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Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits.

Costa RP, Sjöström PJ, van Rossum MC - Front Comput Neurosci (2013)

Bottom Line: We demonstrate that for typical synaptic dynamics such fitting may give unreliable results.Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters.Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data.

View Article: PubMed Central - PubMed

Affiliation: Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh Edinburgh, UK.

ABSTRACT
Short-term synaptic plasticity is highly diverse across brain area, cortical layer, cell type, and developmental stage. Since short-term plasticity (STP) strongly shapes neural dynamics, this diversity suggests a specific and essential role in neural information processing. Therefore, a correct characterization of short-term synaptic plasticity is an important step towards understanding and modeling neural systems. Phenomenological models have been developed, but they are usually fitted to experimental data using least-mean-square methods. We demonstrate that for typical synaptic dynamics such fitting may give unreliable results. As a solution, we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data.

No MeSH data available.


Inference of short-term plasticity parameters from experimental data from visual cortex. (A) Sample experimental STP traces are shown for PC–PC (red), PC–BC (green), and for PC–MC (blue) connections. (B) Marginalized posterior distributions obtained using slice sampling from these three different excitatory connections suggest that PC–MC (n = 9) connections are quite different from PC–PC (n = 9) and PC–BC (n = 12) connections. Light colored lines show individual connections, while dark colored lines correspond to their average.
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Figure 4: Inference of short-term plasticity parameters from experimental data from visual cortex. (A) Sample experimental STP traces are shown for PC–PC (red), PC–BC (green), and for PC–MC (blue) connections. (B) Marginalized posterior distributions obtained using slice sampling from these three different excitatory connections suggest that PC–MC (n = 9) connections are quite different from PC–PC (n = 9) and PC–BC (n = 12) connections. Light colored lines show individual connections, while dark colored lines correspond to their average.

Mentions: As the commonly used paired-pulse ratio, PPR = PSP2/PSP1, only takes the first two pulses into account, we introduce the Every Pulse Ratio (EPR) as a more comprehensive measure of STP dynamics. It is defined as(8)EPR=1(n−1)∑i=1n−1PSPi+1PSPiThis index measures the average amplitude change from the i to the i + 1 response normalized to the i response in the train. EPR is used in Table 1 and elsewhere to quantify the average degree of depression (EPR < 1) or facilitation (EPR > 1). Using these parameters we calculated the synaptic responses with Equations (3, 4) to a spike train of five pulses at 30 Hz (Figures 2, 4).


Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits.

Costa RP, Sjöström PJ, van Rossum MC - Front Comput Neurosci (2013)

Inference of short-term plasticity parameters from experimental data from visual cortex. (A) Sample experimental STP traces are shown for PC–PC (red), PC–BC (green), and for PC–MC (blue) connections. (B) Marginalized posterior distributions obtained using slice sampling from these three different excitatory connections suggest that PC–MC (n = 9) connections are quite different from PC–PC (n = 9) and PC–BC (n = 12) connections. Light colored lines show individual connections, while dark colored lines correspond to their average.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3674479&req=5

Figure 4: Inference of short-term plasticity parameters from experimental data from visual cortex. (A) Sample experimental STP traces are shown for PC–PC (red), PC–BC (green), and for PC–MC (blue) connections. (B) Marginalized posterior distributions obtained using slice sampling from these three different excitatory connections suggest that PC–MC (n = 9) connections are quite different from PC–PC (n = 9) and PC–BC (n = 12) connections. Light colored lines show individual connections, while dark colored lines correspond to their average.
Mentions: As the commonly used paired-pulse ratio, PPR = PSP2/PSP1, only takes the first two pulses into account, we introduce the Every Pulse Ratio (EPR) as a more comprehensive measure of STP dynamics. It is defined as(8)EPR=1(n−1)∑i=1n−1PSPi+1PSPiThis index measures the average amplitude change from the i to the i + 1 response normalized to the i response in the train. EPR is used in Table 1 and elsewhere to quantify the average degree of depression (EPR < 1) or facilitation (EPR > 1). Using these parameters we calculated the synaptic responses with Equations (3, 4) to a spike train of five pulses at 30 Hz (Figures 2, 4).

Bottom Line: We demonstrate that for typical synaptic dynamics such fitting may give unreliable results.Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters.Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data.

View Article: PubMed Central - PubMed

Affiliation: Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh Edinburgh, UK.

ABSTRACT
Short-term synaptic plasticity is highly diverse across brain area, cortical layer, cell type, and developmental stage. Since short-term plasticity (STP) strongly shapes neural dynamics, this diversity suggests a specific and essential role in neural information processing. Therefore, a correct characterization of short-term synaptic plasticity is an important step towards understanding and modeling neural systems. Phenomenological models have been developed, but they are usually fitted to experimental data using least-mean-square methods. We demonstrate that for typical synaptic dynamics such fitting may give unreliable results. As a solution, we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data.

No MeSH data available.