Population heterogeneity, as is generally encountered in primary cultures, reduces measurement yield and limits the efficacy of sensor mass measurements.
These sensors enable the investigation of cellular mass and growth, though previous sensor designs have been limited to the study of homogeneous cell populations. We demonstrate the predictive power of these ESNs on various tasks with cultured and simulated biological neural networks.Microelectromechanical systems (MEMS) resonant sensors provide a high degree of accuracy for measuring the physical properties of chemical and biological samples. This also held for adaptive feed-forward reservoirs, which had no recurrent dynamics. Reservoir adaptation improved the performance of an ESN over readout-only training methods in many cases.
Receiver Operating Characteristic (ROC) curve analysis indicates that these ESNs can imitate the response signal of a reference biological network.
The proposed ESN algorithm learns a predictive model of stimulus-response relations in in vitro and simulated networks, i.e. Specifically, the model is an Echo State Network (ESN) with leaky integrator neurons, whose individual leakage time constants are also adapted. reservoirs, we propose a point process based learning algorithm to train the internal parameters of the reservoir and the connectivity between the reservoir and the memoryless readout neurons.
Employing feed-forward and recurrent networks with fading memory, i.e. for building an artificial system that is functionally equivalent to the reference biological network. We present a reservoir computing approach for functionally identifying a biological neural network, i.e. The complexity of biological neural networks does not allow to directly relate their biophysical properties to the dynamics of their electrical activity.
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