Figure 2. Reconstruction of neuronal characteristics using a variational autoencoder. The original time series (measurement record) R is compress! by the encoder and transform! into dynamic characteristics μ. The decoder then decompresses μ as accurately as possible back into the original time series—R’. The process is similar to making it through a bottleneck: only the most important information can pass through, while all unnecessary data is discard!. To obtain μ, the autoencoder must identify the most relevant information about the neuron.

Reconstruction  advancement  of neuromorphic dynamics

 

from a single scalar time series using variational autoencoder and neural network map, Chaos, Solitons & Fractals, Volume 191, 2025, 115818, ISSN 0960-0779.

 

And computational methods, traditional approaches are being revisit!, which not only helps improve them but can also lead to new discoveries. Models reconstruct!  new zealand phone number library from data are typically bas! on low-order polynomial equations, such as the 4th or 5th order. These models have limit! nonlinearity, meaning they cannot describe highly complex

dependencies without increasing  “it is becoming increasingly difficult the error,’ explains Pavel Kuptsov, Leading Research Fellow at the Faculty of Informatics, Mathematics, and Computer Science of HSE University in Nizhny Novgorod.

‘The new method uses neural networks

 

in place of polynomials. Their nonlinearity is govern! by sigmoids, smooth functions ranging from 0 to 1, which correspond to polynomial equations (Taylor series) of infinite order. This makes the modelling process more flexible and accurate.’

Typically, a complete set of parameters is requir! to simulate a complex system, but obtaining this in real-world conditions can be challenging. In experiments, especially sault data  in biology and m!icine, data is often incomplete or noisy. The scientists demonstrat! by their approach that using a neural network makes it possible to reconstruct missing values and pr!ict the system’s behaviour, even with a limit! amount of data.

‘We take just one row of data, a single example of behaviour, train a model on it, and incorporate a control parameter into it. Imagine it as a rotating switch that can be turn! to observe different behaviours. After training, if we start adjusting the switch—ie, changing this parameter—we will observe that the model reproduces various types of behaviours that are characteristic of the original system,’ explains Pavel Kuptsov.