ArticleScientific reports2018
Effects of Touch Location and Intensity on Interneurons of the Leech Local Bend Network.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Synaptic input and temperature influence sensory coding in a mechanoreceptor.Frontiers in cellular neuroscience · 2023Article
- Non-synaptic Plasticity in Leech Touch Cells.Frontiers in physiology · 2019Article
- Higher Network Activity Induced by Tactile Compared to Electrical Stimulation of Leech Mechanoreceptors.Frontiers in physiology · 2018Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Touch triggers highly precise behavioural responses in the leech. The underlying network of this so-called local bend reflex consists of three layers of individually characterised neurons. While the population of mechanosensory cells provide multiplexed information about the stimulus, not much is known about how interneurons process this information. Here, we analyse the responses of two local bend interneurons (cell 157 and 159) to a mechanical stimulation of the skin and show their response characteristics to naturalistic stimuli. Intracellular dye-fills combined with structural imaging revealed that these interneurons are synaptically coupled to all three types of mechanosensory cells (T, P, and N cells). Since tactile stimulation of the skin evokes spikes in one to two cells of each of the latter types, interneurons combine inputs from up to six mechanosensory cells. We find that properties of touch location and intensity can be estimated reliably and accurately based on the graded interneuron responses. Connections to several mechanosensory cell types and specific response characteristics of the interneuron types indicate specialised filter and integration properties within this small neuronal network, thus providing evidence for more complex signal processing than previously thought.
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