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.

Modulation of neuronal dynamics by sustained and activity-dependent continuous-wave near-infrared laser stimulation

“…The stimulation was triggered when the area reached a specific threshold, which was predicted as in the voltage case, or hand-tuned. For the detection of the minimum point to reset the voltage area, we used an RTXI module based on RTHybrid, a real-time software that includes automatic adaptation algorithms to handle the ongoing variability of the recordings 36 , 37 , 40 available in a GitHub repository at: https://github.com/GNB-UAM/rthybrid-for-rtxi/tree/master/rthybrid_burst_analysis. The use of each mode of the protocol in the experiment was decided depending on the specific requirements of the recorded activity.…”

“…However, some clinical studies have successfully applied continuous-wave (CW) laser for brain stimulation. 23 The use of closed-loop techniques has a large potential in neuroscience, for both physiological and clinical research studies, 2,[31][32][33][34][35][36][37] since they allow adjusting the stimulation to the context of the ongoing neural dynamics and the specific condition of the targeted system/subject. Some of these tools have been developed with open-source approaches, including optical techniques, e.g.…”

A Raspberry Pi Based Hardware Implementations of Various Neuron Models

“…Hard real-time constraints have been overcome thanks to the algorithm that provides Auto-Sync. The open source algorithm also provides time and amplitude adjustment in real time in Linux environment (Reyes-Sanchez et al 2020). Gomar et al realized a Central Pattern Generator (CPG) structure which consisted of HR neurons on an FPGA using the PWL method, both with and without a multiplier.…”