Frequency variation analysis in neuronal cultures for stimulus response characterization
In vitro neuronal cultures embodied in a closed-loop control system have been used recently to study neuronal dynamics. This allows the development of neurons in a controlled environment with the purpose of exploring the computational capabilities of such biological neural networks. Due to the intrinsic properties of in vitro neuronal cultures and how the neuronal tissue grows in them, the ways in which signals are transmitted and generated within and throughout the culture can be difficult to characterize. The neural code is formed by patterns of spikes whose properties are in essence nonlinear and non-stationary. The usual approach for this characterization has been the use of the post-stimulus time histogram (PSTH). PSTH is calculated by counting the spikes detected in each neuronal culture electrode during some time windows after a stimulus in one of the electrodes. The objective is to find pairs of electrodes where stimulation in one of the pairs produces a response in the other but not in the rest of the electrodes in other pairs. The aim of this work is to explore possible ways of extracting relevant information from the global response to culture stimulus by studying the patterns of variation over time for the firing rate, estimated from inverse inter-spike intervals, in each electrode. Machine learning methods can then be applied to distinguish the electrode being stimulated from the whole culture response, in order to obtain a better characterization of the culture and its computational capabilities so it can be useful for robotic applications.
KeywordsMEA Dissociated neurons Neuronal stimulation Hybrots Machine learning
We want to acknowledge Programa de Ayudas a Grupos de Excelencia de la Región de Murcia, from Fundación Séneca, Agencia de Ciencia y Tecnología de la Región de Murcia.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All the experimental procedures were conformed to the directive 2010/63/EU of the European Parliament and the RD 53/2013 Spanish regulation, and approved by the Miguel Hernandez University Committee for Animal Research with reference number UMH.IB.EFJ.03.13. In addition, Directorate-General for Agricultural Production and Livestock of Generalitat Valenciana, Spain, authorized these procedures with code 2014/VSC/PEA/00110.
- 4.Xydas D, Norcott D, Warwick K, Whalley B, Nasuto S, Becerra V, Hammond MW, Downes J, Marshall S (2008) Architecture for neuronal cell control of a mobile robot. In: Bruyninckx H, Přeučil L, Kulich M (eds) European Robotics Symposium 2008. Springer, pp 23–31Google Scholar
- 5.Braitenberg V (1984) Vehicles: experiments in synthetic psychology. The MIT Press, CambridgeGoogle Scholar
- 7.Novellino A, D’Angelo P, Cozzi L, Chiappalone M, Sanguineti V, Martinoia S (2007) Connecting neurons to a mobile robot: an in vitro bidirectional neural interface. Comput Intell Neurosci 2007 (Article ID 12725). https://www.hindawi.com/journals/cin/2007/012725/cta/
- 10.Bakkum DJ, Shkolnik AC, Ben-Ary G, Gamblen P, DeMarse TB, Potter SM (2004) Removing some ‘A’ from AI: embodied cultured networks. In: Iida F, Pfeifer R, Steels L, Kuniyoshi Y (eds) Embodied artificial intelligence. Lecture notes in computer science, vol 3139. Springer, Berlin, pp 130–145Google Scholar
- 11.Tessadori J, Venuta D, Kumar SS, Bisio M, Pasquale V, Chiappalone M (2013) Embodied neuronal assemblies: a closed-loop environment for coding and decoding studies. In: 6th International IEEE/EMBS Conference on Neural Engineering (NER), pp 899–902Google Scholar
- 13.Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second international conference on knowledge discovery and data mining (KDD-96). AAAI Press, pp 226–231Google Scholar
- 14.Rosenblatt F (1957) The perceptron: a perceiving and recognizing automaton. Techreport 85-460-1, Cornell Aeronautical LaboratoryGoogle Scholar
- 15.von Neumann J (1952) Probabilistic logics and the synthesis of reliable organisms from unreliable components. Lectures delivered at the California Institute of Technology. Notes by R. S. PierceGoogle Scholar