Visual robustness under real-world conditions remains a critical bottleneck for modern reinforcement learning agents. In contrast, biological systems such as mice show remarkable resilience to environ...

This paper discusses how experimental and computational studies integrating multimodal data, such as RNA expression, connectivity and neural activity, are advancing our understanding of the architectu...

Improvements to brain–computer interfaces are bringing the technology closer to natural conversation speed.

Gamma-frequency oscillations are a hallmark of active information processing and are generated by interactions between excitatory and inhibitory neuro…

An accessible undergraduate textbook in computational neuroscience that provides an introduction to the mathematical and computational modeling of neurons an...

Artificial neural networks, central to deep learning, are powerful but energy-consuming and prone to overfitting. The authors propose a network design inspired by biological dendrites, which offe...

This Open Access volume explores advancements in methodologies, efficient code management, and scalable data processing of neuroimaging datasets.

How does the primate brain coordinate plasticity to support its remarkable ability to learn object categories? To address this question, we measured the consequences of category learning on the macaqu...

Machine learning (ML) has become a powerful tool for data analysis, leading to significant advances in neuroscience research. While ML algorithms are proficient in general-purpose tasks, their highly technical nature often hinders their compatibility with the observed biological principles and constraints in the brain, thereby limiting their suitability for neuroscience applications. In this work, we introduce NeuroTorch, a comprehensive ML pipeline specifically designed to assist neuroscientists in leveraging ML techniques using biologically inspired neural network models. NeuroTorch enables the training of recurrent neural networks equipped with either spiking or firing-rate dynamics, incorporating additional biological constraints such as Dale's law and synaptic excitatory-inhibitory balance. The pipeline offers various learning methods, including backpropagation through time and eligibility trace forward propagation, aiming to allow neuroscientists to effectively employ ML approaches. To evaluate the performance of NeuroTorch, we conducted experiments on well-established public datasets for classification tasks, namely MNIST, Fashion-MNIST, and Heidelberg. Notably, NeuroTorch achieved accuracies that replicated the results obtained using the Norse and SpyTorch packages. Additionally, we tested NeuroTorch on real neuronal activity data obtained through volumetric calcium imaging in larval zebrafish. On training sets representing 9.3 minutes of activity under darkflash stimuli from 522 neurons, the mean proportion of variance explained for the spiking and firing-rate neural network models, subject to Dale's law, exceeded 0.97 and 0.96, respectively. Our analysis of networks trained on these datasets indicates that both Dale's law and spiking dynamics have a beneficial impact on the resilience of network models when subjected to connection ablations. NeuroTorch provides an accessible and well-performing tool for neuroscientists, granting them access to state-of-the-art ML models used in the field without requiring in-depth expertise in computer science. ### Competing Interest Statement The authors have declared no competing interest.

Nanocourse: Approaches to Study Neural Circuits This course was taught by Anita Autry, Tiago Gonçalves, and Luke Sjulson at Albert Einstein College of Medici...

The complexity of problems and data in psychiatry requires powerful computational approaches. Computational psychiatry is an emerging field encompassing mechanistic theory-driven models and theoretica...

We’re releasing emg2qwerty and emg2pose—two large datasets and benchmarks for sEMG-based typing and pose estimation, as part of the NeurIPS 2024 Datasets and Benchmarks track.