Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network

Area of Science:

• Computational neuroscience
• Neural network modeling
• Striatal function

Background:

• Medium spiny neurons (MSNs) form a sparsely connected network in the striatum, receiving excitatory input from the cortex.
• Previous models showed that sparse inhibitory networks can form cell assemblies firing coherently with constant excitation.
• Experimental studies reveal strong firing rate modulations in MSN populations during behavioral tasks.

Purpose of the Study:

• To extend computational models of MSN networks to investigate responses to dynamic cortical input.
• To explore how varying excitatory input strength and patterns affect cell assembly dynamics.
• To determine if MSN networks can generate temporally specific, stimulus-dependent responses relevant to behavior.

Main Methods:

• Numerical simulations of sparse random networks of inhibitory spiking neurons (modeling MSNs).
• Introduction of excitatory glutamatergic cortical input, varying in strength and pattern (Poisson processes, sudden switches).
• Analysis of network activity using peri-stimulus time histograms (PSTH) to observe firing patterns and cell assembly dynamics.

Main Results:

• Cell assembly dynamics persist with weak, noisy Poisson cortical input, but regular firing and quiescence emerge with stronger input.
• Sequences of cell assembly activations can be locked to sudden switches in excitatory input.
• Model PSTHs show stimulus and temporal specificity, with modulations locked to task events, demonstrating diverse, evolving responses.

Conclusions:

• The simulated MSN network can generate complex, temporally evolving, stimulus-dependent responses.
• These network properties are suitable for generating slow, coherent, task-dependent activity observed in animal behavior.
• Dynamic cortical input significantly shapes MSN network output, influencing cell assembly activation patterns.