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This case study investigates the frequency modulation in spiking networks comprising twenty Izhikevich model neurons, with both excitatory and inhibitory inputs and outputs. Through phasic input activity (frequencies of 100-1000 Hz) and tonic output (10-100 Hz), we analyze the effects of connectivity and synaptic strengths governed by a fitness function. Our findings reveal a linear relationship between input and output discharge frequencies, highlighting the significant role of inhibition in network performance and probing whether an optimal balance exists for frequency modulation persistence.
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Case Study: Frequency Modulation in spiking network activity
Spiking neurons and network • Twenty neurons (Izhikevich model neurons) • 4 excitatory input units • 10 excitatory and 2 inhibitory units • 4 excitatory output units • Input is phasic activity (frequencies from 100-1000Hz). • Output is tonic activity (frequencies from 10-100Hz). • Connectivity and synaptic strengths are determined by a fitness function. • Two fitness terms: • Arithmetic mean of Inter Spiking Intervals (ISI) of output neurons to target ISI (25, 50, 75, 100 ms) • Variance of ISI of output neurons
Example: Frequency modulation of Tonic activity at ISI of 75ms
Frequency Modulation Connectivity tuned to ISI of 75ms Connectivity tuned to ISI of 25ms
Conclusions • Linear relation between input/output discharge frequencies. • Slope of relationship is determined by network structure that is tuned to a target ISI’s. • Inhibition affect’s network’s performance. Is there an optimal balance? • BUT, all this still doesn’t explain how modulated frequency can persistent in such a network. Mmm…?