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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

Metaplasticity of short-term plasticity. (A) Shift Problem. The goal is for the post-synaptic unit (red) to fire to the shift pattern (left), but not to the synchronous pattern (right). (B) If both input synapses exhibit the same type of STP the shift problem cannot be solved. The traces depict the voltage contribution of each input (light and dark blue) to the total post-synaptic voltage (red). PPF or paired-pulse depression in both inputs cannot solve the problem because the neuron's peak response (red trace) will always be to the second or first pulse of the Synch pattern, respectively. Each input exhibits PPF or PPD depending on whether the inputs have a low or high U, respectively. (C) A simple learning rule that adjusts the variable U (“Pr”) at each synaptic terminal can solve the shift problem. . S is a variable that reflects the number of presynaptic spikes (see Materials and Methods). Training: Pairing post-synaptic depolarization (I) – which generates a spike and acts as the “supervisor” – with the coincident presynaptic spikes of the Shift pattern results in PPF at synapse 1 and PPD at synapse 2, in addition to conventional post-synaptic LTP at both synapses (driven by STDP). The rationale is that the time of the post-synaptic spike in relation to a presynaptic spike train determines whether those synapses will show PPD (early pairing) or PPF (late pairing). By pairing post-synaptic depolarization with either the first or second spikes of the synchronous pattern the post-synaptic neuron will also learn to respond selectively to the synch pattern.
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Figure 1: Metaplasticity of short-term plasticity. (A) Shift Problem. The goal is for the post-synaptic unit (red) to fire to the shift pattern (left), but not to the synchronous pattern (right). (B) If both input synapses exhibit the same type of STP the shift problem cannot be solved. The traces depict the voltage contribution of each input (light and dark blue) to the total post-synaptic voltage (red). PPF or paired-pulse depression in both inputs cannot solve the problem because the neuron's peak response (red trace) will always be to the second or first pulse of the Synch pattern, respectively. Each input exhibits PPF or PPD depending on whether the inputs have a low or high U, respectively. (C) A simple learning rule that adjusts the variable U (“Pr”) at each synaptic terminal can solve the shift problem. . S is a variable that reflects the number of presynaptic spikes (see Materials and Methods). Training: Pairing post-synaptic depolarization (I) – which generates a spike and acts as the “supervisor” – with the coincident presynaptic spikes of the Shift pattern results in PPF at synapse 1 and PPD at synapse 2, in addition to conventional post-synaptic LTP at both synapses (driven by STDP). The rationale is that the time of the post-synaptic spike in relation to a presynaptic spike train determines whether those synapses will show PPD (early pairing) or PPF (late pairing). By pairing post-synaptic depolarization with either the first or second spikes of the synchronous pattern the post-synaptic neuron will also learn to respond selectively to the synch pattern.

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)

Metaplasticity of short-term plasticity. (A) Shift Problem. The goal is for the post-synaptic unit (red) to fire to the shift pattern (left), but not to the synchronous pattern (right). (B) If both input synapses exhibit the same type of STP the shift problem cannot be solved. The traces depict the voltage contribution of each input (light and dark blue) to the total post-synaptic voltage (red). PPF or paired-pulse depression in both inputs cannot solve the problem because the neuron's peak response (red trace) will always be to the second or first pulse of the Synch pattern, respectively. Each input exhibits PPF or PPD depending on whether the inputs have a low or high U, respectively. (C) A simple learning rule that adjusts the variable U (“Pr”) at each synaptic terminal can solve the shift problem. . S is a variable that reflects the number of presynaptic spikes (see Materials and Methods). Training: Pairing post-synaptic depolarization (I) – which generates a spike and acts as the “supervisor” – with the coincident presynaptic spikes of the Shift pattern results in PPF at synapse 1 and PPD at synapse 2, in addition to conventional post-synaptic LTP at both synapses (driven by STDP). The rationale is that the time of the post-synaptic spike in relation to a presynaptic spike train determines whether those synapses will show PPD (early pairing) or PPF (late pairing). By pairing post-synaptic depolarization with either the first or second spikes of the synchronous pattern the post-synaptic neuron will also learn to respond selectively to the synch pattern.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Metaplasticity of short-term plasticity. (A) Shift Problem. The goal is for the post-synaptic unit (red) to fire to the shift pattern (left), but not to the synchronous pattern (right). (B) If both input synapses exhibit the same type of STP the shift problem cannot be solved. The traces depict the voltage contribution of each input (light and dark blue) to the total post-synaptic voltage (red). PPF or paired-pulse depression in both inputs cannot solve the problem because the neuron's peak response (red trace) will always be to the second or first pulse of the Synch pattern, respectively. Each input exhibits PPF or PPD depending on whether the inputs have a low or high U, respectively. (C) A simple learning rule that adjusts the variable U (“Pr”) at each synaptic terminal can solve the shift problem. . S is a variable that reflects the number of presynaptic spikes (see Materials and Methods). Training: Pairing post-synaptic depolarization (I) – which generates a spike and acts as the “supervisor” – with the coincident presynaptic spikes of the Shift pattern results in PPF at synapse 1 and PPD at synapse 2, in addition to conventional post-synaptic LTP at both synapses (driven by STDP). The rationale is that the time of the post-synaptic spike in relation to a presynaptic spike train determines whether those synapses will show PPD (early pairing) or PPF (late pairing). By pairing post-synaptic depolarization with either the first or second spikes of the synchronous pattern the post-synaptic neuron will also learn to respond selectively to the synch pattern.
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