The cellular architecture of memory modules in Drosophila supports stochastic input integration

Abstract

The ability to associate neutral stimuli with valence information and to store these associations as memories forms the basis for decision making. To determine the underlying computational principles, we build a realistic computational model of a central decision module within the Drosophila mushroom body (MB), the fly's center for learning and memory. Our model combines the electron microscopy-based architecture of one MB output neuron (MBON-α3), the synaptic connectivity of its 948 presynaptic Kenyon cells (KCs), and its membrane properties obtained from patch-clamp recordings. We show that this neuron is electrotonically compact and that synaptic input corresponding to simulated odor input robustly drives its spiking behavior. Therefore, sparse innervation by KCs can efficiently control and modulate MBON activity in response to learning with minimal requirements on the specificity of synaptic localization. This architecture allows efficient storage of large numbers of memories using the flexible stochastic connectivity of the circuit.

Morphology and synapse topography optimize linear encoding of synapse numbers inDrosophilalooming responsive descending neurons

“…Synapses proximal to the SIZ had slightly larger amplitudes compared to more distal synapses (Figure 9). This distance dependent bias has also been noted in other Drosophila neurons that exhibit synaptic democracy (Hafez et al, 2023;Liu et al, 2022).…”

“…Small current injections were used to ensure that no voltage-gated ion channels were activated, and that the membrane responded passively across trials. The resulting model parameters [capacitance (C m ), leak conductance (g leak ), axial resistivity (R a ), and reversal potential (E rev )] were found to be within physiological ranges (Borst and Haag, 1996;Gouwens and Wilson, 2009) and corresponded to other published models of Drosophila neurons (Gouwens and Wilson, 2009;Günay et al, 2015;Hafez et al, 2023;Liu et al, 2022). All 5 investigated DNs possess the capacity for action potential generation (Dombrovski et al, 2023;Jang et al, 2023;Namiki et al, 2018;Peek, 2018;von Reyn et al, 2017von Reyn et al, , 2014.…”

“…Some studies have suggested that amplifying distal synaptic conductances or shaping synaptic potentials through dendritic ion channels could be how synaptic democracy is established (Häusser, 2001;Häusser and Mel, 2003;Jaffe and Carnevale, 1999;Rumsey andAbbott, 2006, 2006;Williams and Stuart, 2003). On the other hand, the idea that synaptic democracy can emerge from passive electrotonic structure alone has gained accumulating evidence from demonstrations of synaptic democracy in passive models with detailed morphologies and experimentally supported SIZ locations and passive properties (Hafez et al, 2023;Liu et al, 2022;Otopalik et al, 2019Otopalik et al, , 2017Timofeeva et al, 2008).…”

Neuromorphic sequence learning with an event camera on routes through vegetation

“…Overall, the advantage of being able to use the same parameters across environments was considered more crucial to demonstrate the robustness of the approach. Neuron parameters were set on the basis of calculations and electrophysiological data (see Table 1) (73)(74)(75)(76)(77).…”

“…We set the parameters for neuron models and synapse models based on biological data found in (73)(74)(75)(76)(77) and the neuron parameters shown in Table 1.…”