Limits...
Learning intrinsic excitability in medium spiny neurons.

Scheler G - F1000Res (2013)

Bottom Line: We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions.We emphasize that the effects of intrinsic neuronal modulation on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input.The adaptation of the spike response may result in either "positive" or "negative" pattern learning.

View Article: PubMed Central - PubMed

Affiliation: Carl Correns Foundation for Mathematical Biology, Mountain View, CA, 94040, USA.

ABSTRACT
We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal modulation on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how modulation and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP). The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic modulation determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction.

No MeSH data available.


Related in: MedlinePlus

Input correlation-dependent read-out of intrinsic memory.Response to inputs generated fromN = 80 neurons firing with 20Hz with independent Poisson processes using different correlation parametersW = 0.2, 0.9 (A,B). Extreme values of correlations have been chosen for demonstration purposes. Three slightly different neurons withμAs = 1.1,1.3,1.5 are shown under BOTH conditions. (A) Response modulation and different firing rates for each neuron (here: 20, 26, 40Hz) occur with distributed (low correlation) input. (B) Highly correlated input produces reliable spiking and by implication a single firing rate (20Hz). The upper panel shows the membrane voltage, the middle panel shows the membrane conductances, and the lower panel shows the synaptic input as conductance.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4264637&req=5

f4: Input correlation-dependent read-out of intrinsic memory.Response to inputs generated fromN = 80 neurons firing with 20Hz with independent Poisson processes using different correlation parametersW = 0.2, 0.9 (A,B). Extreme values of correlations have been chosen for demonstration purposes. Three slightly different neurons withμAs = 1.1,1.3,1.5 are shown under BOTH conditions. (A) Response modulation and different firing rates for each neuron (here: 20, 26, 40Hz) occur with distributed (low correlation) input. (B) Highly correlated input produces reliable spiking and by implication a single firing rate (20Hz). The upper panel shows the membrane voltage, the middle panel shows the membrane conductances, and the lower panel shows the synaptic input as conductance.

Mentions: To further explore the influence of modulation on the activation function, we apply realistic synaptic input with different amounts of correlation to individual MSNs (seeFigure 4).


Learning intrinsic excitability in medium spiny neurons.

Scheler G - F1000Res (2013)

Input correlation-dependent read-out of intrinsic memory.Response to inputs generated fromN = 80 neurons firing with 20Hz with independent Poisson processes using different correlation parametersW = 0.2, 0.9 (A,B). Extreme values of correlations have been chosen for demonstration purposes. Three slightly different neurons withμAs = 1.1,1.3,1.5 are shown under BOTH conditions. (A) Response modulation and different firing rates for each neuron (here: 20, 26, 40Hz) occur with distributed (low correlation) input. (B) Highly correlated input produces reliable spiking and by implication a single firing rate (20Hz). The upper panel shows the membrane voltage, the middle panel shows the membrane conductances, and the lower panel shows the synaptic input as conductance.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4264637&req=5

f4: Input correlation-dependent read-out of intrinsic memory.Response to inputs generated fromN = 80 neurons firing with 20Hz with independent Poisson processes using different correlation parametersW = 0.2, 0.9 (A,B). Extreme values of correlations have been chosen for demonstration purposes. Three slightly different neurons withμAs = 1.1,1.3,1.5 are shown under BOTH conditions. (A) Response modulation and different firing rates for each neuron (here: 20, 26, 40Hz) occur with distributed (low correlation) input. (B) Highly correlated input produces reliable spiking and by implication a single firing rate (20Hz). The upper panel shows the membrane voltage, the middle panel shows the membrane conductances, and the lower panel shows the synaptic input as conductance.
Mentions: To further explore the influence of modulation on the activation function, we apply realistic synaptic input with different amounts of correlation to individual MSNs (seeFigure 4).

Bottom Line: We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions.We emphasize that the effects of intrinsic neuronal modulation on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input.The adaptation of the spike response may result in either "positive" or "negative" pattern learning.

View Article: PubMed Central - PubMed

Affiliation: Carl Correns Foundation for Mathematical Biology, Mountain View, CA, 94040, USA.

ABSTRACT
We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal modulation on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how modulation and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP). The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic modulation determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction.

No MeSH data available.


Related in: MedlinePlus