neuronal models

Introducing dendritic computations to SNNs with Dendrify

Current SNNs studies frequently ignore dendrites, the thin membranous extensions of biological neurons that receive and preprocess nearly all synaptic inputs in the brain. However, decades of experimental and theoretical research suggest that dendrites possess compelling computational capabilities that greatly influence neuronal and circuit functions. Notably, standard point-neuron networks cannot adequately capture most hallmark dendritic properties. Meanwhile, biophysically detailed neuron models are impractical for large-network simulations due to their complexity, and high computational cost. For this reason, we introduce Dendrify, a new theoretical framework combined with an open-source Python package (compatible with Brian2) that facilitates the development of bioinspired SNNs. Dendrify, through simple commands, can generate reduced compartmental neuron models with simplified yet biologically relevant dendritic and synaptic integrative properties. Such models strike a good balance between flexibility, performance, and biological accuracy, allowing us to explore dendritic contributions to network-level functions while paving the way for developing more realistic neuromorphic systems.

Spanning the arc between optimality theories and data

Ideas about optimization are at the core of how we approach biological complexity. Quantitative predictions about biological systems have been successfully derived from first principles in the context of efficient coding, metabolic and transport networks, evolution, reinforcement learning, and decision making, by postulating that a system has evolved to optimize some utility function under biophysical constraints. Yet as normative theories become increasingly high-dimensional and optimal solutions stop being unique, it gets progressively hard to judge whether theoretical predictions are consistent with, or "close to", data. I will illustrate these issues using efficient coding applied to simple neuronal models as well as to a complex and realistic biochemical reaction network. As a solution, we developed a statistical framework which smoothly interpolates between ab initio optimality predictions and Bayesian parameter inference from data, while also permitting statistically rigorous tests of optimality hypotheses.

IMPROVING THE ELECTROPHYSIOLOGICAL FUNCTIONALITY OF HIPSC-DERIVED GLUTAMATERGIC NEURONAL MODELS TO EXAMINE <EM>DE NOVO</EM> POTASSIUM CHANNELOPATHIES

FENS Forum 2026

ALTERED NEUOPHYSIOLOGICAL ACTIVITY OF IPSC-DERIVED NEURONAL MODELS OF 22Q11.2 DELETION SYNDROME

FENS Forum 2026

BRAIN ORGANOIDS AND NEURONAL MODELS TO UNDERSTAND SCHAAF-YANG SYNDROME

FENS Forum 2026

HUMAN INDUCED PLURIPOTENT STEM CELL-DERIVED NEURONAL MODELS OF GABA<SUB >A</SUB> RECEPTOR-RELATED DEVELOPMENTAL AND EPILEPTIC ENCEPHALOPATHIES REVEALED ACCELERATED NEURODEVELOPMENT

FENS Forum 2026

​A STEPWISE 2D-3D SCREENING STRATEGY FOR IDENTIFYING NEUROPROTECTIVE NATURAL COMPOUNDS USING HUMAN NEURONAL MODELS AND MIDBRAIN ORGANOIDS

FENS Forum 2026

Investigating The Effectiveness of Keap1-Nrf2 Protein-Protein Interaction Disruptors in Protecting Human Neuronal Models of Alzheimer’s Disease

Studying mitophagy in neuronal models of alpha-synucleinopathy with the fluorescent MitoRosella reporter

Characterization of neuronal models of glucocerebrosidase deficiency: towards a better understanding of Parkinson's disease