ArticlePloS one2014
Injection of fully-defined signal mixtures: a novel high-throughput tool to study neuronal encoding and computations.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Overexpression of KCNN4 channels in principal neurons produces an anti-seizure effect without reducing their coding ability.Gene therapy · 2024Article
- Estimating short-term synaptic plasticity from pre- and postsynaptic spiking.PLoS computational biology · 2017Article
- Encoding of High Frequencies Improves with Maturation of Action Potential Generation in Cultured Neocortical Neurons.Frontiers in cellular neuroscience · 2017Article
- Cortical Specializations Underlying Fast Computations.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2016Review
- Identifying and tracking simulated synaptic inputs from neuronal firing: insights from in vitro experiments.PLoS computational biology · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Understanding of how neurons transform fluctuations of membrane potential, reflecting input activity, into spike responses, which communicate the ultimate results of single-neuron computation, is one of the central challenges for cellular and computational neuroscience. To study this transformation under controlled conditions, previous work has used a signal immersed in noise paradigm where neurons are injected with a current consisting of fluctuating noise that mimics on-going synaptic activity and a systematic signal whose transmission is studied. One limitation of this established paradigm is that it is designed to examine the encoding of only one signal under a specific, repeated condition. As a result, characterizing how encoding depends on neuronal properties, signal parameters, and the interaction of multiple inputs is cumbersome. Here we introduce a novel fully-defined signal mixture paradigm, which allows us to overcome these problems. In this paradigm, current for injection is synthetized as a sum of artificial postsynaptic currents (PSCs) resulting from the activity of a large population of model presynaptic neurons. PSCs from any presynaptic neuron(s) can be now considered as "signal", while the sum of all other inputs is considered as "noise". This allows us to study the encoding of a large number of different signals in a single experiment, thus dramatically increasing the throughput of data acquisition. Using this novel paradigm, we characterize the detection of excitatory and inhibitory PSCs from neuronal spike responses over a wide range of amplitudes and firing-rates. We show, that for moderately-sized neuronal populations the detectability of individual inputs is higher for excitatory than for inhibitory inputs during the 2-5 ms following PSC onset, but becomes comparable after 7-8 ms. This transient imbalance of sensitivity in favor of excitation may enhance propagation of balanced signals through neuronal networks. Finally, we discuss several open questions that this novel high-throughput paradigm may address.
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