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Exploring Frequency Modulation in Spiking Neuron Networks: Impact of Inhibition and Connectivity

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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Exploring Frequency Modulation in Spiking Neuron Networks: Impact of Inhibition and Connectivity

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  1. Case Study: Frequency Modulation in spiking network activity

  2. 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

  3. Example: Frequency modulation of Tonic activity at ISI of 75ms

  4. Frequency Modulation Connectivity tuned to ISI of 75ms Connectivity tuned to ISI of 25ms

  5. Does Inhibition actually affect network performance?

  6. Apparently, Yes…

  7. 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…?

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