Neuronal Maximum a Posteriori Estimation on Microelectrode Arrays

Field: Neuroscience (Life Sciences)

Event Abstract Back to Event Neuronal Maximum a Posteriori Estimation on Microelectrode Arrays Takuya Isomura1*, Kiyoshi Kotani2 and Yasuhiko Jimbo3 1 The University of Tokyo, Dept of Human and Engineered Environmental Studies, Japan 2 The University of Tokyo, Research Center for Advanced Science and Technology, Japan 3 The University of Tokyo, Dept Precision Eng, Japan Motivation To date, a candidate unified theory of the higher brain functions has been proposed [1], and such functions are hypothesized to be explained using machine-learning-like algorithms. However, it remains unclear whether and how living neural networks implement algorithms such as these. A cultured neural network is one of the simplest networks consisting of living cells, and exhibits stimulation-pattern dependent synaptic plasticity [2,3] and performs several learning tasks including supervised learning [4], adaptation to inputs [5], pattern recognition [6], logical calculation [7], and short-term memory [8]. These reports imply that cultured neural networks can learn the statistical properties of inputs in a similar way to machine learning, at least when tasks are easy. Here, we explicitly show the capability of cultured neural networks by examining whether they can perform blind source separation (BSS) and maximum a posteriori (MAP) estimation of input sequences. Material and Methods All animal experiments were performed with the approval of the animal experiment ethics committee at the University of Tokyo (approval number, C-12-02, KA-14-2) and according to the University of Tokyo guidelines for the care and use of laboratory animals. The procedure for preparing dissociated cultures of cortical neurons was based on a modified version of the procedure described in a previous study [2]. Rat cortical cells were dissociated and cultured on microelectrode arrays (MEAs) which have 8×8 microelectrodes embedded in the bottom of the dishes. Electrical stimulations and recordings were performed using a custom MEA system. We performed the BSS task by preparing two lines of hidden sources and induced their mixture into neural networks [9]. The MAP estimation task was carried out by inducing sequential stimulations that consist of four spatial patterns with a 20-ms interval. A typical stimulation pattern set, which is shown in Fig. 1A, is constructed from 4 random patterns and displayed on 8 × 8 distributed electrodes of MEA. Colored sites are stimulated electrodes. We termed these groups of electrodes included in each pattern as Group 1, 2, 3, and 4. Between trails, single pulses were induced from Group 1 electrodes (black) as the test stimulation. The respective BSS and MAP estimation capabilities were evaluated through Kullback-Leibler divergence (KLD) and response deviation. Note that the deviation was calculated as the difference between the evoked spike numbers recorded at a specific electrode and the all-electrode average. Results We previously showed that cultured neural networks could separate sources from mixed inputs, which was observed through the increase in KLDs after training [9]. That is, neurons learned to represent particular sources while filtering out others. A well-known example of BSS in the brain is the cocktail party problem, but its neural mechanism is not determined. Our results showed that BSS could be performed even at the neural network level. Next, we investigated whether cultured neural networks can predict the dynamics of external inputs (predictive coding; PC). PC as well as BSS is crucial to sensory perception. Repetitive sequential patterned stimulations were induced and the long-term transitions of the evoked responses to the test stimulation from Group 1 electrodes were monitored. The transitions (the change from baseline) are plotted in Fig. 1B, where the black, red, green, and blue circles indicate deviations of Groups 1, 2, 3, and 4, respectively. After training, the neural response deviations corresponding to electrodes included in the most likely subsequent pattern (Group 2; red circles in Fig. 1B) increased. In contrast, those of Groups 3 (green) and 4 (blue) decreased. At the end of the experiment (the mean of trial 96 to 100), there were significant differences between Groups 2 and 3 (*, p = 0.015) and Groups 2 and 4 (**, p = 0.0052). These data were obtained from n = 19 cultures and the Mann-Whitney U test was used. These changes suggest that cultured neurons performed MAP-estimation-like learning of the input sequence through activity-dependent synaptic plasticity. Discussion The neuronal capabilities of spatial BSS and temporal MAP estimation were investigated. Several mathematical models of neuronal BSS and MAP estimation based on Hebbian rule and spike-timing dependent plasticity have been proposed. The use of living neural networks is a unique point of our studies, and our results may be useful to examine biological plausibility of these algorithms and develop a novel biologically plausible algorithm. Conclusion We observed that cultured neural networks performed spatial BSS and temporal MAP-like estimation, which suggests neuronal self-organization by which cultured neural networks were able to predict input statistics. These results imply the existence of common learning mechanisms in machine learning and cultured neural networks. Acknowledgements This work was partially supported by the JSPS through KAKENHI (26560202) and Grant-in-Aid for JSPS Fellows (26-8435). References (1) Friston, K. (2010). The free-energy principle: a unified brain theory?. Nat Rev Neurosci, 11(2), 127-138. (2) Jimbo, Y., Tateno, T., & Robinson, H. P. C. (1999). Simultaneous induction of pathway-specific potentiation and depression in networks of cortical neurons. Biophys J, 76(2), 670-678. (3) Johnson, H. A., Goel, A., & Buonomano, D. V. (2010). Neural dynamics of in vitro cortical networks reflects experienced temporal patterns. Nat Neurosci, 13(8), 917-919. (4) Shahaf, G., & Marom, S. (2001). Learning in networks of cortical neurons. J Neurosci, 21(22), 8782-8788. (5) Eytan, D., Brenner, N., & Marom, S. (2003). Selective adaptation in networks of cortical neurons. J Neurosci, 23(28), 9349-9356. (6) Ruaro, M. E., Bonifazi, P., & Torre, V. (2005). Toward the neurocomputer: image processing and pattern recognition with neuronal cultures. Biomed Eng, IEEE Trans, 52(3), 371-383. (7) Feinerman, O., Rotem, A., & Moses, E. (2008). Reliable neuronal logic devices from patterned hippocampal cultures. Nat Phys, 4(12), 967-973. (8) Dranias, M. R., Ju, H., Rajaram, E., & VanDongen, A. M. (2013). Short-term memory in networks of dissociated cortical neurons. J Neurosci, 33(5), 1940-1953. (9) Isomura, T., Kotani, K., & Jimbo, Y. (2015). Cultured Cortical Neurons Can Perform Blind Source Separation According to the Free-Energy Principle. PLoS Comput Biol, 11(12), e1004643. Figure Legend Figure 1. Schematic images of input patterns (A) and transitions of neural response deviations (B). Figure 1 Keywords: machine learning, blind source separation, Maximum a posteriori estimation, Cortical neural network Conference: MEA Meeting 2016 | 10th International Meeting on Substrate-Integrated Electrode Arrays, Reutlingen, Germany, 28 Jun - 1 Jul, 2016. Presentation Type: oral Topic: MEA Meeting 2016 Citation: Isomura T, Kotani K and Jimbo Y (2016). Neuronal Maximum a Posteriori Estimation on Microelectrode Arrays. Front. Neurosci. Conference Abstract: MEA Meeting 2016 | 10th International Meeting on Substrate-Integrated Electrode Arrays. doi: 10.3389/conf.fnins.2016.93.00120 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. Takuya Isomura, The University of Tokyo, Dept of Human and Engineered Environmental Studies, Tokyo, Japan, isomura@neuron.t.u-tokyo.ac.jp Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Takuya Isomura Kiyoshi Kotani Yasuhiko Jimbo Google Takuya Isomura Kiyoshi Kotani Yasuhiko Jimbo Google Scholar Takuya Isomura Kiyoshi Kotani Yasuhiko Jimbo PubMed Takuya Isomura Kiyoshi Kotani Yasuhiko Jimbo Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.