Susin (2021): Gamma vs Drive

Introduction

In the pyramidal–interneuron gamma (PING) network of Susin and Destexhe (2021), increasing nonrhythmic excitatory Poisson drive onto both populations shifted an asynchronous-irregular-like baseline toward stronger gamma oscillations generated by recurrent E/I interactions.[1]

We attempted a modified replication, retaining the reference synaptic decay times, delays and stimulus timing while searching afferent rates and recurrent weights for weak baseline correlations and stimulus-dependent bursting.

Our 2,000-neuron network is smaller and denser but gives each neuron fewer recurrent inputs. Non-adapting LIF neurons replace AdEx neurons; afferent rates and recurrent excitation are lower; inputs are independent across targets; and a 500 ms hidden baseline precedes the display. These changes limit quantitative alignment in firing rates, burst structure or timing.

Results

Recorded circuit

Private drive strengthens bursting

In the selected configuration, a 50% increase in E- and I-targeted afferent rates strengthened population bursting (Fig. 3). Mean E/I firing rates rose from 3.53/3.83 Hz at baseline to 11.51/11.93 Hz during the plateau and were 4.28/4.38 Hz in recovery. Mean E spike-count correlation rose from 0.009 to 0.267.

Excitatory and inhibitory neurons formed aligned bursts during increased drive (Fig. 3B). Their plateau spike-count profiles overlap strongly but vary in amplitude and timing (Fig. 3C–D); narrow burst bands alternate with dispersed spikes.

Reference PING network

The networks share a drive-dependent response but differ in three respects:

1. Increased E/I excitation strengthens collective activity in both; ours has sharper burst bands (Fig. 4B–C Fig. 3A–B).

2. Their raster samples 1,000 E and 1,000 I neurons from 25,000; ours shows all 1,600 E and 400 I neurons.

3. Both show 600 ms with a 200 ms pulse. Their drive is 2→3→2 Hz during ongoing activity; ours is 0.8→1.2→0.8 Hz after a hidden 500 ms baseline.

Sharper bursts may reflect our non-adapting LIF neurons, altered thresholds and refractory periods, fewer recurrent inputs, recalibrated weights or lower background drive (Table A1); sampling also affects raster density. Controlled comparisons are needed.

Methods

1. Construct the modified replication. We replaced the adapting AdEx neurons in the PING network of Susin and Destexhe (2021)[1] with 1,600 excitatory and 400 inhibitory conductance-based LIF neurons. Recurrent connections, including possible self-connections, were sampled independently with 10% probability. Table A1 lists the available recorded settings. We used a 0.1 ms timestep.

2. Apply the input protocol. Each neuron received 400 equivalent independent excitatory Poisson afferents, each adding 4 nS of AMPA conductance. We initialized voltages at −65 mV, simulated a 500 ms hidden baseline, then preserved state through the 600 ms display. From displayed time 200 ms, both populations received a 50% rate increase with a 50 ms rise, 100 ms plateau and 50 ms fall (Fig. 2C).

3. Measure population activity. We measured per-neuron firing rates during baseline (0–200 ms), plateau (250–350 ms) and recovery (400–600 ms). Synchrony was the mean pairwise Pearson correlation of 10 ms spike counts among up to 100 evenly sampled neurons per population with nonzero count variance. We calculated median interspike-interval coefficient of variation (CV) among neurons producing at least five spikes, retaining the eligible-neuron count.

The retained pipeline reproduces the selected configuration. The earlier exploratory calibration search is not retained as an identified input to this report and is not independently reproducible from this pin. Its selection limits the scope of the displayed result.