
@article {140660,
	title = {A machine-learning tool to identify bistable states from calcium imaging data.},
	journal = {J Physiol},
	year = {2024},
	month = {2024 Mar 14},
	abstract = {<p>Mapping neuronal activation using calcium imaging in vivo during behavioural tasks has advanced our understanding of nervous system function. In almost all of these studies, calcium imaging is used to infer spike probabilities because action potentials activate voltage-gated calcium channels and increase intracellular calcium levels. However, neurons not only fire action potentials, but also convey information via intrinsic dynamics such as by generating bistable membrane potential states. Although a number of tools for spike inference have been developed and are currently being used, no tool exists for converting calcium imaging signals to maps of cellular state in bistable neurons. Purkinje neurons in the larval zebrafish cerebellum exhibit membrane potential bistability, firing either tonically or in bursts. Several studies have implicated the role of a population code in cerebellar function, with bistability adding an extra layer of complexity to this code. In the present study, we develop a tool, CaMLSort, which uses convolutional recurrent neural networks to classify calcium imaging traces as arising from either tonic or bursting cells. We validate this classifier using a number of different methods and find that it performs well on simulated event rasters as well as real biological data that it had not previously seen. Moreover, we find that CaMLsort generalizes to other bistable neurons, such as dopaminergic neurons in the ventral tegmental area of mice. Thus, this tool offers a new way of analysing calcium imaging data from bistable neurons to understand how they participate in network computation and natural behaviours. KEY POINTS: Calcium imaging, compriising the gold standard of inferring neuronal activity, does not report cellular state in neurons that are bistable, such as Purkinje neurons in the cerebellum of larval zebrafish. We model the relationship between Purkinje neuron electrical activity and its corresponding calcium signal to compile a dataset of state-labelled simulated calcium signals. We apply machine-learning methods to this dataset to develop a tool that can classify the state of a Purkinje neuron using only its calcium signal, which works well on real data even though it was trained only on simulated data. CaMLsort (Calcium imaging and Machine Learning based tool to sort intracellular state) also generalizes well to bistable neurons in a different brain region (ventral tegmental area) in a different model organism (mouse). This tool can facilitate our understanding of how these neurons carry out their functions in a circuit.</p>
},
	issn = {1469-7793},
	doi = {10.1113/JP284373},
	author = {Varma, Aalok and Udupa, Sathvik and Sengupta, Mohini and Ghosh, Prasanta Kumar and Thirumalai, Vatsala}
}
@article {17945,
	title = {Loss of the Habenula Intrinsic Neuromodulator Kisspeptin1 Affects Learning in Larval Zebrafish.},
	journal = {eNeuro},
	volume = {4},
	year = {2017},
	month = {2017 May-Jun},
	abstract = {<p>Learning how to actively avoid a predictable threat involves two steps: recognizing the cue that predicts upcoming punishment and learning a behavioral response that will lead to avoidance. In zebrafish, ventral habenula (vHb) neurons have been proposed to participate in both steps by encoding the expected aversiveness of a stimulus. vHb neurons increase their firing rate as expectation of punishment grows but reduce their activity as avoidance learning occurs. This leads to changes in the activity of raphe neurons, which are downstream of the vHb, during learning. How vHb activity is regulated is not known. Here, we ask whether the neuromodulator Kisspeptin1, which is expressed in the ventral habenula together with its receptor, could be involved. Kiss1 mutants were generated with CRISPR/Cas9 using guide RNAs targeted to the signal sequence. Mutants, which have a stop codon upstream of the active Kisspeptin1 peptide, have a deficiency in learning to avoid a shock that is predicted by light. Electrophysiology indicates that Kisspeptin1 has a concentration-dependent effect on vHb neurons: depolarizing at low concentrations and hyperpolarizing at high concentrations. Two-photon calcium imaging shows that mutants have reduced raphe response to shock. These data are consistent with the hypothesis that Kisspeptin1 modulates habenula neurons as the fish learns to cope with a threat. Learning a behavioral strategy to overcome a stressor may thus be accompanied by physiological change in the habenula, mediated by intrinsic neuromodulation.</p>
},
	issn = {2373-2822},
	doi = {10.1523/ENEURO.0326-16.2017},
	author = {Lupton, Charlotte and Sengupta, Mohini and Cheng, Ruey-Kuang and Chia, Joanne and Thirumalai, Vatsala and Jesuthasan, Suresh}
}
