Modeling of synaptic plasticity via correlated pre- and postsynaptic activity

2004 ◽  
Author(s):  
T. Kitajima ◽  
M. Nishiyama ◽  
K. Hara
Keyword(s):  
Synaptic Plasticity ◽  
Postsynaptic Activity

scholarly journals Behavioral Time Scale Plasticity of Place Fields: Mathematical Analysis

2021 ◽  
Vol 15 ◽  
Author(s):  
Ian Cone ◽  
Harel Z. Shouval
Keyword(s):  
Synaptic Plasticity ◽  
Fixed Points ◽  
Time Scales ◽  
Convergence Time ◽  
Simple Mathematical Model ◽  
System Parameters ◽  
Temporal Correlations ◽  
Postsynaptic Activity ◽  
Naturally Occurring ◽  
Place Fields

Traditional synaptic plasticity experiments and models depend on tight temporal correlations between pre- and postsynaptic activity. These tight temporal correlations, on the order of tens of milliseconds, are incompatible with significantly longer behavioral time scales, and as such might not be able to account for plasticity induced by behavior. Indeed, recent findings in hippocampus suggest that rapid, bidirectional synaptic plasticity which modifies place fields in CA1 operates at behavioral time scales. These experimental results suggest that presynaptic activity generates synaptic eligibility traces both for potentiation and depression, which last on the order of seconds. These traces can be converted to changes in synaptic efficacies by the activation of an instructive signal that depends on naturally occurring or experimentally induced plateau potentials. We have developed a simple mathematical model that is consistent with these observations. This model can be fully analyzed to find the fixed points of induced place fields and how these fixed points depend on system parameters such as the size and shape of presynaptic place fields, the animal's velocity during induction, and the parameters of the plasticity rule. We also make predictions about the convergence time to these fixed points, both for induced and pre-existing place fields.

scholarly journals Hippocampal Spine Head Sizes are Highly Precise

2015 ◽  
Author(s):  
Thomas M Bartol ◽  
Cailey Bromer ◽  
Justin P Kinney ◽  
Michael A Chirillo ◽  
Jennifer N Bourne ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Strong Correlation ◽  
Time Window ◽  
Synaptic Activity ◽  
Electron Microscopic ◽  
Synaptic Contacts ◽  
Amount Of Information ◽  
Spine Head ◽  
Postsynaptic Activity

Hippocampal synaptic activity is probabilistic and because synaptic plasticity depends on its history, the amount of information that can be stored at a synapse is limited. The strong correlation between the size and efficacy of a synapse allowed us to estimate the precision of synaptic plasticity. In an electron microscopic reconstruction of hippocampal neuropil we found single axons making two or more synaptic contacts onto the same dendrites which would have shared histories of presynaptic and postsynaptic activity. The postsynaptic spine heads, but not the spine necks, of these pairs were nearly identical in size. The precision is much greater than previous estimates and requires postsynaptic averaging over a time window many seconds to minutes in duration depending on the rate of input spikes and probability of release.

scholarly journals Behavioral Time Scale Plasticity of Place Fields: Mathematical Analysis

2020 ◽  
Author(s):  
Harel Z. Shouval ◽  
Ian Cone
Keyword(s):  
Mathematical Model ◽  
Synaptic Plasticity ◽  
Fixed Points ◽  
Time Scales ◽  
Simple Mathematical Model ◽  
System Parameters ◽  
Temporal Correlations ◽  
Postsynaptic Activity ◽  
Naturally Occurring ◽  
Place Fields

AbstractTraditional synaptic plasticity experiments and models depend on tight temporal correlations between pre- and postsynaptic activity. These tight temporal correlations, on the order of tens of milliseconds, are incompatible with significantly longer behavioral time scales, and as such might not be able to account for plasticity induced by behavior. Indeed, recent findings in hippocampus suggest that rapid, bidirectional synaptic plasticity which modifies place fields in CA1 operates at behavioral time scales. These experimental results suggest that presynaptic activity generates synaptic eligibility traces both for potentiation and depression, which last on the order of seconds. These traces can be converted to changes in synaptic efficacies by the activation of an instructive signal that depends on naturally occurring or experimentally induced plateau potentials. We have developed a simple mathematical model that is consistent with these observations. This model can be fully analyzed to find the fixed points of induced place fields, the convergence to these fixed points, and how these fixed points depend on system parameters such as the size and shape of presynaptic place fields, the animal’s velocity, and the parameters of the plasticity rule.

scholarly journals Nanoconnectomic upper bound on the variability of synaptic plasticity

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Thomas M Bartol ◽  
Cailey Bromer ◽  
Justin Kinney ◽  
Michael A Chirillo ◽  
Jennifer N Bourne ◽  
...  
Keyword(s):  
Synaptic Plasticity ◽  
Upper Bound ◽  
Strong Correlation ◽  
Synaptic Activity ◽  
Synaptic Activation ◽  
Synaptic Contacts ◽  
Postsynaptic Activity ◽  
History Of ◽  
The Brain

Information in a computer is quantified by the number of bits that can be stored and recovered. An important question about the brain is how much information can be stored at a synapse through synaptic plasticity, which depends on the history of probabilistic synaptic activity. The strong correlation between size and efficacy of a synapse allowed us to estimate the variability of synaptic plasticity. In an EM reconstruction of hippocampal neuropil we found single axons making two or more synaptic contacts onto the same dendrites, having shared histories of presynaptic and postsynaptic activity. The spine heads and neck diameters, but not neck lengths, of these pairs were nearly identical in size. We found that there is a minimum of 26 distinguishable synaptic strengths, corresponding to storing 4.7 bits of information at each synapse. Because of stochastic variability of synaptic activation the observed precision requires averaging activity over several minutes.

Associative synaptic plasticity in hippocampal CA1 neurons is not sensitive to unpaired presynaptic activity

1996 ◽  
Vol 76 (1) ◽  
pp. 631-636 ◽  
Author(s):  
D. V. Buonomano ◽  
M. M. Merzenich
Keyword(s):  
Synaptic Plasticity ◽  
Temporal Structure ◽  
Learning Rule ◽  
Inverse Correlation ◽  
Pyramidal Cells ◽  
Long Term Potentiation ◽  
Time Varying ◽  
Postsynaptic Activity ◽  
Term Potentiation

1. Hebbian or associative synaptic plasticity has been proposed to play an important role in learning and memory. Whereas many behaviorally relevant stimuli are time-varying, most experimental and theoretical work on synaptic plasticity has focused on stimuli or induction protocols without temporal structure. Recent theoretical studies have suggested that associative plasticity sensitive to only the conjunction of pre- and postsynaptic activity is not an effective learning rule for networks required to learn time-varying stimuli. Our goal in the current experiment was to determine whether associative long-term potentiation (LTP) is sensitive to temporal structure. We examined whether the presentation of unpaired presynaptic pulses in addition to paired pre- and postsynaptic activity altered the induction of associative LTP. 2. By using intracellular recordings from CA1 pyramidal cells, associative long-term potentiation (LTP) was induced in a control pathway by pairing a single presynaptic pulse with postsynaptic depolarization every 5 s (50-70 x). The experimental pathway received the same training, with additional unpaired presynaptic pulses delivered in close temporal proximity, either after or before associative pairing. Five separate sets of experiments were performed with intervals of -200, -50, +50, +200, or +800 ms. Negative intervals indicate that the unpaired presynaptic pulse was presented before the depolarizing pulse. Our results showed that the presence of unpaired presynaptic pulses, occurring either before or after pairing, did not significantly alter the magnitude of LTP. 3. The experimental design permitted an analysis of whether changes in paired-pulse facilitation (PPF) occur as a result of associative LTP. The average degree of PPF was the same before and after LTP. However, there was a significant inverse correlation between the initial degree of PPF and the degree of PPF after LTP. There was no relationship between the change in PPF, and whether the first or second pulse had been paired with depolarization. 4. These results indicate that the presence of unpaired presynaptic pulses does not alter the induction of synaptic plasticity, suggesting that plasticity of the Schaffer collateral-CA1 synapse is primarily conjunctive rather than correlative.

scholarly journals Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

2020 ◽  
Author(s):  
Alexandre Payeur ◽  
Jordan Guerguiev ◽  
Friedemann Zenke ◽  
Blake A. Richards ◽  
Richard Naud
Keyword(s):  
Synaptic Plasticity ◽  
Physiological Mechanism ◽  
Learning Rule ◽  
Credit Assignment ◽  
Hierarchical Networks ◽  
Postsynaptic Activity ◽  
Challenging Tasks ◽  
Synaptic Dynamics ◽  
Mathematical Analyses ◽  
Dependent Learning

AbstractSynaptic plasticity is believed to be a key physiological mechanism for learning. It is well-established that it depends on pre and postsynaptic activity. However, models that rely solely on pre and postsynaptic activity for synaptic changes have, to date, not been able to account for learning complex tasks that demand credit assignment in hierarchical networks. Here, we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then neurons higher in a hierarchical circuit can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites, and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses, and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits.

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