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.

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.

Stable Competitive Dynamics Emerge from Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity

2006 ◽  
Vol 18 (10) ◽  
pp. 2414-2464 ◽  
Author(s):  
Peter A. Appleby ◽  
Terry Elliott
Keyword(s):  
Synaptic Plasticity ◽  
Stochastic Model ◽  
Higher Order ◽  
Spike Timing ◽  
Competitive Dynamics ◽  
Spike Timing Dependent Plasticity ◽  
Interaction Function ◽  
Dependent Plasticity ◽  
Order Interaction ◽  
Synaptic Dynamics

In earlier work we presented a stochastic model of spike-timing-dependent plasticity (STDP) in which STDP emerges only at the level of temporal or spatial synaptic ensembles. We derived the two-spike interaction function from this model and showed that it exhibits an STDP-like form. Here, we extend this work by examining the general n-spike interaction functions that may be derived from the model. A comparison between the two-spike interaction function and the higher-order interaction functions reveals profound differences. In particular, we show that the two-spike interaction function cannot support stable, competitive synaptic plasticity, such as that seen during neuronal development, without including modifications designed specifically to stabilize its behavior. In contrast, we show that all the higher-order interaction functions exhibit a fixed-point structure consistent with the presence of competitive synaptic dynamics. This difference originates in the unification of our proposed “switch” mechanism for synaptic plasticity, coupling synaptic depression and synaptic potentiation processes together. While three or more spikes are required to probe this coupling, two spikes can never do so. We conclude that this coupling is critical to the presence of competitive dynamics and that multispike interactions are therefore vital to understanding synaptic competition.

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.

scholarly journals Burst Synchronization in A Scale-Free Neuronal Network with Inhibitory Spike-Timing-Dependent Plasticity

2018 ◽  
Author(s):  
Sang-Yoon Kim ◽  
Woochang Lim
Keyword(s):  
Synaptic Plasticity ◽  
Network Architecture ◽  
Neuronal Population ◽  
Matthew Effect ◽  
Spike Timing ◽  
Synaptic Inhibition ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Scale Free ◽  
Burst Synchronization

We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barabasi-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without considering iSTDP, BS was found to appear in a range of noise intensities for fixed synaptic inhibition strengths. In contrast, in our present work, we take into consideration iSTDP and investigate its effect on BS by varying the noise intensity. Our new main result is to find occurrence of a Matthew effect in inhibitory synaptic plasticity: good BS gets better via LTD, while bad BS get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). We note that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic plasticity are reversed in comparison with those in excitatory synaptic plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the preand the post-synaptic burst onset times. Finally, in the presence of iSTDP we investigate the effects of network architecture on BS by varying the symmetric attachment degree l* and the asymmetry parameter Δl in the SFN.

scholarly journals Long-term population spike-timing-dependent plasticity promotes synaptic tagging but not cross-tagging in rat hippocampal area CA1

2019 ◽  
Vol 116 (12) ◽  
pp. 5737-5746 ◽  
Author(s):  
Karen Ka Lam Pang ◽  
Mahima Sharma ◽  
Kumar Krishna-K. ◽  
Thomas Behnisch ◽  
Sreedharan Sajikumar
Keyword(s):  
Synaptic Plasticity ◽  
Long Term Potentiation ◽  
Neuronal Population ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Adult Rat ◽  
Term Potentiation ◽  
Hippocampal Ca3

In spike-timing-dependent plasticity (STDP), the direction and degree of synaptic modification are determined by the coherence of pre- and postsynaptic activities within a neuron. However, in the adult rat hippocampus, it remains unclear whether STDP-like mechanisms in a neuronal population induce synaptic potentiation of a long duration. Thus, we asked whether the magnitude and maintenance of synaptic plasticity in a population of CA1 neurons differ as a function of the temporal order and interval between pre- and postsynaptic activities. Modulation of the relative timing of Schaffer collateral fibers (presynaptic component) and CA1 axons (postsynaptic component) stimulations resulted in an asymmetric population STDP (pSTDP). The resulting potentiation in response to 20 pairings at 1 Hz was largest in magnitude and most persistent (4 h) when presynaptic activity coincided with or preceded postsynaptic activity. Interestingly, when postsynaptic activation preceded presynaptic stimulation by 20 ms, an immediate increase in field excitatory postsynaptic potentials was observed, but it eventually transformed into a synaptic depression. Furthermore, pSTDP engaged in selective forms of late-associative activity: It facilitated the maintenance of tetanization-induced early long-term potentiation (LTP) in neighboring synapses but not early long-term depression, reflecting possible mechanistic differences with classical tetanization-induced LTP. The data demonstrate that a pairing of pre- and postsynaptic activities in a neuronal population can greatly reduce the required number of synaptic plasticity-evoking events and induce a potentiation of a degree and duration similar to that with repeated tetanization. Thus, pSTDP determines synaptic efficacy in the hippocampal CA3–CA1 circuit and could bias the CA1 neuronal population toward potentiation in future events.

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.

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