A Drosophila computational brain model reveals sensorimotor processing

Abstract

The recent assembly of the adult Drosophila melanogaster central brain connectome, containing more than 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain^1,2. Here we create a leaky integrate-and-fire computational model of the entire Drosophila brain, on the basis of neural connectivity and neurotransmitter identity³, to study circuit properties of feeding and grooming behaviours. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation⁴. In addition, using the model to activate neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing⁵-a testable hypothesis that we validate by optogenetic activation and behavioural studies. Activating different classes of gustatory neurons in the model makes accurate predictions of how several taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit, and accurately describes the circuit response upon activation of different mechanosensory subtypes^6-10. Our results demonstrate that modelling brain circuits using only synapse-level connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can describe complete sensorimotor transformations.

Fig. 1. The computational model accurately predicts neurons that respond to sugar stimulation and neurons required for proboscis extension to sugar.

a, Computational model schematic. Activation of the grey neuron at the times indicated by the arrows causes depolarization of the green and purple neurons in proportion to their connectivity from the grey neuron. Upon reaching the firing threshold, a neuron spikes, and its membrane potential is reset. b, Schematic of the proboscis extension response: unilateral sugar presentation causes proboscis extension towards the side of the fly with sugar. Arrow widths are proportional to the number of synapses connecting each set of neurons. c, Predicted MN9 firing rate of either the ipsilateral or contralateral MN9 in response to unilateral left hemisphere sugar GRN activation. Error bars represent s.d., Mann–Whitney U test. d, Heatmap depicting the predicted firing rates in response to unilateral 10–200 Hz sugar GRN firing. The y axis is ordered by firing rate at 200 Hz sugar activation, and depicts the top 200 most active neurons. e, Heatmap depicting the predicted MN9 firing rate when the top 200 responsive neurons are activated at 25–200 Hz; in d and e, the grey squares represent neurons that respond to sugar. f, Heatmap depicting the change in the contralateral MN9 firing rate in response to activation of sugar GRNs, while individually silencing each of the top 200 responsive neurons. The MN9 firing rate for each silenced neuron is normalized relative to the firing of MN9 when no neurons are silenced for each GRN activation frequency; In e and f, the y axis is ordered as in d. g, Histogram of the non-GRNs in f at 50 Hz. h, Venn diagram depicting the intersection between neurons predicted to activate MN9 and neurons predicted to cause a 20% decrease in MN9 firing when silenced.

Fig. 2. The computational model predicts neurons that cause proboscis extension.

a, Predicted MN9 firing rates when each of 106 cell types are activated computationally at 50 Hz. Cell types are ordered by predicted MN9 firing rate. b, Fraction of flies extending the rostrum—the segment of the proboscis controlled by MN9—in response to optogenetic activation; cell types are ordered as in a. Where several split-GAL4 lines for a cell type were tested, the line with the highest extension rate is plotted; n = 10 flies per cell type. c, Confusion matrix showing the accuracy of MN9 activation predictions and the number of cell types in each category. The rostrum was predicted to extend if MN9 was predicted to have non-zero firing as a result of 50 Hz cell-type activation.

Fig. 3. The computational model correctly predicts that Ir94e neurons are aversive but fail to inhibit proboscis extension to a strong sugar stimulus.

a, Schematic outlining the previously known and unknown roles of sugar, bitter and Ir94e neurons. Question marks indicate that the exact substrate(s) that activate Ir94e neurons are not known, nor is it known whether Ir94e activation influences proboscis extension. b,c, Heatmap depicting the predicted MN9 firing rates in response to the combination of sugar GRN firing and bitter (b) or Ir94e (c) GRN activation. d,e, Fraction of flies exhibiting PER upon 50 mM sucrose stimulation or 1 M sucrose stimulation when Gr66a/bitter GRNs (d) or Ir94e GRNs (e) are optogenetically activated. Red bars indicate red light condition; n = 26–32, see Supplementary Table 9 for exact values. Mean ± 95% confidence intervals using Wilson’s score interval, Fisher’s exact test. f, Venn diagram showing the number and overlap of neurons that respond to sugar GRN (green) or water GRN (blue) activation that elicits 40 Hz MN9 firing, as well as bitter GRN (red) or Ir94e GRN activation (purple) activated to reduce 40 Hz MN9 firing to 1 Hz.

Fig. 4. The computational model correctly predicts that the sugar and water pathways share components and additively promote proboscis extension.

a, Heatmap depicting the predicting firing rates in response to 20 to 260 Hz water GRN firing. The y axis is ordered by firing rate at 260 Hz water activation. b, Heatmap depicting the predicted MN9 firing rate when the top 200 responsive neurons are activated at 25–200 Hz. c, Heatmap depicting the change in MN9 firing rate in response to activation of water GRNs at the specified firing rate, while individually silencing each of the top 200 responsive neurons. d, The fraction of flies exhibiting PER upon water stimulation. Green bars indicate green light condition; n = 30–50; see Supplementary Table 9 for exact values. Open and filled circles represent whether the computational model predicted a greater than 20% decrease in MN9 firing at 160 Hz water GRN stimulation. e, Heatmap depicting the predicted MN9 firing rates in response to the combination of sugar and water GRN activity. f, The fraction of flies exhibiting PER upon water stimulation. n = 39–40. d,f, Mean ± 95% confidence intervals, Fisher’s exact test.

Fig. 5. The computational model correctly identifies key neurons in the antennal grooming circuit as well as subtype circuit responses.

a, Schematic of the antennal grooming circuit. Arrows represent known functional connectivity. Grey oval around aDNs indicates that JONs activate aDNs, but exactly which aDNs are not known. b, Heatmap depicting the predicting firing rates in response to 20–220 Hz JON firing; 147 JONs were activated, and are the neurons that have the highest firing rates. Neurons are ordered by firing rate at 220 Hz. c, Heatmap depicting the predicted aDN1 firing rate when the top 300 responsive neurons are activated at 25–200 Hz. d, Heatmap depicting the change in aDN1 firing rate in response to activation of JOs at the specified firing rate, while individually silencing each of the top responsive neurons. e, Histogram of the predicted change in aDN1 firing rate as a result of silencing each non-JONs, when JONs are activated at 140 Hz. The y axis depicts the number of neurons in each bin. Neurons previously identified are labelled. f, Venn diagram depicting the overlap between neurons predicted to be sufficient to activate aDN1 at greater than 2 Hz and neurons required for aDN1 activation. g, JO subtype connectivity onto aBN1 and predicted aBN1 firing in response to JO activation at the specified rate. Error bars, s.d. h, Calcium imaging of aBN1 in response to optogenetic activation of each subtype. The ΔF/F average ± s.e.m. is shown; n ≥ 5 flies tested. Shaded bar indicates when a red light pulse was delivered.

Extended Data Fig. 1. Identification of water and sugar GRN modality.

A. Hierarchical clustering of GRNs and second-order gustatory neurons based on cosine similarity of connectivity of GRNs (y-axis) onto second-order neurons (x-axis). Below, the taste response properties of tested second-order neurons in starved flies is plotted (Shiu, Sterne et al.), demonstrating that GRN cluster 2 synapses most strongly onto neurons that respond only to sugar in starved flies. B. Hierarchical clustering of “zero-order” gustatory neurons onto cluster 2 and 3 GRNs based on cosine similiarity. Black, green and blue lines designate which cluster from (A) each GRN in B belongs to. C. Calcium responses of the second-order neuron Fudog to stimulation of the proboscis in food-deprived flies. Quade’s test with Quade’s All Pairs test, using Holm’s correction to adjust for multiple comparisons. D. Predicted MN9 firing rate of either the ipsilateral or contralateral MN9 in response to unilateral right-hemisphere sugar GRN activation. Mean +/− Standard deviation, Mann-Whitney U-test. E. The fraction of flies exhibiting proboscis extension response upon 50 mM sucrose stimulation. Mean +/− 95% confidence intervals using Wilson’s score interval, Fisher’s exact test. n = 50 flies per genotype.

Extended Data Fig. 2. A comparison between predicted water, sugar, bitter and Ir94e activation.

A-C. Schematics showing which cell types either respond to sugar (A), are sufficient for proboscis extension (B), or are required for proboscis extension to sugar (C). The color of the circle perimeter corresponds to the model predictions, and the inside of the circle represents experimental results. There are mixed results for whether Fdg or FMIn are required for proboscis extension to sugar. D. Venn diagram of the sugar-responsive neurons that are sufficient for MN9 activation and water-responsive neurons sufficient for MN9 activation. E. Venn diagram comparing predicted water-responsive neurons sufficient for MN9 and water-responsive neurons required (i.e., predicted to reduce MN9 firing >20%) for MN9 activity. GRNs are excluded from this analysis. (A-C): Figure adapted with permission from Shiu, Sterne et al.; CC-BY License.

Extended Data Fig. 3. A comparison between predicted water silencing phenotypes and optogenetic silencing.

A. Calcium responses of the second-order neuron Zorro to stimulation of the proboscis in pseudodessicated flies. Quade’s test with Quade’s All Pairs test, using Holm’s correction to adjust for multiple comparisons (testing between tastants), or Wilcoxon one-sided test (testing if different than 0). B. The fraction of flies exhibiting proboscis extension response upon water stimulation. Green bars indicate green light condition. n = 40–50. ns: not significant. Open and filled circled represent whether the computational model predictions a 20% decrease in MN9 firing at 160 Hz water GRN stimulation. C. Histogram of water silencing predictions of the non-GRNs in Fig. 4c at 160 Hz, with tested cell types labelled. Y-axis depicts the number of neurons in each bin. Cell types in red have an experimental water silencing phenotype. D. Comparison of neurons found to be required for feeding initiation to either water or 50 mM sucrose (Shiu, Sterne et al.), when silenced with the anion channelrhodopsin GtACR1. E. The fraction of flies exhibiting proboscis extension response upon water stimulation, n = 90–100. F. The fraction of flies exhibiting proboscis extension response upon 50 mM sucrose stimulation. n = 45–56. B, E, F: Mean +/− 95% confidence intervals (Wilson Interval Score), Fisher’s exact test.

Extended Data Fig. 4. The computational model predicts neurons that elicit aDN2, and neurons that inhibit aBN1.

A. Heatmap depicting the predicted aDN2 firing rate when the top 300 responsive neurons are activated at 25-200 Hz B. Heatmap depicting the change in aDN2 firing rate in response to activation of JOs at the specified firing rate, while individually silencing each of the top responsive neurons. C. Predicted aBN1 firing in response to JO-F activation at the specified rate. Red triangles, JO-F activation with co-silencing of three inhibitory neurons. Error bars represent one standard deviation. D. Schematic of the circuit, with neurons sufficient for antennal grooming in green, and required for antennal grooming in red. Width of arrows is proportional to number of synaptic connections.