Automatic adaptation of model neurons and connections to build hybrid circuits with living networks

Abstract

Hybrid circuits built by creating mono-or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this paper, we present a set of algorithms for the automatic adaptation of model neurons and connections in the creation of hybrid circuits with living neural networks. The algorithms perform model time and amplitude scaling, drift compensation, goal-driven synaptic and model tuning/calibration and also automatic parameter mapping. These algorithms have been implemented in RTHybrid, an open-source library that works with hard real-time constraints. We provide validation examples by building hybrid circuits in a central pattern generator. The results of the validation experiments show that the proposed dynamic adaptation facilitates building hybrid circuits and closedloop communication among living and artificial model neurons and connections. Furthermore contributes to characterize system dynamics, achieve control, automate experimental protocols and extend the lifespan of the preparations.

RTHybrid: A Standardized and Open-Source Real-Time Software Model Library for Experimental Neuroscience

“…Communication with DAQ devices is achieved through Comedi open-source drivers for National Instruments' and several other manufacturers' hardware (or Analogy drivers in the case of Xenomai) (Schleef et al, 2012 ). Automatic calibration and experiment automation algorithms are included to deal with the differences between models and living neurons in terms of temporal scale and amplitude (Reyes-Sanchez et al, 2018 ). They also cover other possible experimental complications such as the presence of signal drift.…”

“…Table 4 shows the currently included neuron models, which have been selected due to their rich intrinsic dynamics and suitability to build hybrid circuits. All of them have different characteristics and require also specific adaptations to work in real-time, which are handled automatically by auto-calibration algorithms (Reyes-Sanchez et al, 2018 ). Several numerical integration methods can be chosen by the user, including Euler, Heun, Order 4 Runge-Kutta (Press et al, 1988 ) and (6)5 Runge-Kutta (Hull et al, 1972 ).…”

“…Neuron model parameters were set to produce bursting behavior and the duration of each burst was set to automatically match the living neuron activity. Signal amplitude and temporal scaling was automatically performed by RTHybrid calibration algorithms (Reyes-Sanchez et al, 2018 ). Validation tests are shown in Figures 8 , 9 .…”

“…All of them are adapted to work under hard real-time restrictions and additional neuron, network and synapse models can be easily added using C language. Moreover, calibration algorithms are integrated in the library to automate the adaptation of model amplitude and time scales to the living neuron behavioral range (Reyes-Sanchez et al, 2018 ). Core isolation can also be employed as validated in the tests reported in this paper.…”