Behavioural experiments to study decision-making in response to context-dependent accumulation of evidence provide testable models that are consistent with the heterogeneity in neural signatures among...

Bayesian models explain how context biases perceptual behavior toward expected categories, but sensory neurons do not reflect this bias. Instead, expectation sharpens sensory acuity, independent of do...

You may never have considered yourself to be one. Why would you? But if you’re reading this, there is more than a likelihood that you are one. If you’re a

Nature Neuroscience - The latent circuit model identifies low-dimensional mechanisms of task execution from heterogenous neural responses. This approach reveals a latent inhibitory mechanism for...

Nature Neuroscience - The default mode network (DMN) is implicated in cognition and behavior. Here, the authors show that the DMN is cytoarchitecturally heterogeneous, it contains regions receptive...

Five of the 10 French grant-winners in the European Research Council's (ERC) 'Proof of Concept' call for 2024 come from the CNRS.

Many artificial neural networks (ANNs) trained with ecologically plausible objectives on naturalistic data align with behavior and neural representations in biological systems. Here, we show that this...

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.

Scientific Reports - Neuronal travelling waves explain rotational dynamics in experimental datasets and modelling

Working memories are thought to be held in attractor networks in the brain. These attractors should keep track of the uncertainty associated with e...