scholarly journals A Radial Basis Function Spike Model for Indirect Learning via Integrate-and-Fire Sampling and Reconstruction Techniques

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
X. Zhang ◽  
G. Foderaro ◽  
C. Henriquez ◽  
A. M. J. VanDongen ◽  
S. Ferrari
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Spike Trains ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Learning Mechanisms ◽  
Integrate And Fire ◽  
Spike Model ◽  
Radial Basis ◽  
Input Spike

This paper presents a deterministic and adaptive spike model derived from radial basis functions and a leaky integrate-and-fire sampler developed for training spiking neural networks without direct weight manipulation. Several algorithms have been proposed for training spiking neural networks through biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity and Hebbian plasticity. These algorithms typically rely on the ability to update the synaptic strengths, or weights, directly, through a weight update rule in which the weight increment can be decided and implemented based on the training equations. However, in several potential applications of adaptive spiking neural networks, including neuroprosthetic devices and CMOS/memristor nanoscale neuromorphic chips, the weights cannot be manipulated directly and, instead, tend to change over time by virtue of the pre- and postsynaptic neural activity. This paper presents an indirect learning method that induces changes in the synaptic weights by modulating spike-timing-dependent plasticity by means of controlled input spike trains. In place of the weights, the algorithm manipulates the input spike trains used to stimulate the input neurons by determining a sequence of spike timings that minimize a desired objective function and, indirectly, induce the desired synaptic plasticity in the network.

Spike-Timing Dependent Plasticity for Evolved Robots

2002 ◽  
Vol 10 (3-4) ◽  
pp. 243-263 ◽  
Author(s):  
Ezequiel Di Paolo
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Spike Trains ◽  
Spike Timing ◽  
Behavioral Strategies ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Neural Noise ◽  
Functional Value ◽  
Millisecond Range

Plastic spiking neural networks are synthesized for phototactic robots using evolutionary techniques. Synaptic plasticity asymmetrically depends on the precise relative timing between presynaptic and postsynaptic spikes at the millisecond range and on longer-term activity-dependent regulatory scaling. Comparative studies have been carried out for different kinds of plastic neural networks with low and high levels of neural noise. In all cases, the evolved controllers are highly robust against internal synaptic decay and other perturbations. The importance of the precise timing of spikes is demonstrated by randomizing the spike trains. In the low neural noise scenario, weight values undergo rhythmic changes at the mesoscale due to bursting, but during periods of high activity they are finely regulated at the microscale by synchronous or entrained firing. Spike train randomization results in loss of performance in this case. In contrast, in the high neural noise scenario, robots are robust to loss of information in the timing of the spike trains, demonstrating the counterintuitive results that plasticity, which is dependent on precise spike timing, can work even in its absence, provided the behavioral strategies make use of robust longer-term invariants of sensorimotor interaction. A comparison with a rate-based model of synaptic plasticity shows that under similarly noisy conditions, asymmetric spike-timing dependent plasticity achieves better performance by means of efficient reduction in weight variance over time. Performance also presents negative sensitivity to reduced levels of noise, showing that random firing has a functional value.

scholarly journals Does High Firing Irregularity Enhance Learning?

2011 ◽  
Vol 23 (3) ◽  
pp. 656-663 ◽  
Author(s):  
Chris Christodoulou ◽  
Aristodemos Cleanthous
Keyword(s):  
Neural Networks ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Benchmark Problem ◽  
Integrate And Fire ◽  
Eligibility Trace ◽  
Firing Irregularity ◽  
The Brain ◽  
High Firing ◽  
Integrate And Fire Neuron

In this note, we demonstrate that the high firing irregularity produced by the leaky integrate-and-fire neuron with the partial somatic reset mechanism, which has been shown to be the most likely candidate to reflect the mechanism used in the brain for reproducing the highly irregular cortical neuron firing at high rates (Bugmann, Christodoulou, & Taylor, 1997 ; Christodoulou & Bugmann, 2001 ), enhances learning. More specifically, it enhances reward-modulated spike-timing-dependent plasticity with eligibility trace when used in spiking neural networks, as shown by the results when tested in the simple benchmark problem of XOR, as well as in a complex multiagent setting task.

scholarly journals Biological modelling of a computational spiking neural network with neuronal avalanches

2017 ◽  
Vol 375 (2096) ◽  
pp. 20160286 ◽  
Author(s):  
Xiumin Li ◽  
Qing Chen ◽  
Fangzheng Xue
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Critical State ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Mathematical Methods ◽  
Computational Accuracy ◽  
Active Neuron ◽  
Computational Performance ◽  
Neuronal Avalanches

In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance. This article is part of the themed issue ‘Mathematical methods in medicine: neuroscience, cardiology and pathology’.

scholarly journals Event-Driven Simulations of Nonlinear Integrate-and-Fire Neurons

2007 ◽  
Vol 19 (12) ◽  
pp. 3226-3238 ◽  
Author(s):  
Arnaud Tonnelier ◽  
Hana Belmabrouk ◽  
Dominique Martinez
Keyword(s):  
Neural Networks ◽  
Spiking Neural Networks ◽  
Synaptic Currents ◽  
Fire Model ◽  
Integrate And Fire ◽  
Fire Models ◽  
Event Driven

Event-driven strategies have been used to simulate spiking neural networks exactly. Previous work is limited to linear integrate-and-fire neurons. In this note, we extend event-driven schemes to a class of nonlinear integrate-and-fire models. Results are presented for the quadratic integrate-and-fire model with instantaneous or exponential synaptic currents. Extensions to conductance-based currents and exponential integrate-and-fire neurons are discussed.

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 Biologically Plausible Sequence Learning with Spiking Neural Networks

2020 ◽  
Vol 34 (02) ◽  
pp. 1316-1323
Author(s):  
Zuozhu Liu ◽  
Thiparat Chotibut ◽  
Christopher Hillar ◽  
Shaowei Lin
Keyword(s):  
Neural Networks ◽  
Sequence Learning ◽  
Nervous Activity ◽  
Learning Rule ◽  
Generative Models ◽  
Spike Timing ◽  
Hopfield Network ◽  
Spiking Neural Networks ◽  
Theoretical Ground ◽  
Time Model

Motivated by the celebrated discrete-time model of nervous activity outlined by McCulloch and Pitts in 1943, we propose a novel continuous-time model, the McCulloch-Pitts network (MPN), for sequence learning in spiking neural networks. Our model has a local learning rule, such that the synaptic weight updates depend only on the information directly accessible by the synapse. By exploiting asymmetry in the connections between binary neurons, we show that MPN can be trained to robustly memorize multiple spatiotemporal patterns of binary vectors, generalizing the ability of the symmetric Hopfield network to memorize static spatial patterns. In addition, we demonstrate that the model can efficiently learn sequences of binary pictures as well as generative models for experimental neural spike-train data. Our learning rule is consistent with spike-timing-dependent plasticity (STDP), thus providing a theoretical ground for the systematic design of biologically inspired networks with large and robust long-range sequence storage capacity.

Neural Autopoiesis: Organizing Self-Boundaries by Stimulus Avoidance in Biological and Artificial Neural Networks

2020 ◽  
Vol 26 (1) ◽  
pp. 130-151 ◽  
Author(s):  
Atsushi Masumori ◽  
Lana Sinapayen ◽  
Norihiro Maruyama ◽  
Takeshi Mita ◽  
Douglas Bakkum ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Environmental Changes ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Cultured Neurons ◽  
Autonomous Regulation ◽  
Artificial Neural ◽  
Living Organisms ◽  
External Stimuli

Living organisms must actively maintain themselves in order to continue existing. Autopoiesis is a key concept in the study of living organisms, where the boundaries of the organism are not static but dynamically regulated by the system itself. To study the autonomous regulation of a self-boundary, we focus on neural homeodynamic responses to environmental changes using both biological and artificial neural networks. Previous studies showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) learn an action as they avoid stimulation from outside. In this article, as a result of our experiments using embodied cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: If the agent cannot learn an action to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes, as if to ignore the uncontrollable input. We also show such a behavior is reproduced by spiking neural networks with asymmetric STDP. We consider that these properties are to be regarded as autonomous regulation of self and nonself for the network, in which a controllable neuron is regarded as self, and an uncontrollable neuron is regarded as nonself. Finally, we introduce neural autopoiesis by proposing the principle of stimulus avoidance.

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