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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

Complex relationship between final synaptic weights and values of U. (A) Change in both w and U over the course of training for one simulation. The origin of each arrow marks the initial value of w (initially all weak) and U; the arrowhead marks the values of w and U for each synapse after training. Arrow colors reflect each of the five output units (each trained to recognize a different pattern). (B) Final values of w and U for all synapses across all 20 experiments. (C) Initial (blue) and final (red) distribution of U values across all 20 experiments.
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Figure 3: Complex relationship between final synaptic weights and values of U. (A) Change in both w and U over the course of training for one simulation. The origin of each arrow marks the initial value of w (initially all weak) and U; the arrowhead marks the values of w and U for each synapse after training. Arrow colors reflect each of the five output units (each trained to recognize a different pattern). (B) Final values of w and U for all synapses across all 20 experiments. (C) Initial (blue) and final (red) distribution of U values across all 20 experiments.

Mentions: Implicit in the notion that synapses may adjust their short-term plasticity to better process the temporal features of stimuli is that U should not be correlated with post-synaptic weights in any simple fashion. To examine the changes in U we plotted the pretraining and posttraining values of U and w (for a single experiment) in Figure 3A. The plot reveals that changes in U and w do not simply reflect initial values or a uniform shift. Across all synapses and experiments there was a complex relationship between U and w, after training (Figure 3B). Additionally, training resulted in a significant change in the distribution of U, the final distribution was bimodal with a peak around 0.2–0.3 and one at the upper boundary (Figure 3C). Note that in Eq. 5, there are two equilibrium points, and when the post-synaptic spike occurs after multiple presynaptic spikes U should be driven towards a value that results in (0.5) at the time of the “last” presynaptic spike. The two neighboring “peaks” in Figure 3B (around 0.22 and 0.3) reflect this. For example, in the case of two presynaptic spikes, followed by a post-synaptic spike, a value of U = 0.3 will yield a value of approximately 0.51 (U + U  (1 − U); assuming the time constant τF is very large relative to the presynaptic interspike interval) at the time of the second presynaptic spike. Similarly in the case of three presynaptic spikes, Eq. 5 will drive U to values around 0.22. The presence of these multiple “peaks” demonstrates that the final values are strongly dependent on the structure of the temporal patterns. And the absence of a simple relationship between U and w, indicates that in these simulations STP is not an epiphenomenon of baseline synaptic strength determined by the post-synaptic terminal.


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

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

Complex relationship between final synaptic weights and values of U. (A) Change in both w and U over the course of training for one simulation. The origin of each arrow marks the initial value of w (initially all weak) and U; the arrowhead marks the values of w and U for each synapse after training. Arrow colors reflect each of the five output units (each trained to recognize a different pattern). (B) Final values of w and U for all synapses across all 20 experiments. (C) Initial (blue) and final (red) distribution of U values across all 20 experiments.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3105243&req=5

Figure 3: Complex relationship between final synaptic weights and values of U. (A) Change in both w and U over the course of training for one simulation. The origin of each arrow marks the initial value of w (initially all weak) and U; the arrowhead marks the values of w and U for each synapse after training. Arrow colors reflect each of the five output units (each trained to recognize a different pattern). (B) Final values of w and U for all synapses across all 20 experiments. (C) Initial (blue) and final (red) distribution of U values across all 20 experiments.
Mentions: Implicit in the notion that synapses may adjust their short-term plasticity to better process the temporal features of stimuli is that U should not be correlated with post-synaptic weights in any simple fashion. To examine the changes in U we plotted the pretraining and posttraining values of U and w (for a single experiment) in Figure 3A. The plot reveals that changes in U and w do not simply reflect initial values or a uniform shift. Across all synapses and experiments there was a complex relationship between U and w, after training (Figure 3B). Additionally, training resulted in a significant change in the distribution of U, the final distribution was bimodal with a peak around 0.2–0.3 and one at the upper boundary (Figure 3C). Note that in Eq. 5, there are two equilibrium points, and when the post-synaptic spike occurs after multiple presynaptic spikes U should be driven towards a value that results in (0.5) at the time of the “last” presynaptic spike. The two neighboring “peaks” in Figure 3B (around 0.22 and 0.3) reflect this. For example, in the case of two presynaptic spikes, followed by a post-synaptic spike, a value of U = 0.3 will yield a value of approximately 0.51 (U + U  (1 − U); assuming the time constant τF is very large relative to the presynaptic interspike interval) at the time of the second presynaptic spike. Similarly in the case of three presynaptic spikes, Eq. 5 will drive U to values around 0.22. The presence of these multiple “peaks” demonstrates that the final values are strongly dependent on the structure of the temporal patterns. And the absence of a simple relationship between U and w, indicates that in these simulations STP is not an epiphenomenon of baseline synaptic strength determined by the post-synaptic terminal.

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