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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 enhances the discrimination of complex spatiotemporal patterns. (A) Sample of three (out of five) spike patterns of one stimulus set – three forward patterns and their reverses are shown. Each pattern is composed of 10 inputs. (B) Performance during training on the FWD patterns in an IAF neuron. The blue line depicts learning without short-term plasticity and the green line depicts training when synapses were assigned random but fixed STP values. In the red line condition synapses were “metaplastic,” i.e., U underwent long-term plasticity, guided by the temporal synaptic plasticity learning rule. The yellow line depicts retraining with the shuffled STP values obtained from the simulations shown in red again in the absence of STP plasticity. The tempotron learning rule alone (no STP) performed well (blue), yielding 5–7% errors. Here an error is a failure to detect the target (forward) pattern or firing to any of the non-target patterns. Training for longer periods (2000 trials) or stopping training when a fixed error level is achieved (e.g., 20%) yields results similar to the ones shown in (C). (C) Performance during testing. Filled bars represent total errors (FWD + REV), and the dotted bars represent the REV errors (e.g., an output unit trained to recognize pattern #1 responded to pattern #1 presented backwards). Notice that the inclusion of synapses with random (but “fixed”) STP values before training improves performance significantly (green). However, using temporal synaptic plasticity to tune STP (starting with random values) further decreases the number of errors (red). (D) Top row: Response of IAF neuron trained to recognize the stimulus shown in the first row of A (three overlaid test presentations are shown). 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. Each plot shows three trials represented in different shades of blue or red.
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Figure 2: Temporal synaptic plasticity enhances the discrimination of complex spatiotemporal patterns. (A) Sample of three (out of five) spike patterns of one stimulus set – three forward patterns and their reverses are shown. Each pattern is composed of 10 inputs. (B) Performance during training on the FWD patterns in an IAF neuron. The blue line depicts learning without short-term plasticity and the green line depicts training when synapses were assigned random but fixed STP values. In the red line condition synapses were “metaplastic,” i.e., U underwent long-term plasticity, guided by the temporal synaptic plasticity learning rule. The yellow line depicts retraining with the shuffled STP values obtained from the simulations shown in red again in the absence of STP plasticity. The tempotron learning rule alone (no STP) performed well (blue), yielding 5–7% errors. Here an error is a failure to detect the target (forward) pattern or firing to any of the non-target patterns. Training for longer periods (2000 trials) or stopping training when a fixed error level is achieved (e.g., 20%) yields results similar to the ones shown in (C). (C) Performance during testing. Filled bars represent total errors (FWD + REV), and the dotted bars represent the REV errors (e.g., an output unit trained to recognize pattern #1 responded to pattern #1 presented backwards). Notice that the inclusion of synapses with random (but “fixed”) STP values before training improves performance significantly (green). However, using temporal synaptic plasticity to tune STP (starting with random values) further decreases the number of errors (red). (D) Top row: Response of IAF neuron trained to recognize the stimulus shown in the first row of A (three overlaid test presentations are shown). 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. Each plot shows three trials represented in different shades of blue or red.

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 enhances the discrimination of complex spatiotemporal patterns. (A) Sample of three (out of five) spike patterns of one stimulus set – three forward patterns and their reverses are shown. Each pattern is composed of 10 inputs. (B) Performance during training on the FWD patterns in an IAF neuron. The blue line depicts learning without short-term plasticity and the green line depicts training when synapses were assigned random but fixed STP values. In the red line condition synapses were “metaplastic,” i.e., U underwent long-term plasticity, guided by the temporal synaptic plasticity learning rule. The yellow line depicts retraining with the shuffled STP values obtained from the simulations shown in red again in the absence of STP plasticity. The tempotron learning rule alone (no STP) performed well (blue), yielding 5–7% errors. Here an error is a failure to detect the target (forward) pattern or firing to any of the non-target patterns. Training for longer periods (2000 trials) or stopping training when a fixed error level is achieved (e.g., 20%) yields results similar to the ones shown in (C). (C) Performance during testing. Filled bars represent total errors (FWD + REV), and the dotted bars represent the REV errors (e.g., an output unit trained to recognize pattern #1 responded to pattern #1 presented backwards). Notice that the inclusion of synapses with random (but “fixed”) STP values before training improves performance significantly (green). However, using temporal synaptic plasticity to tune STP (starting with random values) further decreases the number of errors (red). (D) Top row: Response of IAF neuron trained to recognize the stimulus shown in the first row of A (three overlaid test presentations are shown). 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. Each plot shows three trials represented in different shades of blue or red.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Temporal synaptic plasticity enhances the discrimination of complex spatiotemporal patterns. (A) Sample of three (out of five) spike patterns of one stimulus set – three forward patterns and their reverses are shown. Each pattern is composed of 10 inputs. (B) Performance during training on the FWD patterns in an IAF neuron. The blue line depicts learning without short-term plasticity and the green line depicts training when synapses were assigned random but fixed STP values. In the red line condition synapses were “metaplastic,” i.e., U underwent long-term plasticity, guided by the temporal synaptic plasticity learning rule. The yellow line depicts retraining with the shuffled STP values obtained from the simulations shown in red again in the absence of STP plasticity. The tempotron learning rule alone (no STP) performed well (blue), yielding 5–7% errors. Here an error is a failure to detect the target (forward) pattern or firing to any of the non-target patterns. Training for longer periods (2000 trials) or stopping training when a fixed error level is achieved (e.g., 20%) yields results similar to the ones shown in (C). (C) Performance during testing. Filled bars represent total errors (FWD + REV), and the dotted bars represent the REV errors (e.g., an output unit trained to recognize pattern #1 responded to pattern #1 presented backwards). Notice that the inclusion of synapses with random (but “fixed”) STP values before training improves performance significantly (green). However, using temporal synaptic plasticity to tune STP (starting with random values) further decreases the number of errors (red). (D) Top row: Response of IAF neuron trained to recognize the stimulus shown in the first row of A (three overlaid test presentations are shown). 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. Each plot shows three trials represented in different shades of blue or red.
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