A sticky Poisson Hidden Markov Model for solving the problem of over-segmentation and rapid state switching in cortical datasets.

The application of hidden Markov models (HMMs) to neural data has uncovered hidden states and signatures of neural dynamics that are relevant for sensory and cognitive processes. However, training an HMM on cortical data requires a careful handling of model selection, since models with more numerous hidden states generally have a higher likelihood on new (unseen) data. A potentially related problem is the occurrence of very rapid state switching after decoding the data with an HMM.

Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits.

Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons have enhanced synaptic connections among groups of neurons forming structures called cell assemblies; such networks are capable of producing metastable dynamics that is in agreement with many experimental results. However, it is unclear how a clustered network structure producing metastable dynamics may emerge from a fully local plasticity rule, i.

Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits.

Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons have enhanced synaptic connections among groups of neurons forming structures called cell assemblies; such networks are capable of producing metastable dynamics that is in agreement with many experimental results. However, it is unclear how a clustered network structure producing metastable dynamics may emerge from a fully local plasticity rule, i.

Differences in Physico-Chemical Properties and Immunological Response in Nanosimilar Complex Drugs: The Case of Liposomal Doxorubicin.

This study aims to highlight the impact of physicochemical properties on the behaviour of nanopharmaceuticals and how much carrier structure and physiochemical characteristics weigh on the effects of a formulation. For this purpose, two commercially available nanosimilar formulations of Doxil and their respective carriers were compared as a case study. Although the two formulations were "similar", we detected different toxicological effects (profiles) in terms of in vitro toxicity and immunological responses at the level of cytokines release and complement activation (iC3b fragment), that could be correlated with the differences in the physicochemical properties of the formulations.

Incidence and Long-term Functional Outcome of Neurologic Disorders in Hospitalized Patients With COVID-19 Infected With Pre-Omicron Variants.

Background And Objectives: A variety of neurologic disorders have been reported as presentations or complications of coronavirus disease 2019 (COVID-19) infection. The objective of this study was to determine their incidence dynamics and long-term functional outcome.

Methods: The Neuro-COVID Italy study was a multicenter, observational, cohort study with ambispective recruitment and prospective follow-up.