神经元与认知
Neural networks are high-dimensional nonlinear dynamical systems that process information through the coordinated activity of many connected units. Understanding how biological and machine-learning networks function and learn requires…
Although the latent spaces learned by distinct neural networks are not generally directly comparable, recent work in machine learning has shown that it is possible to use the similarities and differences among latent space vectors to derive…
Maselli et al. do an excellent job highlighting how studying rich, complex, naturalistic behaviors can help neuroscientists and psychologists understand neural and mental phenomena. We fully concur with their arguments, as well as with…
Among the best-known facts of the brain are the contralateral visual, auditory, sensational, and motor mappings in the forebrain. How and why did these evolve? The few theories to this question provide functional answers, such as better…
This study examines the modulatory effect of contextual hedonic olfactory stimuli on the visual processing of neutral faces using event-related potentials (ERPs) and effective connectivity analysis. The aim is to investigate how odors'…
Recent experimental observations have supported the hypothesis that the cerebral cortex operates in a dynamical regime near criticality, where the neuronal network exhibits a mixture of ordered and disordered patterns. However, A…
Evaluating artificial systems for signs of consciousness is increasingly becoming a pressing concern, and a rigorous psychometric measurement framework may be of crucial importance in evaluating large language models in this regard. Most…
Humans are constantly exposed to sequences of events in the environment. Those sequences frequently evince statistical regularities, such as the probabilities with which one event transitions to another. Collectively, inter-event transition…
We present a didactic introduction to spectral Dynamic Causal Modelling (DCM), a Bayesian state-space modelling approach used to infer effective connectivity from non-invasive neuroimaging data. Spectral DCM is currently the most widely…
Recent neuroimaging studies have highlighted the importance of network-centric brain analysis, particularly with functional magnetic resonance imaging. The emergence of Deep Neural Networks has fostered a substantial interest in predicting…
Previous brain decoding research primarily involves single-subject studies, reconstructing stimuli via fMRI activity from the same subject. Our study aims to introduce a generalization technique for cross-subject brain decoding, facilitated…
Anesthetic agents are known to induce a range of alterations in cortical electrophysiological activity, such as the rise of signature patterns, changes in statistical properties, and altered dynamic behavior of neural records. Plenty of…
Propofol, when administered for general anesthesia, induces oscillatory dynamic brain states that are thought to underlie the drug's pharmacological effects. Despite the elucidation of propofol's mechanisms of action at the molecular level,…
Anesthetic agents are neurotropic drugs capable of inducing significant alterations in the thalamocortical system, promoting a profound decrease in awareness and level of consciousness. There is experimental evidence that general anesthesia…
One of the main goals of neuroscience is to understand how an organism's cognitive capacities or physiological states are potentially related to brain activities that involve the interaction of multiple brain structures and cortical areas.…
Several experiments provide evidence that specialized brain regions functionally interact and reveal that the brain processes and integrates information in a specific and structured manner. Networks can be applied to model brain functional…
In the field of neuroscience, the importance of constructing closed-loop experimental systems has increased in conjunction with technological advances in measuring and controlling neural activity in live animals. This paper provides an…
One major challenge of neuroscience is finding interesting structures in a seemingly disorganized neural activity. Often these structures have computational implications that help to understand the functional role of a particular brain…
Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses, but also how long it takes to instantiate the network model in computer…
Artificial neural networks are being proposed as models of parts of the brain. The networks are compared to recordings of biological neurons, and good performance in reproducing neural responses is considered to support the model's…