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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

Negative pattern learning.Learning results in different activation functions for high (4, 8), medium (3, 5, 7, 9) and low (1, 2, 6, 10) input.
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f7: Negative pattern learning.Learning results in different activation functions for high (4, 8), medium (3, 5, 7, 9) and low (1, 2, 6, 10) input.

Mentions: We show that this mechanism can be employed not only for positive trace learning, when excitability adaptation corresponds to frequency response, but also for negative trace learning, when excitability adaptation counteracts frequency response and approximates a target firing rateθ. This target rate could be set as a result of global inhibitory mechanisms corresponding to the expected meanAi values under physiological stimulation. Accordingly, the neuron responds with decreases of excitability to high input ranges and increases of excitability to low input ranges (Figure 7).


Learning intrinsic excitability in medium spiny neurons.

Scheler G - F1000Res (2013)

Negative pattern learning.Learning results in different activation functions for high (4, 8), medium (3, 5, 7, 9) and low (1, 2, 6, 10) input.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f7: Negative pattern learning.Learning results in different activation functions for high (4, 8), medium (3, 5, 7, 9) and low (1, 2, 6, 10) input.
Mentions: We show that this mechanism can be employed not only for positive trace learning, when excitability adaptation corresponds to frequency response, but also for negative trace learning, when excitability adaptation counteracts frequency response and approximates a target firing rateθ. This target rate could be set as a result of global inhibitory mechanisms corresponding to the expected meanAi values under physiological stimulation. Accordingly, the neuron responds with decreases of excitability to high input ranges and increases of excitability to low input ranges (Figure 7).

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