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Recurrent architecture for adaptive regulation of learning in the insect brain

Nature Neuroscience volume 23, pages 544–555 (2020)Cite this article

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Abstract

Dopaminergic neurons (DANs) drive learning across the animal kingdom, but the upstream circuits that regulate their activity and thereby learning remain poorly understood. We provide a synaptic-resolution connectome of the circuitry upstream of all DANs in a learning center, the mushroom body of Drosophila larva. We discover afferent sensory pathways and a large population of neurons that provide feedback from mushroom body output neurons and link distinct memory systems (aversive and appetitive). We combine this with functional studies of DANs and their presynaptic partners and with comprehensive circuit modeling. We find that DANs compare convergent feedback from aversive and appetitive systems, which enables the computation of integrated predictions that may improve future learning. Computational modeling reveals that the discovered feedback motifs increase model flexibility and performance on learning tasks. Our study provides the most detailed view to date of biological circuit motifs that support associative learning.

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Fig. 1: Individual vertical lobe DANs can induce aversive memory and represent different kinds of punishments in Drosophila larva.
Fig. 2: Comprehensive EM reconstruction of premodulatory neurons reveals a multilayered recurrent architecture for regulating learning.
Fig. 3: Modulatory neurons receive convergent one-step feedback from multiple MBONs from functionally distinct compartments.
Fig. 4: Functional inhibitory and excitatory feedback from compartments of opposite valence converge onto a DAN.
Fig. 5: Two-step feedback from most MBONs to most modulatory neurons further increases intercompartment connectivity.
Fig. 6: Feedback neurons can drive associative memory formation.
Fig. 7: Model reveals effects of feedback motifs on learning task performance.

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Data availability

The source data and statistics for Fig. 1 are available in Source Data Fig. 1. The EM volume of the whole CNS26 and the reconstructed neurons and synapses included in this paper are publicly available at https://l1em.catmaid.virtualflybrain.org/, accessible online thanks to the software CATMAID65,66, the same software used to reconstruct and analyze the neuronal circuits included here. Supplementary Adjacency Matrices 1, 2 and 3 contain all the connectivity information for all neurons from the extended MB network discussed in the study. The light microscopy image stacks of genetic driver lines are available from the corresponding authors upon reasonable request.

Code availability

Python code (used for the model in Fig. 7) and Matlab code (used for Supplementary Fig. 3b) can be found in Supplementary Software. The Matlab code for the analysis of imaging and behavioral data is available from the corresponding author upon reasonable request.

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Acknowledgements

Michael Winding, Mei Shao and Casey Schneider-Mizell contributed equally to this work. We thank Fly Light at HHMI Janelia Research Campus (JRC) for generating confocal images of the GAL4 lines, J. Simpson for sharing a two-photon microscope, Y. Aso and G. Rubin for sharing unpublished driver lines, V. Jayaraman for sharing unpublished versions of CsChrimson and GCamp6f, L. Feng and I. Andrade for help with circuit mapping from EM, K. Hibbard and JRC FLY Core for generating some of the fly stocks, Fly EM at JRC for generating the EM volume, T. Saumweber for discussions, D. Bonnery for help with analyses and Z. Zavala-Ruiz and the JRC Visiting Scientist Program and HHMI JRC for funding. M.Z. was also supported by the European Research Council Consolidator Grant 819650 - LeaRNN, by the Wellcome Trust International Recruitment Supplement 205050/A/16/Z and by the Wellcome Trust Investigator Award 205050/Z/16/Z. C.E was also supported by the European Research Council Consolidator Grant 819650 - LeaRNN and the Wellcome Trust International Recruitment Supplement 205050/A/16/Z. A.C. was supported by Wellcome Trust Grant 205038/Z/16/Z and Wellcome Trust International Recruitment Supplement 205038/A/16/Z. M.W. was also supported by the Wellcome Trust International Recruitment Supplement 205050/A/16/Z and the Wellcome Trust Investigator Award 205050/Z/16/Z. A.L.-K. was supported by the Burroughs Wellcome Foundation, the Gatsby Charitable Foundation, the Simons Collaboration on the Global Brain and NSF award DBI-1707398. B.G. received grant support from the Deutsche Forschungsgemeinschaft (Grant Nos. Ge1091/4-1 and FOR 2705-TP2). A.S.T received grant support from the Deutsche Forschungsgemeinschaft (Grant Nos. TH1584/6-1 and TH1584/7-1).

Author information

Author notes

  1. These authors contributed equally: Claire Eschbach and Akira Fushiki.

Authors and Affiliations

  1. HHMI Janelia Research Campus, Ashburn, VA, USA

    Claire Eschbach, Akira Fushiki, Michael Winding, Casey M. Schneider-Mizell, Mei Shao, Rebecca Arruda, Katharina Eichler, Javier Valdes-Aleman, Tomoko Ohyama, Richard D. Fetter, James W. Truman, Albert Cardona & Marta Zlatic

  2. Department of Zoology, University of Cambridge, Cambridge, UK

    Claire Eschbach, Michael Winding & Marta Zlatic

  3. Departments of Neuroscience and Neurology, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA

    Akira Fushiki

  4. Allen Institute for Brain Science, Seattle, WA, USA

    Casey M. Schneider-Mizell

  5. Institute of Neurobiology, University of Puerto Rico Medical Science Campus, San Juan, Puerto Rico, USA

    Katharina Eichler

  6. Department of Biology, McGill University, Montreal, Quebec, Canada

    Tomoko Ohyama

  7. Department of Genetics, Institute for Biology, University of Leipzig, Leipzig, Germany

    Andreas S. Thum

  8. Abteilung Genetik von Lernen & Gedächtnis, Leibniz Institut für Neurobiologie, Otto von Guericke University Magdeburg, Institut für Biologie, Verhaltensgenetik, & Center for Behavioral Brain Sciences, Magdeburg, Germany

    Bertram Gerber

  9. Department of Biology, University of Washington, Seattle, WA, USA

    James W. Truman

  10. Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA

    Ashok Litwin-Kumar

  11. MRC Laboratory of Molecular Biology, Cambridge, UK

    Albert Cardona & Marta Zlatic

  12. Department of Physiology, Development & Neuroscience, University of Cambridge, Cambridge, UK

    Albert Cardona

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  1. Claire Eschbach
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  2. Akira Fushiki
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Contributions

C.E. conceived the study, designed and performed experiments and data analysis, and wrote the manuscript. A.F. performed circuit reconstruction and analysis, designed and performed experiments and data analysis, and wrote the manuscript. M.Z. and A.C. conceived the study, performed data analysis, and wrote the manuscript. A.L.-K. developed the model and wrote the manuscript. M.W., C.M.S.-M. and J.V.-A. performed circuit reconstruction and analysis. M.S. performed patch-clamp recordings. R.A., K.E. and T.O. performed experiments. A.S.T. and B.G. contributed data analysis, experimental design and revision of the manuscript. R.D.F. and J.W.T. contributed data and reagents.

Corresponding authors

Correspondence to Ashok Litwin-Kumar, Albert Cardona or Marta Zlatic.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature Neuroscience thanks Rachel Wilson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Extended Data Fig. 1 Expression patterns of Split-GAL4 lines.

Each panel shows a representative confocal maximum intensity projection (out of N=3) of the complete CNS of third-instar larvae (indicated by the dotted line in the first panel), with the neuropil labeled with anti-N-Cad antibody (blue) and the Split-GAL4line expression pattern revealed with UAS-myr-GFP (green). Arrowheads indicate cell bodies of identified neurons.

Extended Data Fig. 2 Detailed characterization of associative memories formed through different kinds of ‘optogenetic punishments’ or ‘optogenetic rewards’.

Preference scores are shown for the trained odor, ethyl acetate, when it was paired (paired group, closed circles) or not paired (unpaired group, open circles) with optogenetic punishments or rewards. These Preference scores are used to compute the Learning Performance Scores as shown in Fig. 1c,e and 6a-c, additional testing conditions are also shown here. With some natural punishments, aversive memory is behaviorally expressed by trained Drosophila larvae only if the punishment is present at the moment of the test16. Here we assayed olfactory aversive memories in two ways: both with or without optogenetic punishment (red and black bars, respectively) during the retention test. Odor preference was decreased and increased, respectively, relative to genetic controls, after pairing the odor with the presence and absence of the following optogenetic punishments: co-activation of the aversive DAN-f1 and DAN-g1, co-activation of DAN-f1 and DAN-c1, or activation of Basins. Aversive memory formed by DAN activation (green) or by Basins activation (blue) was expressed to the same extent with or without the DANs activated during the retention test. However, memory evoked by the activation of nociceptive MD IV neurons (orange) or FB2N-19 (yellow) was fully expressed only if these neurons were active again during the retention test. Odor preference was increased and decreased, respectively, relative to genetic controls, after pairing the odor with the presence and absence of the following optogenetic rewards: the co-activation of DAN-h1, -i1, and -k1 (dark green); the activation of FB2N-18 and FB2N-11 (yellow), or activation of FAN-7 (blue-gray). Thus, both absence of odor in the unpaired group of animals, as well as the presence of odor in the paired group of animals can be associated with the activation of some DANs or some of their afferent neurons. These results suggest that presenting an odor unpaired with the activation of some of these DANs induces memory of opposite valence to the paired presentation. For other DANs or afferent neurons, only paired (for example A00c, purple) or only unpaired (for example the modulatory DAN-f1, the nociceptive MD IV sensory neuron, or FB2N-19) contingency significantly affected odor preference with respect to the control group. Either of these two observed types of effects can contribute to the negative or positive learning performance indexes plotted in Fig. 1c,e and 6a-c. Sample sizes: N = 42, 11, 17, 16, 12, 14, 12, 13, 12, 16, 12, 14, 12, 12, 15, 14, 12, 11, 14, 13, 12, 14, 11, 11, 11, 18, 11, 20, 25, 33, 52,14, 21, 14, 14, 18, 18, 31, 52, 27, 11,11, 10, 13, 20 (control groups in bold). Mean and standard deviations are shown. Black *: p-value<Holm-Bonferroni-adjusted threshold for 0.05 from a two-sided Wilcoxon signed rank test comparison between paired and unpaired group. Grey *: p-value<Holm-Bonferroni-adjusted threshold for 0.05 from a two-sided Mann-Whitney U test comparison between the preference scores for a given group (paired or unpaired) and the preference scores (for paired or unpaired protocol, respectively) obtained by the control line shown on the left of each set of data. Exact p-values are available in Source Data Fig. 1.

Extended Data Fig. 3 Matrix of similarity between modulator neurons based on the amount of common input.

We computed the cosine similarity of the connectivity matrix of row input neurons onto column modulatory neurons, which is the dot product of the rows, divided by the multiplication of the norm of each row. An input here is a connection, consisting typically of many synapses, from a specific cell type onto the modulatory neuron. Inputs onto a modulatory neuron are considered if the pair of left and right neurons presynaptic to the pair of left and right modulatory neurons is each above a threshold of 1% (for example the presynaptic neuron makes 3 synapses onto a neuron with 300 postsynaptic sites) and the sum of both is over 3.3% (for example the sum of both connections is above 10 synapses for receiving neurons with 300 postsynaptic sites). Interestingly, functionally similar DANs, whose activation leads to aversive memory for paired odors share a higher fraction of presynaptic partners with each other than with other DANs. By contrast some modulatory neurons that innervate the same compartment but express different neurotransmitters (for example OAN-g1 and DAN-g1) receive inputs from drastically different subsets of pre-modulatory neurons. Such modulatory neurons that innervate the same compartment could therefore be differentially recruited during learning. Interestingly though, OAN-e1 and MBIN-e2 in the UVL, whose activation paired with odor did not in-duce memory in our paradigm and that were not significantly activated by fictive punishments share a higher fraction of their input with the VL/LA DANs than with other modulatory neurons. This raises the possibility that the UVL modulatory neurons may be recruited by similar stimuli to the VL/LA DANs, but only in specific circumstances.

Extended Data Fig. 4 Input onto feedback neurons.

Figure shows the fractions of total dendritic input each pre-modulatory neuron (FBN, FB2N or FFN) receives from KCs, modulatory neurons, MBONs, FBNs, FB2Ns, FFNs, and from other non-MB neurons (others). a FBNs receive on average 12% of their inputs directly from MBONs and most of them also receive inputs from other FBNs, with an average of 26% from MBONs and other FBNs combined (see also Supplementary Figure 4a). b FB2Nsreceive inputs both from FBNs (on average 17%) and from other FB2Ns (on average 28% from FBNs and FB2Ns combined).Many feedback neurons also receive a significant fraction of input from other unknown neurons from other brain areas (other than MB), suggesting that the feedback about the learnt valences of stimuli is integrated with or modulated by other information. c Tables show percent of inputs onto FBNs (top) and FB2Ns (bottom) from MBONs, FBNs, FB2Ns, FFNs, modulatory neurons and Kenyon cells.

Extended Data Fig. 5 (related to Fig. 3a) Modulatory neurons receive convergent one-step feedback from multiple MBONs from functionally distinct compartments.

Connectivity of each of the 40 feedback neuron (FBN) pairs that provide one-step feedback from MBONs to DANs. Each diagram represents the connectivity of a single left-right pair of homologous FBNs. Each box indicates a separate compartment. Purple, compartment(s) of the presynaptic MBON(s). Green, compartment(s) of the postsynaptic modulatory neuron(s). FBNs are ordered according to the modulatory neuron they innervate, starting with peduncle modulatory neurons and ending with the medial lobe ones. Classical neurotransmitter profiles of the MBONs and FBNs are indicated by the arrow (cholinergic, excitatory connection), vertical line (GABAergic, inhibitory connection) or square (glutamatergic, probably also inhibitory connection) for the neurons for which they are known from immunostaining (For images, see Extended Data Fig. 9 for FBNs and ref. 12 for MBONs), or by a circle when they are unknown. 7 FBNs provide exclusively within-compartment feedback. 13 FBNs provide exclusively cross-compartment feedback (named Connectivity of each of the 40 feedback neuron (FBN) pairs that provide one-step feedback from MBONs to DANs. Each diagram represents the connectivity of a single left-right pair of homologous FBNs. Each box indicates a separate compartment. Purple, compartment(s) of the presynaptic MBON(s). Green, compartment(s) of the postsynaptic modulatory neuron(s). FBNs are ordered according to the modulatory neuron they innervate, starting with peduncle modulatory neurons and ending with the medial lobe ones. Classical neurotransmitter profiles of the MBONs and FBNs are indicated by the arrow (cholinergic, excitatory connection), vertical line (GABAergic, inhibitory connection) or square (glutamatergic, probably also inhibitory connection) for the neurons for which they are known from immunostaining (For images, see Extended Data Fig. 9 for FBNs and ref.12 for MBONs), or by a circle when they are unknown. 7 FBNs provide exclusively within-compartment feedback. 13 FBNs provide exclusively cross-compartment feedback (named FANs, for feed-across). 8 FBNs synapse onto multiple modulatory neurons from multiple compartments. The largest class of FBNs (17) receives input from multiple MBONs, with the majority (at least 13) receiving input of potentially opposite sign from MBONs from functionally distinct compartments. More than a quarter of FBNs (at least 12) receive direct GABAergic(inhibitory) or glutamatergic (also potentially inhibitory) input from MBONs from one compartment and direct cholinergic(excitatory) input from MBONs from a functionally distinct compartments enabling them to compare the odor drive to these MBONs. Many DANs (DAN-f1, d1, i1, j1, and k1) receive potentially inhibitory (excitatory FBN downstream of an inhibitory MBON) one-step feedback from MBONs from one compartment and potentially disinhibitory (inhibitory FBN downstream of an inhibitory MBON) or excitatory (excitatory FBN downstream of an excitatory MBON) one-step feedback from MBONs from a functionally distinct compartment. A common pattern for the lobe DANs implicated in memory formation may be a likely inhibitory connection from an MBON from their own compartment and a likely disinhibitory connection from an MBON from a compartment of opposite valence (observed for both DAN-g1 and i1), that could enable these DANs to compare the odor drive to MBONs from compartments of opposite valence.

Extended Data Fig. 6 Clustering FBNs based on input from MBONs.

a Heat map of FBN similarity based on the pattern of FBN synaptic inputs from MBONs. The similarity between a pair of FBNs was computed as the cosine similarity between the vectors of their normalized synaptic inputs from all MBONs. Indices were ordered by agglomerative clustering with average linkage (dendrogram shown at top). We highlight six groups of FBNs defined by similarities in their input patterns (bold lines in dendrogram, numbered). b Heat maps showing patterns of input from MBONs onto FBNs for the input groups highlighted in a. In all cases, connectivity is measured in a Heat map of FBN similarity based on the pattern of FBN synaptic inputs from MBONs. The similarity between a pair of FBNs was computed as the cosine similarity between the vectors of their normalized synaptic inputs from all MBONs. Indices were ordered by agglomerative clustering with average linkage (dendrogram shown at top). We highlight six groups of FBNs defined by similarities in their input patterns (bold lines in dendrogram, numbered). b Heat maps showing patterns of input from MBONs onto FBNs for the input groups highlighted in a. In all cases, connectivity is measured in normalized synaptic input on the postsynaptic neuron. Most input groups receive dominant input from a single specific MBON (Groups 1,2, 5) or small group of MBONs (Groups 3 and 4), while Group 6 is not well-clustered and contains a variety of dissimilar input patterns. c Heat maps showing the patterns of synaptic output from FBNs to modulatory neurons for the input groups highlighted in a. d The observed similarity in the output patterns between FBNs within each group, compared to shuffled data. For each group clustered by input pattern, we computed the observed median of cosine similarity of the output vectors across all pairs of neurons (red line). In Groups 1,2, and 3, the neurons clustered by inputs had more similar output patterns than would be expected by chance. To determine significance, we compared the observed similarity to the distribution of the median cosine similarity for randomly permuted samples from the observed population of input vectors (black histograms, n=10000 randomized trials) with a one-sided permutation test. A Holm-Sidak correction was applied to p-values to correct for multiple comparisons. n.s.: p>0.05.

Extended Data Fig. 7 Clustering FBNs based on output onto modulatory neurons.

a Heat map of FBN similarity based on the pattern of FBN synaptic output across all modulatory neurons. The similarity between a pair of FBNs was computed as the cosine similarity between the vectors of normalized synaptic output onto all modulatory neurons. Indices were ordered by agglomerative clustering with average linkage (dendrogram shown at top). We highlight six groups of FBNs defined by similarities in their output patterns (bold lines in dendrogram, numbered). b Heat maps showing patterns of synaptic output from FBNs to modulatory neurons for output groups highlighted in a. Each group corresponds to several FBNs strongly targeting one or a small number of modulatory neurons, suggesting that some modulatory neurons are more strongly modulated than others. c Heat maps showing patterns of input onto FBNs from MBONs for the output groups highlighted in a. d The observed similarity in the input patterns between FBNs within each group, compared to shuffled data. For each group (as defined by output patterns), we computed the observed median of cosine similarity of the input vectors across all pairs of neurons (red line). In Groups 1-5, the neurons clustered by outputs had more input output patterns than would be expected by chance. To determine significance, we compared the observed similarity to the distribution of the median cosine similarity for randomly permuted samples from the observed population of input vectors (black histograms, n=10000 randomized trials) with a one-sided permutation test. A Holm-Sidak correction was applied to p-values to correct for multiple comparisons. n.s.: p>0.05.

Extended Data Fig. 8 Direct MBONs to modulatory neuron connectivity is very sparse, in contrast to the very dense connectivity via one-and two-step feedback pathways.

a Connectivity matrix showing normalized synaptic input (expressed as % input) each modulatory neuron (columns) receives from each MBON (rows). Only reliable connections for which the postsynaptic neuron receives at least 1% of input from the presynaptic neuron are shown. When the neurotransmitter of the MBON is known, the circle is color-coded to represent type of connection: excitatory (ChAT) or probably disinhibitory (GluT). Color shades represent the valence of the memory formed in a given compartment (red: aversive memory, blue: appetitive memory). True within-compartment feedback connections from an MBON that receives direct synaptic input from that modulatory neuron are boxed in bold. Very few modulatory neurons receive direct input from MBONs, in contrast to the dense connectivity between MBONs and modulatory neurons via the indirect one- and two-step feedback pathways (b). b Connectivity matrix showing indirect connections between MBONs and modulatory neurons via one-step and/or two-step feedback pathways. The matrix was obtained by summing the matrices from Fig. 3b and Fig. 5e. The color indicates the type of indirect connection existing between a given MBON and a given DAN. Bubble size represents a connectivity index computed as in Fig. 3b and Fig. 5e. A connectivity index of 1 or 10 means that for all connections comprising that indirect feedback pathway the presynaptic neuron accounts for 1% and 10% of input onto that postsynaptic neuron, respectively. One- and two-step feedback drastically increases the connectivity between MBONs and modulatory neurons, compared to direct connections (a).

Extended Data Fig. 9 Identification of neurotransmitters expressed in some FBNs/FB2Ns.

Neurotransmitter expression detected in neuron somata using antibody labelling. We identified GAL4 lines that drive gene expression in some of the FBN or FB2N neurons and used them to express GFP in these neurons. We stained central nervous systems with antibodies against GFP and either ChAT (choline acetyltransferase), GABA (gamma aminobutyric acid) or GLUT (vesicular glutamate transporter).Each row shows from left to right: the name of the individual neuron, anti-GFP (green), anti-ChAT (magenta), and both antibody stainings combined; anti-GFP (green), anti-GABA (magenta), and both antibody stainings combined; anti-GFP (green) and anti-GLUT (magenta), and both antibody stainings combined. Whether a cell is cholinergic, GABAergic or glutamatergic is listed at the beginning of each row under the neuron name. Images show confocal maximum intensity projections of specific neuronal cell bodies. At least two replicates were obtained per genotype. Scale bars: 5μm.

Extended Data Fig. 10 (related to Fig. 6) Identification of driver lines for EM-reconstructed Feedback neurons.

We were able to generate Split-GAL4 lines that drive expression in a single pair of neurons, or in very few cell types, for three different pairs of FBNs or FB2Ns that target VL DANs (a-c). We used these lines to optogenetically activate these neurons instead of a US during an associative learning paradigm (Fig. 6). i) Skeletons of specific feedback neurons reconstructed in the EM. Red dots, presynaptic sites. Blue dots, postsynaptic sites. Grey, mushroom body vertical lobe (MB vl) for reference. d, dendritic arbor. ii) Maximum intensity projections of confocal stacks of larval brains showing the same neurons visualized with reporters targeted using specific Split-GAL4 lines. For some lines multicolor flp-outs were used to visualize each neuron in a different color to facilitate identification and comparison with EM (N=1). Grey, neuropil visualized with N-cad. Dashed line, brain outline. iii) Maximum intensity projections of confocal stacks of the entire nervous system showing the complete expression pattern of each line revealed by driving UAS-myr-GFP. Grey, neuropil visualized with N-cad. Dashed line, nervous system outline. Representative image from N=3. a, The SS02401-Split-GAL4 line drives expression in FB2N-19 (i) in the brain (ii), and very weakly and stochastically (not reproducibly in all samples) in a few ascending neurons and ensheathing glia in the nerve chord (iii). b, The SS02108-Split-GAL4 line drives expression in FAN-7 and MB2ON-86 (i) in the brain visualized with multicolor flp-outs in (ii). Complete expression pattern of SS02108-Split-GAL4 visualized with UAS-myr-GFP shows additional expression in a few somatosensory interneurons in the nerve cord, called ladders, that mediate avoidance behavior and are hence unlikely to have a positive valence and evoke the appetitive memory observed in Fig. 6b. We identified the SS04330-Split-GAL4 line as driving expression specifically in the MB2ON-86 neuron and used it as an additional control in Fig. 6b. c, The SS01778-Split-GAL4 line drives expression in both FB2N-18 and FB2N-11, which have very similar morphology and very similar connectivity (Supplementary Figures 3 and 4b-d). The SS02181-Split-GAL4 line (ii shows multi-color flp-outs) drives expression in FB2N-18 and in MB2IN-207, one of the weakly connected pre-modulatory neurons from lineage DAMv12. Notice the ventrally projecting dendrite (d), a distinctive feature of MB2IN-207 neuron (i). UAS-myr-GFP expression patterns of the two lines show that they do not drive expression in any other neurons in the nerve cord (iii).

Supplementary information

Supplementary Information

Supplementary Table 1, legends for Supplementary Adjacency Matrices 1–3, Atlas, Figs. 1–6.

Reporting Summary

Supplementary Adjacency Matrix 1

All Connection types. Adjacency matrix between all neurons that are part of the extended mushroom body network (that is, modulatory neurons, MBONs, FFNs, FBNs and FB2Ns), connected via all connection types.

Supplementary Adjacency Matrix 2

Axo-dendritic connections. Adjacency matrix between all neurons that are part of the extended mushroom body network, connected via axo-dendritic synapses.

Supplementary Adjacency Matrix 3

Axo-axonic connections. Adjacency matrix between all neurons that are part of the extended mushroom body network, connected via axo-axonic synapses.

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Eschbach, C., Fushiki, A., Winding, M. et al. Recurrent architecture for adaptive regulation of learning in the insect brain. Nat Neurosci 23, 544–555 (2020). https://doi.org/10.1038/s41593-020-0607-9

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