@article {69632,
	title = {Maintaining and updating accurate internal representations of continuous variables with a handful of neurons.},
	journal = {Nat Neurosci},
	year = {2024},
	month = {2024 Oct 03},
	abstract = {<p>Many animals rely on persistent internal representations of continuous variables for working memory, navigation, and motor control. Existing theories typically assume that large networks of neurons are required to maintain such representations accurately; networks with few neurons are thought to generate discrete representations. However, analysis of two-photon calcium imaging data from tethered flies walking in darkness suggests that their small head-direction system can maintain a surprisingly continuous and accurate representation. We thus ask whether it is possible for a small network to generate a continuous, rather than discrete, representation of such a variable. We show analytically that even very small networks can be tuned to maintain continuous internal representations, but this comes at the cost of sensitivity to noise and variations in tuning. This work expands the computational repertoire of small networks, and raises the possibility that larger networks could represent more and higher-dimensional variables than previously thought.</p>
},
	issn = {1546-1726},
	doi = {10.1038/s41593-024-01766-5},
	url = {https://10.1038/s41593-024-01766-5},
	author = {Noorman, Marcella and Hulse, Brad K and Jayaraman, Vivek and Romani, Sandro and Hermundstad, Ann M}
}
@article {67771,
	title = {A connectome of the  central complex reveals network motifs suitable for flexible navigation and context-dependent action selection.},
	journal = {eLife},
	volume = {10},
	year = {2021},
	month = {2021 Oct 26},
	abstract = {<p>Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context- and experience-dependent spatial navigation. We describe the first complete electron-microscopy-based connectome of the CX, including all its neurons and circuits at synaptic resolution. We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly{\textquoteright}s head-direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.</p>
},
	issn = {2050-084X},
	doi = {10.7554/eLife.66039},
	author = {Hulse, Brad K and Haberkern, Hannah and Franconville, Romain and Turner-Evans, Daniel B and Takemura, Shin-ya and Wolff, Tanya and Noorman, Marcella and Dreher, Marisa and Dan, Chuntao and Parekh, Ruchi and Hermundstad, Ann M and Rubin, Gerald M and Jayaraman, Vivek}
}
