Within Cognesia, the chemical controls alter response gain, membrane time constants and synaptic release in selected neurons. Dopamine reduces selected dopamine neurons’ gain and release and, when plasticity is enabled, changes Kenyon-cell-to-MBON weights. Octopamine increases selected visual and dopamine neurons’ gain and shortens selected visual neurons’ membrane time constants; serotonin increases selected inhibitory antennal-lobe neurons’ gain; acetylcholine reduces Kenyon-cell gain; and sNPF increases selected Kenyon-cell-to-MBON release. Nitric oxide, tyramine and the pooled peptide signal have no direct receptor effect in this configuration.

Energy affects PAM dopamine-neuron gain through a hunger and insulin term, while energy, hydration and stress affect endocrine secretion. Arousal scales octopamine source activity, and daily clock phase scales dopamine and serotonin source activity. Holding chemical levels fixed overrides source-driven changes in those fields, so changing a state control need not change voltage.

Studies support octopamine modulation of visual responses and hunger-sensitive signaling in particular circuits [6], but the receptor coverage, coupling strengths, time constants and state equations used here are imposed assumptions. The scenario values are treated as exploratory settings; no calibration to measured chemical concentrations or physiological states is documented. Chemical values are in arbitrary units; physiological controls are normalized from zero to one, with clock phase measured in cycles.

Each run starts from −52 mV membrane potential and zero synaptic drive, followed by 1000 ms of preparation under stationary mean luminance with electrical input off. The saved chemical and physiological initial settings are supplied in the run configurations. The numerical timestep is 0.1 ms and the recording interval is 20 ms. Figure 1 runs for 10,000 ms; Figure 2 and Figure 3 run for 1000 ms. One deterministic stimulus run per scenario is reported, each with a paired stationary-luminance control; there are no independent repetitions. Figure 2 and Figure 3 additionally apply a 5 mV-equivalent, 20 Hz square-pulse input to model neuron index 125,632, with 50% duty cycle from 100 to 1000 ms. This is an additive model input, rather than a measured electrode voltage. All three runs failed the pre-equilibration stationarity check and recorded voltage clamping, which limits their interpretation.

Figure 1 and Figure 2 represent the population mean of 7932 R1 - R6 neurons; Figure 3 represents the mean of 32,379 central-brain neurons. They do not represent individual-neuron voltages. The R1 - R6 ranges and oscillation midranges are calculated over the same 200 - 980 ms interval, including 40 samples at 20 ms spacing and excluding the first 200 ms. The midrange is the average of the minimum and maximum. This common interval does not remove the differences in stimulation between runs.

These runs use FlyWire materialization 783: all 138,639 neurons in Completeness_783.csv and all 15,091,983 directed connections in Connectivity_783.parquet, representing 54,492,922 synapses. Neuron annotations are joined by root ID without an additional cell-type or completion-status filter; no additional synapse-count threshold is imposed. Photoreceptor sign corrections are model assumptions. Cognesia code is available at https://github.com/allenwuzhouhan-web/cognesia. The accompanying Cognesia run configurations.zip contains the original manifests, matching configuration files, dataset hashes and population-mean traces, identifying runs 20260927T053650Z_visual_grating_cd43a5, 20261001T024631Z_visual_grating_172a17 and 20261001T032519Z_visual_grating_f41d6e for Figures 1-3, respectively. The screenshots in Graph 2 and Graph 4 differ from the saved settings; the manifests specify the values actually used. The runs used a modified working tree without a complete archived source snapshot, so exact rerunning is not fully reproducible from the public release; the reported trace calculations can be repeated from the accompanying files.

It is reasonable to begin with the “well-fed-state” as the first model scenario. Graph 2 shows the displayed settings; the saved run configuration records the settings actually used for Figure 1.

Graph 2. Displayed well-fed-state settings. These differ from the saved Figure 1 configuration supplied with the manuscript.

Data

Focusing on the mean voltage of R1 to R6, it can be observed, as in Figure 4, it shows an abrupt drop from −43.73 mV, followed by small oscillations. Over the common analysis interval of 200 - 980 ms, the voltage ranges from −45.34 to −45.28 mV, with an oscillation midrange of −45.31 mV. Here the midrange is the average of the minimum and maximum, rather than the time average.

Analysis

Conclusion

In such case, the stimulus may drive downstream direction-selective motion circuitry [8]; this R1 - R6 trace alone does not predict turning direction.

In details, T4 cells and T5 cells receive the input of the visual electrical signals [8], forming part of the direction-selective motion pathway; their activity and locomotor output were not quantified here.

In this well-fed model condition, the R1 - R6 voltage trace reflects the specified stimulus and parameter settings, without establishing behavior under natural conditions.

Secondly, it is worthwhile to investigate the hungry-state model scenario. The simulated physiological values and chemical messenger controls in model units are shown in Graph 3; the corresponding voltage graph is shown in Figure 2.

Data

Analysis

This presents a rather intriguing result, as the voltage over 200 - 980 ms ranges from −44.65 to −44.46 mV. Over this same interval, the oscillation midrange is −44.55 mV, compared with −45.31 mV in the well-fed run. These values are calculated from the minimum and maximum of each population-mean trace. Over 0 - 980 ms, both traces have the same overall maximum of −43.73 mV.

In this hungry-state scenario, energy reserves, hydration and several other controls are changed together. The stimulus and added electrical input also differ from Figure 1, so the voltage difference cannot be attributed to hunger or an individual messenger.

Connections

Sluggishness and delayed responses remain untested hypotheses. The voltage trace alone does not establish locomotor speed or response latency; behavioral measurements would be needed to test those predictions. Yang et al. (2015) instead report starvation-induced hyperactivity in adult Drosophila, which should be considered when interpreting this scenario [7].

To test such hypotheses in real life, food deprivation, hydration and stress would need to be controlled separately, with locomotor activity and response latency measured directly.

Graph 3. Value settings for hungry state.

Lastly, an edge case worth diverting attention to is the “trance-like-state”. This is an author-defined model label; dizziness, altered awareness and unconsciousness remain untested hypotheses, rather than states established by the voltages.

Because this condition was hypothesized to act on central circuits, we analyzed mean membrane potential across 32,379 central-brain neurons, as shown in Figure 3. Figure 1 and Figure 2 instead measure R1 - R6 voltage. Without corresponding well-fed and hungry central-brain traces in these figures, they do not provide a like-for-like comparison across all three states. Graph 4 shows displayed settings; the saved run configuration records the settings actually used.

Graph 4. Displayed trance-like-state settings. These differ from the saved Figure 3 configuration supplied with the manuscript.

Data

Analysis

The simulated central-brain voltage fluctuates over the 0 - 980 ms recording, ranging from −52.28 mV to −52.09 mV (0.20 mV peak to peak, calculated before rounding the endpoints).

Connections

The simulated central-brain trace fluctuates during visual stimulation, but it does not establish behavioral responsiveness or response latency.