Topic: simulation-based inference

Synaptic plasticity is known to be a key player in the brain’s life-long learning abilities. However, due to experimental limitations, the nature of the local changes at individual synapses and their link with emerging network-level computations remain unclear. I will present a numerical, meta-learning approach to deduce plasticity rules from either neuronal activity data and/or prior knowledge about the network's computation. I will first show how to recover known rules, given a human-designed loss function in rate networks, or directly from data, using an adversarial approach. Then I will pr

The importance of dendritic structures for neural computation has been recognized since the early days of neuroscience. However, it is only recently, with the increasing availability of data and advances in modelling techniques, that researchers have begun to map out the precise roles of different branching structures and synaptic distributions [1, 2]. Here, we investigate how the nonlinear integration of excitatory and inhibitory currents in dendritic branches can enhance the ability of a neuron to develop input selectivity and E/I balance via Hebbian synaptic plasticity. We model a single ra

The generation of appropriate parameter configurations for high-dimensional hierarchical spiking neural network models is currently one of the slowest and most frustrating tasks in the computational neuroscience workflow. Moreover, many of the currently used methods for multidimensional parametrization only provide single-point estimates, which do not allow deeper insights into the parameter regions of interest and parameter interactions. Having better knowledge about this would allow neuroscientists to understand more about the network dynamics and the relationships between structure and func

In the awake human brain, alpha-band activity (8–13 Hz) is the most prominent electrophysiological signature. Several models exist for alpha-rhythm generation using neural mass models, including the Jansen-Rit, Moran-David-Friston, Robinson-Rennie-Wright, and Liley-Wright models [1]. Fitting these models traditionally relies on manual parameter tuning or computationally expensive parameter searches, and they rarely account for inter-individual variation. However, there is substantial experimental variability in the propensity for robust oscillatory activity in EEG, e.g., influenced by structur

Xenopus frog metamorphosis offers a unique opportunity to study motor circuit changes as tadpoles transition from tail-based undulatory swimming to synchronous limb-based movements. Xenopus motor circuits change at a cellular level and generate different motor patterns over development, but the mechanisms driving the changes in motor output during metamorphosis are not well understood.Here, we combine behavioral analysis with computational modeling. We first quantify swimming (tadpole and frog) behavior using the tracking software SLEAP, measuring the decrease in frequency of tail movement, an