Functional connectivity graphs in hippocampal cultures using tetanic stimulation for real time robotic control
-
1
Universidad Nacional de Educación a Distancia (UNED), Departamento de Inteligencia Artificial, Spain
-
2
Universidad Miguel Hernández, Instituto de Bioingeniería, Spain
Motivation
The main objective of this work will be to develop a methodology for finding “neural wires” that we can use to control a robot using these in a simple Braitenberg’s behaviour scheme.
Material and Methods
In this paper we use voltage tetanic stimulation on embryonic hippocampal cultures for analyzing its functional connectivity represented by connectivity graphs. We show that the analysis of the cell culture can lead to find “neural wires” in order to induce over them neural spike trains.
Extensive spike analysis, multiple correlation analysis, post-stimulus time histogram analysis, instantaneous firing frequencies analysis and inter-spike intervals analysis are the procedures used in analyzing the functional behavior.
The spontaneous activity of the cultures before the stimulation experiments is observed in order to describe the neural circuit properties. Multiple correlation analysis show us functional connectivity graphs, where we can capture patterns of deviations from statistical independence between distributed neurons units, and measure their correlation/covariance. The correlation between the activity over the electrodes is analyzed to figure out the circuit topology. This analysis permits identifying neuron units along the hippocampal culture with a strong correlation between them that we will probably can use as a “neural wires”. In summary, this allows us to select the electrodes that are part of the neural circuit. We will apply the stimulus over this subset of electrodes to find out which of them trigger neural units over the neural circuit. Finally we perform a post-stimulus time histogram analysis to measure and register the average activity and the standard deviation of each electrode on this subset of electrodes.
The next step is consequently to perform a stimulus on each electrode of the subset of electrodes that are part of the neural circuit. This stimulus and posterior analysis on each electrode is performed one by one.
Once these electrodes have been stimulated, their evoked response is analyzed. Post-stimulus time histogram analysis gives us the proof that the stimulus has an effect over the neural circuit, evoking response in some neural units. Specifically, shows us which electrodes increase their activity. That activity is measured and compared with the basal activity. The average activity over the first 100 ms after the stimulus on each electrode is compared with the average and standard deviation activity measured in the same electrodes previous to the stimulus.
The objective of this extensive analisys is to find a subset of electrodes that have the following properties:
-The stimulation on electrodes E1, E2, …, En, cause neuronal response in other neural units over the neural circuit.
-The increase of activity is measured in other electrodes, R1, R2, …, Rm.
-Every one of the trigger electrodes, E1 to En, evokes response in one or more of the activated electrodes R1 to Rm.
-The subset of activated electrodes by the trigger electrode Ei, is different to the subset of activated electrodes by the trigger electrode Ej, even though they may overlap.
-If stimulating Ei evokes response on Ej, stimulating Ej does not evoke response on Ei.
If these conditions are fulfilled, we can assume that we have independent neural circuits in the cell culture that we can use as “neural wires”. It is iportant to highlight that unidirectional "neural wires" are preferred.
Additionally, post-stimulus time histogram is used to compare basal activity with the activity after the stimulation in the whole population, for analyzing the stimulus propagation over these “neural wires”.
Finally, a turtlebot robot configured as a Braitenberg's 2b vehicle is used to show that is possible to use biological “neural wires” and tetanic stimulation for a robotic systems real time control.
Results
The results illustrate the existence of qualitatively different responses to stimulation. Our results indicate the existence of an increase in instantaneous firing frequencies in some specifical units but not in others, showing us a way to figure out the inherent circuit topology.
Since this kind of stimulation has been used in attempts to induce plasticity in hypocampal cells, refining some crucial aspects of the stimulation is still crucial.
Discussion and Conclusion
In this work a methodology for analyzing functional behavior in hippocampal cultures is described in order to find “neural wires”. First, the neural responses are analyzed for identifying the relevant and functional electrodes. Tetanization is performed in order to improve the connection through this “neural wires”. Finally the sensors of the robot will stimulate specific electrodes while the information in specific connected ones is used for controlling the robot driving elements following Braitenberg’s principles and getting a characteristic complex behaviour.
References (optional)
-J.M. Ferrandez, V. Lorente, F. delaPaz, J.M. Cuadra, Jose Ramon Alvarez-Sanchez d, and E. Fernandez. A biological neuroprocessor for robotic guidance using a center of area method. ELSEVIER, 2010.
- José Ramón Álvarez-Sánchez · Félix de la Paz López · José Manuel Cuadra Troncoso · Daniel de Santos Sierra. Reactive navigation in real environments using partial center of area method. Article in Robotics and Autonomous Systems 58(12):1231-1237 · December 2010.
-Valentino Braitenberg. Vehicles Experiments in Synthetic Psychology. The MIT Press, 1986.
-Thomas B DeMarse, Daniel A Wagenaar, Axel W Blau, and Steve M Potter. The neurally controlled animat: biological brains acting with simulated bodies. Autonomous robots, 11(3):305–310, 2001.
Figure Legend (optional)
-Figure 1: Multiple correlation analysis over unstimulated MEA.
-Figure 2: Four distinct pictures are represented to show the activity on each stimulated electrode. On each picture, the first PSTH represents the average firing rate after stimulation. Below, normalized activity after stimulation by the average unstimulated electrode activity is represented. The third graph shows, in red color, the average activity and standard deviation over first 100 ms after estimulation which is compared in green color with the average and twice the standar deviation of unstimulated activity. E10-R25 indicates that 'Electrode 10' has been stimulated and the activity over electrode 25 is showed. In this case we can see that stimulating E10 causes an increasing activity on R25, nevertheless stimulating E25 does not causes an increasing activity on R10. We can assume that we have found an unidireccional "neural wire". The same approach was applied over electrodes 36 and 39.
Keywords:
culture,
hippocampal,
robotic,
real time,
braitenberg
Conference:
MEA Meeting 2016 |
10th International Meeting on Substrate-Integrated Electrode Arrays, Reutlingen, Germany, 28 Jun - 1 Jul, 2016.
Presentation Type:
Poster Presentation
Topic:
MEA Meeting 2016
Citation:
Calvo
MV,
Ferrández-Vicente
J,
De La Paz Lopez
F,
Alvarez-Sanchez
JR,
Cuadra Troncoso
JM and
Fernández
E
(2016). Functional connectivity graphs in hippocampal cultures using tetanic stimulation for real time robotic control.
Front. Neurosci.
Conference Abstract:
MEA Meeting 2016 |
10th International Meeting on Substrate-Integrated Electrode Arrays.
doi: 10.3389/conf.fnins.2016.93.00104
Copyright:
The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers.
They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.
The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.
Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.
Received:
22 Jun 2016;
Published Online:
24 Jun 2016.
*
Correspondence:
Dr. Mikel V Calvo, Universidad Nacional de Educación a Distancia (UNED), Departamento de Inteligencia Artificial, Alicante, Spain, mval33@alumno.uned.es