scholarly journals A Biophysical Model of Synaptic Plasticity and Metaplasticity Can Account for the Dynamics of the Backward Shift of Hippocampal Place Fields

2008 ◽  
Vol 100 (2) ◽  
pp. 983-992 ◽  
Author(s):  
Xintian Yu ◽  
Harel Z. Shouval ◽  
James J. Knierim
Keyword(s):  
Synaptic Plasticity ◽  
Cortical Plasticity ◽  
Learning Rule ◽  
Spike Timing ◽  
Biophysical Model ◽  
Place Field ◽  
Dependent Plasticity ◽  
Backward Shift ◽  
Field Shift

Hippocampal place cells in the rat undergo experience-dependent changes when the rat runs stereotyped routes. One such change, the backward shift of the place field center of mass, has been linked by previous modeling efforts to spike-timing–dependent plasticity (STDP). However, these models did not account for the termination of the place field shift and they were based on an abstract implementation of STDP that ignores many of the features found in cortical plasticity. Here, instead of the abstract STDP model, we use a calcium-dependent plasticity (CaDP) learning rule that can account for many of the observed properties of cortical plasticity. We use the CaDP learning rule in combination with a model of metaplasticity to simulate place field dynamics. Without any major changes to the parameters of the original model, the present simulations account both for the initial rapid place field shift and for the subsequent slowing down of this shift. These results suggest that the CaDP model captures the essence of a general cortical mechanism of synaptic plasticity, which may underlie numerous forms of synaptic plasticity observed both in vivo and in vitro.

scholarly journals Synaptic plasticity rules with physiological calcium levels

2020 ◽  
Vol 117 (52) ◽  
pp. 33639-33648
Author(s):  
Yanis Inglebert ◽  
Johnatan Aljadeff ◽  
Nicolas Brunel ◽  
Dominique Debanne
Keyword(s):  
Synaptic Plasticity ◽  
Specific Activity ◽  
Spike Timing ◽  
In Vitro Experiments ◽  
Patch Clamp Recording ◽  
Dependent Plasticity ◽  
Theoretical Approaches ◽  
High Calcium ◽  
Stdp Rule

Spike-timing–dependent plasticity (STDP) is considered as a primary mechanism underlying formation of new memories during learning. Despite the growing interest in activity-dependent plasticity, it is still unclear whether synaptic plasticity rules inferred from in vitro experiments are correct in physiological conditions. The abnormally high calcium concentration used in in vitro studies of STDP suggests that in vivo plasticity rules may differ significantly from in vitro experiments, especially since STDP depends strongly on calcium for induction. We therefore studied here the influence of extracellular calcium on synaptic plasticity. Using a combination of experimental (patch-clamp recording and Ca2+ imaging at CA3-CA1 synapses) and theoretical approaches, we show here that the classic STDP rule in which pairs of single pre- and postsynaptic action potentials induce synaptic modifications is not valid in the physiological Ca2+ range. Rather, we found that these pairs of single stimuli are unable to induce any synaptic modification in 1.3 and 1.5 mM calcium and lead to depression in 1.8 mM. Plasticity can only be recovered when bursts of postsynaptic spikes are used, or when neurons fire at sufficiently high frequency. In conclusion, the STDP rule is profoundly altered in physiological Ca2+, but specific activity regimes restore a classical STDP profile.

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 Calcium and Spike Timing-Dependent Plasticity

2021 ◽  
Vol 15 ◽  
Author(s):  
Yanis Inglebert ◽  
Dominique Debanne
Keyword(s):  
Synaptic Plasticity ◽  
Calcium Concentration ◽  
Calcium Influx ◽  
Spike Timing ◽  
Extracellular Calcium ◽  
Dependent Plasticity ◽  
Extracellular Calcium Concentration ◽  
Activity Dependent

Since its discovery, spike timing-dependent synaptic plasticity (STDP) has been thought to be a primary mechanism underlying the brain’s ability to learn and to form new memories. However, despite the enormous interest in both the experimental and theoretical neuroscience communities in activity-dependent plasticity, it is still unclear whether plasticity rules inferred from in vitro experiments apply to in vivo conditions. Among the multiple reasons why plasticity rules in vivo might differ significantly from in vitro studies is that extracellular calcium concentration use in most studies is higher than concentrations estimated in vivo. STDP, like many forms of long-term synaptic plasticity, strongly depends on intracellular calcium influx for its induction. Here, we discuss the importance of considering physiological levels of extracellular calcium concentration to study functional plasticity.

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.

scholarly journals Dynamic modulation of spike timing-dependent calcium influx during corticostriatal upstates

2013 ◽  
Vol 110 (7) ◽  
pp. 1631-1645 ◽  
Author(s):  
R. C. Evans ◽  
Y. M. Maniar ◽  
K. T. Blackwell
Keyword(s):  
Synaptic Plasticity ◽  
Slow Wave Sleep ◽  
Calcium Influx ◽  
Spike Timing ◽  
Medium Spiny Neuron ◽  
Calcium Transients ◽  
Biophysical Model ◽  
Published Data ◽  
High Calcium

The striatum of the basal ganglia demonstrates distinctive upstate and downstate membrane potential oscillations during slow-wave sleep and under anesthetic. The upstates generate calcium transients in the dendrites, and the amplitude of these calcium transients depends strongly on the timing of the action potential (AP) within the upstate. Calcium is essential for synaptic plasticity in the striatum, and these large calcium transients during the upstates may control which synapses undergo plastic changes. To investigate the mechanisms that underlie the relationship between calcium and AP timing, we have developed a realistic biophysical model of a medium spiny neuron (MSN). We have implemented sophisticated calcium dynamics including calcium diffusion, buffering, and pump extrusion, which accurately replicate published data. Using this model, we found that either the slow inactivation of dendritic sodium channels (NaSI) or the calcium inactivation of voltage-gated calcium channels (CDI) can cause high calcium corresponding to early APs and lower calcium corresponding to later APs. We found that only CDI can account for the experimental observation that sensitivity to AP timing is dependent on NMDA receptors. Additional simulations demonstrated a mechanism by which MSNs can dynamically modulate their sensitivity to AP timing and show that sensitivity to specifically timed pre- and postsynaptic pairings (as in spike timing-dependent plasticity protocols) is altered by the timing of the pairing within the upstate. These findings have implications for synaptic plasticity in vivo during sleep when the upstate-downstate pattern is prominent in the striatum.

Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning

2001 ◽  
Vol 13 (10) ◽  
pp. 2221-2237 ◽  
Author(s):  
Rajesh P. N. Rao ◽  
Terrence J. Sejnowski
Keyword(s):  
Cortical Neuron ◽  
Action Potentials ◽  
Learning Rule ◽  
Spike Timing ◽  
Biophysical Model ◽  
Excitatory Synapses ◽  
Temporal Difference ◽  
Temporal Difference Learning ◽  
Neocortical Neurons ◽  
Hebbian Plasticity

A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window of Hebbian plasticity observed physiologically. Furthermore, the size and shape of the window vary with the distance of the synapse from the soma. Using a simple example, we show how a spike-timing-based temporal difference learning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.

How the Shape of Pre- and Postsynaptic Signals Can Influence STDP: A Biophysical Model

2004 ◽  
Vol 16 (3) ◽  
pp. 595-625 ◽  
Author(s):  
Ausra Saudargiene ◽  
Bernd Porr ◽  
Florentin Wörgötter
Keyword(s):  
Membrane Potential ◽  
Weight Change ◽  
Hebbian Learning ◽  
Dendritic Tree ◽  
Learning Rule ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Biophysical Model ◽  
Nmda Channel

Spike-timing-dependent plasticity (STDP) is described by long-term potentiation (LTP), when a presynaptic event precedes a postsynaptic event, and by long-term depression (LTD), when the temporal order is reversed. In this article, we present a biophysical model of STDP based on a differential Hebbian learning rule (ISO learning). This rule correlates presynaptically the NMDA channel conductance with the derivative of the membrane potential at the synapse as the postsynaptic signal. The model is able to reproduce the generic STDP weight change characteristic. We find that (1) The actual shape of the weight change curve strongly depends on the NMDA channel characteristics and on the shape of the membrane potential at the synapse. (2) The typical antisymmetrical STDP curve (LTD and LTP) can become similar to a standard Hebbian characteristic (LTP only) without having to change the learning rule. This occurs if the membrane depolarization has a shallow onset and is long lasting. (3) It is known that the membrane potential varies along the dendrite as a result of the active or passive backpropagation of somatic spikes or because of local dendritic processes. As a consequence, our model predicts that learning properties will be different at different locations on the dendritic tree. In conclusion, such site-specific synaptic plasticity would provide a neuron with powerful learning capabilities.

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