals Efficient VLSI Architecture for Odor Recognition with a Spiking Neural Network

2019 ◽  
Vol 9 (1S3) ◽  
pp. 6-9
Keyword(s):  
Neural Network ◽  
Synaptic Plasticity ◽  
Vlsi Architecture ◽  
Spike Timing ◽  
Time Difference ◽  
Spiking Neural Network ◽  
Cortical Cells ◽  
Odor Recognition ◽  
Stdp Rule

In this paper spiking neural network (SNN) is presented which can discriminate odor data. Spike timing dependent synaptic plasticity (STDP) means a plasticity which is controlled by the presynaptic and postsynaptic spikes time difference. Using this STDP rule the synaptic weights are modified after the mitral and before the cortical cells. In order to determine whether the circuit has correctly identified the odor the SNN has either a high or a low response at the output for any odor given as the input.

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 Learning predictive cognitive maps with spiking neurons during behaviour and replays

2021 ◽  
Author(s):  
Jacopo Bono ◽  
Sara Zannone ◽  
Victor Pedrosa ◽  
Claudia Clopath
Keyword(s):  
Neural Network ◽  
Synaptic Plasticity ◽  
Cognitive Maps ◽  
Spike Timing ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Firing Rates ◽  
Behavioural Activity ◽  
State Dependent ◽  
State Dependent Delay

AbstractWe describe a framework where a biologically plausible spiking neural network mimicking hippocampal layers learns a cognitive map known as the successor representation. We show analytically how, on the algorithmic level, the learning follows the TD(λ) algorithm, which emerges from the underlying spike-timing dependent plasticity rule. We then analyze the implications of this framework, uncovering how behavioural activity and experience replays can play complementary roles when learning the representation of the environment, how we can learn relations over behavioural timescales with synaptic plasticity acting on the range of milliseconds, and how the learned representation can be flexibly encoded by allowing state-dependent delay discounting through neuromodulation and altered firing rates.

scholarly journals A STDP Rule that Favours Chaotic Spiking over Regular Spiking of Neurons

2021 ◽  
Vol 12 (03) ◽  
pp. 25-33
Author(s):  
Mario Antoine Aoun
Keyword(s):  
Neural Network ◽  
Chaos Theory ◽  
Spike Timing ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Stdp Rule

We compare the number of states of a Spiking Neural Network (SNN) composed from chaotic spiking neurons versus the number of states of a SNN composed from regular spiking neurons while both SNNs implementing a Spike Timing Dependent Plasticity (STDP) rule that we created. We find out that this STDP rule favors chaotic spiking since the number of states is larger in the chaotic SNN than the regular SNN. This chaotic favorability is not general; it is exclusive to this STDP rule only. This research falls under our long-term investigation of STDP and chaos theory.

Neuromorphic Functional Modules of Spiking Neural Network

2021 ◽  
Vol 23 (6) ◽  
pp. 317-326
Author(s):  
E.A. Ryndin ◽  
◽  
N.V. Andreeva ◽  
V.V. Luchinin ◽  
K.S. Goncharov ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Synaptic Plasticity ◽  
Data Processing ◽  
Circuit Design ◽  
Spiking Neural Networks ◽  
Spiking Neural Network ◽  
Wide Range ◽  
Artificial Neural

In the current era, design and development of artificial neural networks exploiting the architecture of the human brain have evolved rapidly. Artificial neural networks effectively solve a wide range of common for artificial intelligence tasks involving data classification and recognition, prediction, forecasting and adaptive control of object behavior. Biologically inspired underlying principles of ANN operation have certain advantages over the conventional von Neumann architecture including unsupervised learning, architectural flexibility and adaptability to environmental change and high performance under significantly reduced power consumption due to heavy parallel and asynchronous data processing. In this paper, we present the circuit design of main functional blocks (neurons and synapses) intended for hardware implementation of a perceptron-based feedforward spiking neural network. As the third generation of artificial neural networks, spiking neural networks perform data processing utilizing spikes, which are discrete events (or functions) that take place at points in time. Neurons in spiking neural networks initiate precisely timing spikes and communicate with each other via spikes transmitted through synaptic connections or synapses with adaptable scalable weight. One of the prospective approach to emulate the synaptic behavior in hardware implemented spiking neural networks is to use non-volatile memory devices with analog conduction modulation (or memristive structures). Here we propose a circuit design for functional analogues of memristive structure to mimic a synaptic plasticity, pre- and postsynaptic neurons which could be used for developing circuit design of spiking neural network architectures with different training algorithms including spike-timing dependent plasticity learning rule. Two different circuits of electronic synapse were developed. The first one is an analog synapse with photoresistive optocoupler used to ensure the tunable conductivity for synaptic plasticity emulation. While the second one is a digital synapse, in which the synaptic weight is stored in a digital code with its direct conversion into conductivity (without digital-to-analog converter andphotoresistive optocoupler). The results of the prototyping of developed circuits for electronic analogues of synapses, pre- and postsynaptic neurons and the study of transient processes are presented. The developed approach could provide a basis for ASIC design of spiking neural networks based on CMOS (complementary metal oxide semiconductor) design technology.

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.

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 Exploring the limits of learning: segregation of information integration and response selection is required for learning a serial reversal task

2017 ◽  
Author(s):  
Camilo J. Mininni ◽  
B. Silvano Zanutto
Keyword(s):  
Neural Network ◽  
Synaptic Plasticity ◽  
Network Model ◽  
Neural Network Model ◽  
Response Selection ◽  
Neural Population ◽  
Spiking Neural Network ◽  
Reversal Task ◽  
Spiking Neural Network Model ◽  
Serial Reversal

AbstractAnimals are proposed to learn the latent rules governing their environment in order to maximize their chances of survival. However, rules may change without notice, forcing animals to keep a memory of which one is currently at work. Rule switching can lead to situations in which the same stimulus/response pairing is positively and negatively rewarded in the long run, depending on variables that are not accessible to the animal. This fact rises questions on how neural systems are capable of reinforcement learning in environments where the reinforcement is inconsistent. Here we address this issue by asking about which aspects of connectivity, neural excitability and synaptic plasticity are key for a very general, stochastic spiking neural network model to solve a task in which rules change without being cued, taking the serial reversal task (SRT) as paradigm. Contrary to what could be expected, we found strong limitations for biologically plausible networks to solve the SRT. Especially, we proved that no network of neurons can learn a SRT if it is a single neural population that integrates stimuli information and at the same time is responsible of choosing the behavioural response. This limitation is independent of the number of neurons, neuronal dynamics or plasticity rules, and arises from the fact that plasticity is locally computed at each synapse, and that synaptic changes and neuronal activity are mutually dependent processes. We propose and characterize a spiking neural network model that solves the SRT, which relies on separating the functions of stimuli integration and response selection. The model suggests that experimental efforts to understand neural function should focus on the characterization of neural circuits according to their connectivity, neural dynamics, and the degree of modulation of synaptic plasticity with reward.

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