scholarly journals Memory Retention and Spike-Timing-Dependent Plasticity

2009 ◽  
Vol 101 (6) ◽  
pp. 2775-2788 ◽  
Author(s):  
Guy Billings ◽  
Mark C. W. van Rossum
Keyword(s):  
Retention Time ◽  
Single Neuron ◽  
Receptive Fields ◽  
Learning Rule ◽  
Spike Timing ◽  
Memory Retention ◽  
Neuron Models ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Dependent Learning

Memory systems should be plastic to allow for learning; however, they should also retain earlier memories. Here we explore how synaptic weights and memories are retained in models of single neurons and networks equipped with spike-timing-dependent plasticity. We show that for single neuron models, the precise learning rule has a strong effect on the memory retention time. In particular, a soft-bound, weight-dependent learning rule has a very short retention time as compared with a learning rule that is independent of the synaptic weights. Next, we explore how the retention time is reflected in receptive field stability in networks. As in the single neuron case, the weight-dependent learning rule yields less stable receptive fields than a weight-independent rule. However, receptive fields stabilize in the presence of sufficient lateral inhibition, demonstrating that plasticity in networks can be regulated by inhibition and suggesting a novel role for inhibition in neural circuits.

scholarly journals Beyond spike-timing-dependent plasticity: a computational study of plasticity gradients across basal dendrites

2016 ◽  
Author(s):  
Jacopo Bono ◽  
Claudia Clopath
Keyword(s):  
Synaptic Plasticity ◽  
Computational Study ◽  
Learning Rule ◽  
Spike Timing ◽  
Memory Retention ◽  
Neuron Models ◽  
Spike Timing Dependent Plasticity ◽  
Principal Mechanism ◽  
Dependent Plasticity ◽  
Dendritic Spikes

AbstractSynaptic plasticity is thought to be the principal mechanism underlying learning in the brain. Models of plastic networks typically combine point neurons with spike-timing-dependent plasticity (STDP) as the learning rule. However, a point neuron does not capture the complexity of dendrites, which allow non-linear local processing of the synaptic inputs. Furthermore, experimental evidence suggests that STDP is not the only learning rule available to neurons. Implementing biophysically realistic neuron models, we studied how dendrites allow for multiple synaptic plasticity mechanisms to coexist in a single cell. In these models, we compared the conditions for STDP and for the synaptic strengthening by local dendritic spikes. We further explored how the connectivity between two cells is affected by these plasticity rules and the synaptic distributions. Finally, we show how memory retention in associative learning can be prolonged in networks of neurons with dendrites.

Supervised Spike-Timing-Dependent Plasticity: A Spatiotemporal Neuronal Learning Rule for Function Approximation and Decisions

2013 ◽  
Vol 25 (12) ◽  
pp. 3113-3130 ◽  
Author(s):  
Jan-Moritz P. Franosch ◽  
Sebastian Urban ◽  
J. Leo van Hemmen
Keyword(s):  
Function Approximation ◽  
Sensory Input ◽  
Learning Rule ◽  
Animal Learn ◽  
Spike Timing ◽  
Time Dependent ◽  
Stable Configuration ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Tactile System

How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as “supervisor.” Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

Photonic implementation of spike timing dependent plasticity with weight-dependent learning window based on VCSOA

Laser Physics ◽  
2021 ◽  
Vol 32 (1) ◽  
pp. 016201
Author(s):  
Tao Tian ◽  
Zhengmao Wu ◽  
Xiaodong Lin ◽  
Xi Tang ◽  
Ziye Gao ◽  
...  
Keyword(s):  
Large Scale ◽  
Optical Amplifier ◽  
Bias Current ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Fabry Perot ◽  
The Stability ◽  
Dependent Learning ◽  
Vertical Cavity

Abstract Based on the well-known Fabry–Pérot approach, after taking into account the variation of bias current of the vertical-cavity semiconductor optical amplifier (VCSOA) according to the present synapse weight, we implement the optical spike timing dependent plasticity (STDP) with weight-dependent learning window in a VCSOA with double optical spike injections, and numerically investigate the corresponding weight-dependent STDP characteristics. The simulation results show that, the bias current of VCSOA has significant effect on the optical STDP curve. After introducing an adaptive variation of the bias current according to the present synapse weight, the optical weight-dependent STDP based on VCSOA can be realized. Moreover, the weight training based on the optical weight-dependent STDP can be effectively controlled by adjusting some typical external or intrinsic parameters and the excessive adjusting of synaptic weight is avoided, which can be used to balance the stability and competition among synapses and pave a way for the future large-scale energy efficient optical spiking neural networks based on the weight-dependent STDP learning mechanism.

scholarly journals Optical spike-timing-dependent plasticity with weight-dependent learning window and reward modulation

2015 ◽  
Vol 23 (19) ◽  
pp. 25247 ◽  
Author(s):  
Quansheng Ren ◽  
Yaolin Zhang ◽  
Rui Wang ◽  
Jianye Zhao
Keyword(s):  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Dependent Learning

Frequency Selectivity Emerging from Spike-Timing-Dependent Plasticity

2012 ◽  
Vol 24 (9) ◽  
pp. 2251-2279 ◽  
Author(s):  
Matthieu Gilson ◽  
Moritz Bürck ◽  
Anthony N. Burkitt ◽  
J. Leo van Hemmen
Keyword(s):  
Modulation Frequency ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Dendritic Location ◽  
Frequency Components ◽  
Frequency Identification ◽  
Specific Frequency ◽  
Dependent Learning ◽  
Dendritic Branches

Periodic neuronal activity has been observed in various areas of the brain, from lower sensory to higher cortical levels. Specific frequency components contained in this periodic activity can be identified by a neuronal circuit that behaves as a bandpass filter with given preferred frequency, or best modulation frequency (BMF). For BMFs typically ranging from 10 to 200 Hz, a plausible and minimal configuration consists of a single neuron with adjusted excitatory and inhibitory synaptic connections. The emergence, however, of such a neuronal circuitry is still unclear. In this letter, we demonstrate how spike-timing-dependent plasticity (STDP) can give rise to frequency-dependent learning, thus leading to an input selectivity that enables frequency identification. We use an in-depth mathematical analysis of the learning dynamics in a population of plastic inhibitory connections. These provide inhomogeneous postsynaptic responses that depend on their dendritic location. We find that synaptic delays play a crucial role in organizing the weight specialization induced by STDP. Under suitable conditions on the synaptic delays and postsynaptic potentials (PSPs), the BMF of a neuron after learning can match the training frequency. In particular, proximal (distal) synapses with shorter (longer) dendritic delay and somatically measured PSP time constants respond better to higher (lower) frequencies. As a result, the neuron will respond maximally to any stimulating frequency (in a given range) with which it has been trained in an unsupervised manner. The model predicts that synapses responding to a given BMF form clusters on dendritic branches.

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