neuronal model

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.

A neuronal model for learning to keep a rhythmic beat

When listening to music, we typically lock onto and move to a beat (1-6 Hz). Behavioral studies on such synchronization (Repp 2005) abound, yet the neural mechanisms remain poorly understood. Some models hypothesize an array of self-sustaining entrainable neural oscillators that resonate when forced with rhythmic stimuli (Large et al. 2010). In contrast, our formulation focuses on event time estimation and plasticity: a neuronal beat generator that adapts its intrinsic frequency and phase to match the extermal rhythm. The model quickly learns new rhythms, within a few cycles as found in human behavior. When the stimulus is removed the beat generator continues to produce the learned rhythm in accordance with a synchronization continuation task.

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.

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

Epigenetic mechanisms of homeostatic plasticity in a human neuronal model system

Generation of a patient specific hIPSC-derived neuronal model for Congenital Central Hypoventilation Syndrome (CCHS)

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

LINE-1 ORF1p is targetting nuclear envelope components in human neuronal model of aging

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

Neuronal impairment and treatment prospects in Smith Magenis syndrome: Findings from patient-specific neuronal model

FENS Forum 2024

Synaptic dysfunction in a hIPSC-derived neuronal model of ALS and FTD

FENS Forum 2024