- Poster Presentation
- Open Access
KIR currents suppress neuronal spiking for unsynchronized distal synaptic inputs in striatal medium spiny neurons: a computational study
© John and Manchanda; licensee BioMed Central Ltd. 2010
- Published: 20 July 2010
- Synaptic Input
- Temporal Integration
- Medium Spiny Neuron
- Neuron Spike
- Noise Fraction
Dendritic active conductances are known to powerfully modulate the integration of subthreshold synaptic inputs and thereby, neuronal excitability in central neurons. A suitable candidate for this modulation in medium spiny (MS) neurons of nucleus accumbens (NAc) is the KIR currents. We predict that KIR currents may differentially affect the temporal integration of synchronized and unsynchronized inputs from distal and proximal synapses and consequent neuron spiking. In this study, using a 189-compartment computational model of the neuron, built using NEURON simulation platform and based on Wolf et al. (2005) , we investigate this issue.
Eighty co-localized NMDA-AMPA synapses were used to mimic physiological synaptic inputs which were distributed either distally or proximally. Randomness of the inputs was varied using an adjustable parameter called “noise fraction” whose value varied between 0 (synchronized) and 1 (completely random) . The synapses were redistributed within each region for each value of noise fraction. 30 trials were done for every combination of noise fraction and synapse position, recording the number of spikes elicited in each trial.
In the presence of KIR, probability of spiking progressively decreased with randomness for distal synaptic inputs (0.2 for completely random inputs) whereas in all other cases the probability was almost 1 (probability = 0.94 for Proximal inputs (Control); Figure 1A). Furthermore, the average number of spikes was greater for proximal inputs (Figure 1B & C). In the presence of KIR, the number of spikes decreased with the randomness of the input (Figure 1B) while in its absence, the number of spikes first increased and then decreased (Figure 1C) with input randomness.
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