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.

scholarly journals Quantized Weight Transfer Method Using Spike-Timing-Dependent Plasticity for Hardware Spiking Neural Network

2021 ◽  
Vol 11 (5) ◽  
pp. 2059
Author(s):  
Sungmin Hwang ◽  
Hyungjin Kim ◽  
Byung-Gook Park
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Spike Timing ◽  
Spiking Neural Network ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Transfer Method ◽  
Artificial Neural Network Ann ◽  
Online Learning Algorithm ◽  
Weight Transfer

A hardware-based spiking neural network (SNN) has attracted many researcher’s attention due to its energy-efficiency. When implementing the hardware-based SNN, offline training is most commonly used by which trained weights by a software-based artificial neural network (ANN) are transferred to synaptic devices. However, it is time-consuming to map all the synaptic weights as the scale of the neural network increases. In this paper, we propose a method for quantized weight transfer using spike-timing-dependent plasticity (STDP) for hardware-based SNN. STDP is an online learning algorithm for SNN, but we utilize it as the weight transfer method. Firstly, we train SNN using the Modified National Institute of Standards and Technology (MNIST) dataset and perform weight quantization. Next, the quantized weights are mapped to the synaptic devices using STDP, by which all the synaptic weights connected to a neuron are transferred simultaneously, reducing the number of pulse steps. The performance of the proposed method is confirmed, and it is demonstrated that there is little reduction in the accuracy at more than a certain level of quantization, but the number of pulse steps for weight transfer substantially decreased. In addition, the effect of the device variation is verified.

Inhibition of the slow afterhyperpolarization restores the classical spike timing-dependent plasticity rule obeyed in layer 2/3 pyramidal cells of the prefrontal cortex

2012 ◽  
Vol 107 (1) ◽  
pp. 205-215 ◽  
Author(s):  
Aleksey V. Zaitsev ◽  
Roger Anwyl
Keyword(s):  
Prefrontal Cortex ◽  
Pyramidal Cells ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Layer 2 ◽  
Stdp Rule ◽  
Stimulation Of

The induction of long-term potentiation (LTP) and long-term depression (LTD) of excitatory postsynaptic currents was investigated in proximal synapses of layer 2/3 pyramidal cells of the rat medial prefrontal cortex. The spike timing-dependent plasticity (STDP) induction protocol of negative timing, with postsynaptic leading presynaptic stimulation of action potentials (APs), induced LTD as expected from the classical STDP rule. However, the positive STDP protocol of presynaptic leading postsynaptic stimulation of APs predominantly induced a presynaptically expressed LTD rather than the expected postsynaptically expressed LTP. Thus the induction of plasticity in layer 2/3 pyramidal cells does not obey the classical STDP rule for positive timing. This unusual STDP switched to a classical timing rule if the slow Ca2+-dependent, K+-mediated afterhyperpolarization (sAHP) was inhibited by the selective blocker N-trityl-3-pyridinemethanamine (UCL2077), by the β-adrenergic receptor agonist isoproterenol, or by the cholinergic agonist carbachol. Thus we demonstrate that neuromodulators can affect synaptic plasticity by inhibition of the sAHP. These findings shed light on a fundamental question in the field of memory research regarding how environmental and behavioral stimuli influence LTP, thereby contributing to the modulation of memory.

scholarly journals Altered spike timing-dependent plasticity rules in physiological calcium

2020 ◽  
Author(s):  
Yanis Inglebert ◽  
Johnatan Aljadeff ◽  
Nicolas Brunel ◽  
Dominique Debanne
Keyword(s):  
Specific Activity ◽  
Spike Timing ◽  
Plasticity Model ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Hippocampal Synapses ◽  
Stdp Rule ◽  
Intracellular Ca

AbstractLike many forms of long-term synaptic plasticity, spike-timing-dependent plasticity (STDP) depends on intracellular Ca2+ signaling for its induction. Yet, all in vitro studies devoted to STDP used abnormally high external Ca2+ concentration. We measured STDP at the CA3-CA1 hippocampal synapses under different extracellular Ca2+ concentrations and found that the sign, shape and magnitude of plasticity strongly depend on Ca2+. A pre-post protocol that results in robust LTP in high Ca2+, yielded only LTD or no plasticity in the physiological Ca2+ range. LTP could be restored by either increasing the number of post-synaptic spikes or increasing the pairing frequency. A calcium-based plasticity model in which depression and potentiation depend on post-synaptic Ca2+ transients was found to fit quantitatively all the data, provided NMDA receptor-mediated non-linearities were implemented. In conclusion, STDP rule is profoundly altered in physiological Ca2+ but specific activity regimes restore a classical STDP profile.

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