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A novel learning rule for long-term plasticity of short-term synaptic plasticity enhances temporal processing.

Carvalho TP, Buonomano DV - Front Integr Neurosci (2011)

Bottom Line: Here we propose that STP is governed by specific learning rules that operate independently and in parallel of the associative learning rules governing baseline synaptic strength.We describe a learning rule for STP and, using simulations, demonstrate that it significantly enhances the discrimination of spatiotemporal stimuli.Additionally we generate a set of experimental predictions aimed at testing our hypothesis.

View Article: PubMed Central - PubMed

Affiliation: Gulbenkian Ph.D. Program in Biomedicine Oeiras, Portugal.

ABSTRACT
It is well established that short-term synaptic plasticity (STP) of neocortical synapses is itself plastic - e.g., the induction of LTP and LTD tend to shift STP towards short-term depression and facilitation, respectively. What has not been addressed theoretically or experimentally is whether STP is "learned"; that is, is STP regulated by specific learning rules that are in place to optimize the computations performed at synapses, or, are changes in STP essentially an epiphenomenon of long-term plasticity? Here we propose that STP is governed by specific learning rules that operate independently and in parallel of the associative learning rules governing baseline synaptic strength. We describe a learning rule for STP and, using simulations, demonstrate that it significantly enhances the discrimination of spatiotemporal stimuli. Additionally we generate a set of experimental predictions aimed at testing our hypothesis.

No MeSH data available.


Related in: MedlinePlus

Temporal synaptic plasticity applied in realistic conditions and background synaptic input. (A) 3D reconstruction of the modeled realistic multicompartment neuron. The location of the driving input synapses is indicated by the red circles (branches in green). (B) Performance of the realistic neuron under the same conditions described in Figure 2C, and as in Figure 2C the filled bars represent total errors, and the dotted bars the REV errors. The presence of STP + temporal synaptic plasticity outperformed the other conditions. (C) Top row: a sample pattern (FWD and REV). Middle row: response of the realistic neuron (C and D) to three repeated presentations of the stimulus above. Synapses do not exhibit short-term plasticity. Bottom row: as above, but synapses exhibit STP, whose parameters were adjusted by the temporal synaptic plasticity learning rule during training. Notice the effectiveness of temporal synaptic plasticity in preventing the neuron to fire to the reverse pattern. This neuron fires in short bursts of doublets and the horizontal dotted line indicates the approximate action potential threshold.
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Figure 4: Temporal synaptic plasticity applied in realistic conditions and background synaptic input. (A) 3D reconstruction of the modeled realistic multicompartment neuron. The location of the driving input synapses is indicated by the red circles (branches in green). (B) Performance of the realistic neuron under the same conditions described in Figure 2C, and as in Figure 2C the filled bars represent total errors, and the dotted bars the REV errors. The presence of STP + temporal synaptic plasticity outperformed the other conditions. (C) Top row: a sample pattern (FWD and REV). Middle row: response of the realistic neuron (C and D) to three repeated presentations of the stimulus above. Synapses do not exhibit short-term plasticity. Bottom row: as above, but synapses exhibit STP, whose parameters were adjusted by the temporal synaptic plasticity learning rule during training. Notice the effectiveness of temporal synaptic plasticity in preventing the neuron to fire to the reverse pattern. This neuron fires in short bursts of doublets and the horizontal dotted line indicates the approximate action potential threshold.

Mentions: To examine the contribution of STP to the discrimination of spatiotemporal stimuli we used a simple feed-forward network, in which the afferents convey the time-varying patterns generated by the stimuli. These inputs synapse onto post-synaptic neurons which act as classifiers. Initially these post-synaptic units were simulated as integrate-and-fire neurons (Figures 1 and 2), and latter as a multi-compartmental model with active conductances (Figure 4).


A novel learning rule for long-term plasticity of short-term synaptic plasticity enhances temporal processing.

Carvalho TP, Buonomano DV - Front Integr Neurosci (2011)

Temporal synaptic plasticity applied in realistic conditions and background synaptic input. (A) 3D reconstruction of the modeled realistic multicompartment neuron. The location of the driving input synapses is indicated by the red circles (branches in green). (B) Performance of the realistic neuron under the same conditions described in Figure 2C, and as in Figure 2C the filled bars represent total errors, and the dotted bars the REV errors. The presence of STP + temporal synaptic plasticity outperformed the other conditions. (C) Top row: a sample pattern (FWD and REV). Middle row: response of the realistic neuron (C and D) to three repeated presentations of the stimulus above. Synapses do not exhibit short-term plasticity. Bottom row: as above, but synapses exhibit STP, whose parameters were adjusted by the temporal synaptic plasticity learning rule during training. Notice the effectiveness of temporal synaptic plasticity in preventing the neuron to fire to the reverse pattern. This neuron fires in short bursts of doublets and the horizontal dotted line indicates the approximate action potential threshold.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC3105243&req=5

Figure 4: Temporal synaptic plasticity applied in realistic conditions and background synaptic input. (A) 3D reconstruction of the modeled realistic multicompartment neuron. The location of the driving input synapses is indicated by the red circles (branches in green). (B) Performance of the realistic neuron under the same conditions described in Figure 2C, and as in Figure 2C the filled bars represent total errors, and the dotted bars the REV errors. The presence of STP + temporal synaptic plasticity outperformed the other conditions. (C) Top row: a sample pattern (FWD and REV). Middle row: response of the realistic neuron (C and D) to three repeated presentations of the stimulus above. Synapses do not exhibit short-term plasticity. Bottom row: as above, but synapses exhibit STP, whose parameters were adjusted by the temporal synaptic plasticity learning rule during training. Notice the effectiveness of temporal synaptic plasticity in preventing the neuron to fire to the reverse pattern. This neuron fires in short bursts of doublets and the horizontal dotted line indicates the approximate action potential threshold.
Mentions: To examine the contribution of STP to the discrimination of spatiotemporal stimuli we used a simple feed-forward network, in which the afferents convey the time-varying patterns generated by the stimuli. These inputs synapse onto post-synaptic neurons which act as classifiers. Initially these post-synaptic units were simulated as integrate-and-fire neurons (Figures 1 and 2), and latter as a multi-compartmental model with active conductances (Figure 4).

Bottom Line: Here we propose that STP is governed by specific learning rules that operate independently and in parallel of the associative learning rules governing baseline synaptic strength.We describe a learning rule for STP and, using simulations, demonstrate that it significantly enhances the discrimination of spatiotemporal stimuli.Additionally we generate a set of experimental predictions aimed at testing our hypothesis.

View Article: PubMed Central - PubMed

Affiliation: Gulbenkian Ph.D. Program in Biomedicine Oeiras, Portugal.

ABSTRACT
It is well established that short-term synaptic plasticity (STP) of neocortical synapses is itself plastic - e.g., the induction of LTP and LTD tend to shift STP towards short-term depression and facilitation, respectively. What has not been addressed theoretically or experimentally is whether STP is "learned"; that is, is STP regulated by specific learning rules that are in place to optimize the computations performed at synapses, or, are changes in STP essentially an epiphenomenon of long-term plasticity? Here we propose that STP is governed by specific learning rules that operate independently and in parallel of the associative learning rules governing baseline synaptic strength. We describe a learning rule for STP and, using simulations, demonstrate that it significantly enhances the discrimination of spatiotemporal stimuli. Additionally we generate a set of experimental predictions aimed at testing our hypothesis.

No MeSH data available.


Related in: MedlinePlus