Topic: biophysical models

Examine the principles connecting growth, form and regeneration across organisms. The programme integrates genetics, experiments, theory and computation to investigate gene-regulatory networks, tissue morphogenesis, cell-fate decisions and developmental pathways in disease. Stem-cell models, imaging, omics and biophysical models support discussion of how developmental mechanisms can inform tissue repair and biomedical applications. Talks, posters and discussion connect shared mechanisms across experimental systems. 17–22 January: check-in begins Sunday at 14:00, with scientific introductions

Build scalable computational pipelines for biophysical models of neurons and neural populations. Apply machine learning and statistical inference to neural recordings, construct biologically realistic networks and help shape research directions at an early-stage company working at the intersection of neuroscience and computation. On-site engagement is preferred and international candidates may apply. The advertised salary range is USD 132,000–259,000; no fixed closing date is stated.

Gaute Einevoll explains how biophysical models connect neural activity to extracellular measurements such as spikes, local field potentials and EEG.

Spiking neural networks (SNNs) are characterized by low power consumption, highly parallel computation and event-driven information processing [1]. Many of the concepts that promise to improve the efficiency of SNNs compared to conventional deep neural networks, such as spikes, sparsity, and static suppression, are inspired by our understanding of how the brain processes information. Hence, we can further advance the development of SNNs by improving our current knowledge of how biological neural networks learn through spike-based local plasticity mechanisms. In recent years, many spike-based

Neurons rely on the interplay between dendritic morphology and ion channels to transform synaptic inputs into a sequence of somatic spikes. Detailed biophysical models with active dendrites have been instrumental for exploring this interaction. However, such models can be challenging to understand and validate due to the large number of parameters involved. In this work, we introduce DendroTweaks — a framework designed to illuminate how morpho-electric properties map to dendritic events, and how these dendritic events shape neuronal output. Through its graphical interface, users can access the