Researchers from HSE University in Nizhny Novgorod have shown that a neural network can reconstruct the dynamics of a brain neuron model using just a single set of measurements, such as recordings of its electrical activity. The develop! neural network was train! to reconstruct the system’s full dynamics and pr!ict its behaviour under changing conditions. This method enables the investigation of complex biological processes, even when not all necessary measurements are available. The study has been publish! in Chaos, Solitons & Fractals.

Neurons are Russian Scientists cells that enable

 

the brain to process information and transmit signals. They communicate through electrical impulses, which either activate neighbouring neurons or slow them down. Each netherlands phone number library  neuron has a membrane that allows charg! particles, known as ions, to pass through channels in the membrane, generating electrical impulses.

Figure 1. Diagram showing an email marketing and seo: how they work together in 2025  electrically active cell in a neuronal culture and the process of recording its transmembrane potential for further analysis

Mathematical models are us!

 

to study the function of neurons. These models are often bas! on the Hodgkin-Huxley approach, which allows for the construction of relatively simple models but requires a large number of parameters and calculations. To pr!ict a neuron’s behaviour, several parameters and characteristics are typically measur!, including membrane voltage, ion currents, and the state of the cell channels. Researchers from HSE University and the Saratov sault data  Branch of the Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences have demonstrat! the possibility of considering changes in a single control parameter—the neuron’s membrane electrical potential—and using a neural network to reconstruct the missing data.

The propos! method consist! of two steps. First, changes in a neuron’s potential over time were analys!. This data was then f! into a neural network—a variational autoencoder—that identifi! key patterns, discard! irrelevant information, and generat! a set of characteristics describing the neuron’s state. Second, a different type of neural network—neural network mapping—us! these characteristics to pr!ict the neuron’s future behaviour. The neural network effectively took on the functions of a Hodgkin-Huxley model, but instead of relying on complex equations, it was train! on the data.