![](bg3.png)
associating an ISI with a particular current value dif-
ficult.
Additionally, this same experimental setup allows
to clamp the firing frequency of a neuron at a given
value, which can be useful in protocols that require
delivering perturbations at fixed phases in the firing
cycle of the cell (Miranda-Dom´ınguez et al., 2010).
4 DISCUSSION
In this contribution we have presented LCG, a soft-
ware to perform open- and closed-loop electrophysi-
ological experiments. We have described some of the
general principles that underly code reusability and
flexibility of the toolbox. As a test case, we have
shown a novel efficient way of measuring the input-
output relationship of neurons by continuously esti-
mating the firing frequency of the cell and, in closed-
loop, clamping it to a desired frequency value. This
allows to sample the f-I curve both with very high res-
olution and in a relatively short time (less than 30s).
We are currently using LCG to perform both
dynamic clamp experiments where very low laten-
cies are required and in vivo experiments combining
whole cell with extracellular recordings where large
populations of neurons are probed simultaneously.
Using LCG in conjunction with high-level scripting
languages such as Python or Matlab offers several ad-
vantages, among which we mention:
• the possibility to use standard optimisation proce-
dures to find, in real-time, optimal parameter val-
ues.
• The possibility to implement an experimental
pipeline with standardised protocols, which allow
to easily compare cells across experimental condi-
tions and to speed up the successive data analysis.
• A relatively easy implementation of hybrid mi-
crocircuits, along the lines of what has been pre-
sented in (Kispersky et al., 2011).
It is worthwhile noting that, while some commercial
and open source packages offer scripting capabili-
ties, the possibility of performing electrophysiologi-
cal recordings at the command line opens new pos-
sibilities for automating experimental workflows and
allows users to closely integrate standard optimisation
tools (for instance from Python’s Numpy module) in
their experiments.
By enabling closed loop experiments at various lev-
els of latency and allowing to interface with general-
purpose scripting languages, LCG has the potential to
boost electrophysiologicalresearch to another level of
automation and protocol complexity with minimal ef-
fort on the neuroscientist part.
ACKNOWLEDGEMENTS
D.L. is supported by the Flanders Research Foun-
dation (grant no. 12C9112N, http://www.fwo.be).
This work was partly supported by the University of
Antwerp and by the European Commission, through
the Seventh Framework Programme under the ICT -
Future and Emerging Technologies scheme (project
ENLIGHTENMENT, grant agreement no. 284801).
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