Turning the PING Loop On

PING stripped to its biophysical fundamentals: free-running activity, population spectra and rate responses with the excitatory–inhibitory loop off and on.

Abstract

We compared untrained excitatory–inhibitory circuits with reciprocal feedback disabled or enabled, using one seed and a matched Poisson-drive sweep. In the separate illustrative trials, loop-off E firing was 23.86 Hz at 5 Hz input, versus 8.43 Hz at 45 Hz input with feedback. The matched-drive sweep showed suppression under reciprocal coupling. These observations establish this circuit’s operating behaviour, but do not isolate a causal contribution of gamma timing.

Results

Architecture, spectra and rates

Reciprocal coupling produced recurring E/I volleys and suppressed E firing across the matched-drive sweep. The illustrative raster trials used different drive rates, so their rates alone do not isolate the effect of the loop.

Loop-off and loop-on schematics above population rasters, spectra and firing-rate curves.
Figure 1.

(A–B) COBA and PING schematics; (C–D) E/I rasters; (E, G) E-population spectra on the same vertical scale; (F, H) firing-rate–input curves. COBA occupies A, C, E and F; PING occupies B, D, G and H. E spikes are black and I spikes red. C–H use the same 1024-input network setup, with fixed weights within each loop condition. E/G are spectra of the exact recordings shown in C/D. Rasters and spectra show 400 ms at 5 and 45 Hz input, respectively. Rate curves show one-trial population means over the matched-drive sweep, without error bars; F/H vertical scales differ. The dashed marker is the analysis-selected spectral peak, not a rhythmicity significance test. Schematics describe the model, not a measurement.

COBA excitatory-neuron dynamics

The selected loop-off E neuron received input excitation and leak without inhibitory feedback. Its voltage and reconstructed currents illustrate a single active neuron rather than a population-average response.

Figure 2.

(A) Membrane voltage, (B) conductances and (C) signed currents of the same highest-spike-count E neuron with the loop off, driven at 5 Hz for 400 ms. This is an illustrative neuron, not a population mean. In A, the dashed line marks the -50 mV threshold; spikes reset voltage to -65 mV. In B, excitatory conductance is black and fixed leak dotted; input spikes increase excitation, which decays between events. The disconnected inhibitory feedback contributes no conductance. In C, positive current is depolarising; currents were reconstructed from voltage and conductance using the driving-force relation in Methods.

PING excitatory–inhibitory dynamics

These cellular traces illustrate reciprocal E→I→E feedback (Fig. 3A–F); they do not measure population-wide phase locking or spikes per cycle.

Figure 3.

(A–C) Membrane voltage, conductances and signed currents of the highest-spike-count PING E neuron; (D–F) the same quantities for the highest-spike-count I neuron from the same trial. Input was 45 Hz for 400 ms, a different rate from COBA in Fig. 2, not a matched-input trace comparison. These are illustrative neurons, not population means. In A and D, dashed lines mark the -50 mV threshold; spikes reset voltage to -65 mV. In B and E, excitation is black, inhibition red when nonzero and fixed leak dotted. The I neuron receives excitation from E; the model has no I→I synapse. All conductances are non-negative; inhibition enters through its reversal potential, not a negative conductance. In C and F, positive current is depolarising; currents were reconstructed using the driving-force relation in Methods. Inhibitory current in C is hyperpolarising when voltage exceeds the -80 mV inhibitory reversal potential, even though inhibitory conductance is positive.

Methods

We compared untrained loop-off and loop-on networks using the following simulation and measurement procedure.

Compute

  1. Construct the two loop conditions. Both networks contained 1024 excitatory (E) and 256 inhibitory (I) neurons. One 1024-channel input layer and the same realized weights were reused across raster trials and the drive sweep within each loop condition. An input layer drove E neurons through feedforward weights; reciprocal E→I and I→E weights formed the PING loop, without E→E or I→I recurrence. Loop coupling was 0 or 1.5; the I→E parent mean was 2 times E→I, and both parent standard deviations were 10% of their means. Input parent weights had mean 1.5 and standard deviation 0.3; lower-clamped Gaussian draws were normalised by fan-in, with 95% initially zero and survivors rescaled.

  2. Generate the drive and simulate. Uniform Poisson input drove every channel at the condition’s rate, with seed 42 for input and network initialization. Membranes began at -65 mV and conductances at 0 µS. Exponential-Euler membrane integration used 0.1 ms steps; AMPA and GABA decay constants were 2 and 6 ms, with E/I refractory periods 1.2/0.6 ms.

    Full trial recordings included spikes, voltages and conductances.

Analyse

  1. Measure the drive response. Both loop conditions used 1 trial at each of 2, 5, 10, 20, 40, 70, 100 Hz through 1024 input channels for 400 ms, without a discarded transient. Mean per-neuron firing rate was rP=nspike,P/(NPTpresent),r_P = n_{\mathrm{spike},P} / (N_P T_{\mathrm{present}}), where PP identifies E or I, nspike,Pn_{\mathrm{spike},P} is the population’s total spike count, NPN_P its neuron count, TpresentT_{\mathrm{present}} the full presentation duration in seconds and rPr_P the rate in hertz. No averaging across independent seeds was performed.

  2. Estimate the population spectrum. The mean E spike trace was demeaned and passed to Welch’s density estimator with one full-trial window.1 This is a single-window estimate, not an average over independent segments. The largest peak between 5 and 150 Hz was refined by three-bin parabolic interpolation, clamped to half a bin; it was reported only when I spikes were present. This reporting rule is not a test of significant rhythmicity.

Present

  1. Reconstruct signed currents. Each displayed neuron was the first neuron attaining the highest total spike count in its population during that raster trial. A silent E population used neuron zero; a silent I population had no selected-neuron panels. Raster trials used 1024 input channels at 5 Hz for COBA and 45 Hz for PING. These trials used the same network setup as the matched-drive sweep; only input rate differed.

    For the selected neurons, recorded conductances and voltages gave IXin=−gX(Vm−EX),I_X^{\mathrm{in}} = -g_X (V_m - E_X), where XX identifies excitation, inhibition or leak; gXg_X is conductance in µS, VmV_m membrane voltage and EXE_X reversal potential in mV, and IXinI_X^{\mathrm{in}} inward current in nA. Reversals were 0, -80 and -65 mV, respectively; positive current is depolarising. The sign lives in the driving force, never in the conductance.

Network diagrams

Executed loop-off circuit; both reciprocal E/I matrices are zero.
Figure A1.

Executed loop-off circuit from its retained compiled bundle. XX denotes input spike channels and EE/II the excitatory/inhibitory populations. Both reciprocal matrices are zero; the inactive I population is retained to match the executed definition. Every projection is labelled WXYW_{XY}, with target XX first and source YY second. Arrowheads show excitation; bars show inhibition. Recording aliases and score reductions are omitted.

Executed PING circuit with input drive and reciprocal E/I feedback.
Figure A2.

Executed loop-on circuit from its retained compiled bundle. Symbols follow Figure A1; the reciprocal E→I and I→E matrices are active. Parameter and input-rate sweeps reuse this architecture.

Parameters

Model values are read from original native snapshots through the selected presentation’s compute ancestry. Paired values are E / I. Initializer parent means and SDs describe the original weight distributions, in µS for conductance projections and dimensionless for the current-based readout; they are not measured trained-weight statistics. Missing snapshots or fields are unavailable; current recipes and historical copied tables are not substituted. The recorded configuration identifies the input, numerical and analysis choices of that computation; it does not establish a separate upstream training protocol.

ParameterLoop offLoop on
Network
E / I neuronsUnavailable / UnavailableUnavailable / Unavailable
Output neuronsUnavailableUnavailable
Neurons
Capacitance, E / I (nF)Unavailable / UnavailableUnavailable / Unavailable
Leak conductance, E / I (µS)Unavailable / UnavailableUnavailable / Unavailable
Resting voltage, E / I (mV)Unavailable / UnavailableUnavailable / Unavailable
Threshold, E / I (mV)Unavailable / UnavailableUnavailable / Unavailable
Reset, E / I (mV)Unavailable / UnavailableUnavailable / Unavailable
Refractory period, E / IUnavailable / UnavailableUnavailable / Unavailable
Synapses and weights
Input→E synapseUnavailable / UnavailableUnavailable / Unavailable
E→I synapseUnavailable / UnavailableUnavailable / Unavailable
I→E synapseUnavailable / UnavailableUnavailable / Unavailable
E→Output synapseUnavailable / UnavailableUnavailable / Unavailable
Input→E initializer parent mean / SDUnavailable / UnavailableUnavailable / Unavailable
E→I initializer parent mean / SDUnavailable / UnavailableUnavailable / Unavailable
I→E initializer parent mean / SDUnavailable / UnavailableUnavailable / Unavailable
E→Output initializer parent mean / SDUnavailable / UnavailableUnavailable / Unavailable
Native parameter stageUnavailableUnavailable
Input and simulation
Timestep (ms)UnavailableUnavailable
Duration (ms)UnavailableUnavailable
Evaluation samplesUnavailableUnavailable
Seeds4242
Input rate (Hz)UnavailableUnavailable
Post-burn exclusion (ms)UnavailableUnavailable
Readout and training
Output neuron modelUnavailableUnavailable
EpochsUnavailableUnavailable
Checkpoint roleUnavailableUnavailable
Checkpoint purposeUnavailableUnavailable
Analysis
Frequency search (Hz)UnavailableUnavailable
Count-bin width (ms)UnavailableUnavailable
Autocorrelation maximum lag (ms)UnavailableUnavailable

Dataset

Upstream: —

Downstream: exp110 — Manuscript

Current runset

RunDateDurationSizeEvidenceStorageRan onVerification
exp023-r030-present2s4.4 MBCurrentR2LocalLocal: absent; R2: catalog-verified
exp023-r026-present1s1.3 MBCurrentR2LocalLocal: absent; R2: catalog-verified
exp023-r024-analyse<1s253.3 KBCurrentR2LocalLocal: absent; R2: catalog-verified
exp023-r023-compute14s2.5 MBCurrentR2LocalLocal: absent; R2: catalog-verified

References

  1. P. D. Welch. “The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms.” IEEE Transactions on Audio and Electroacoustics 15(2), 70–73 (1967). doi:10.1109/TAU.1967.1161901.