User profiles for W. F. Podlaski
 | William PodlaskiChampalimaud Research Verified email at research.fchampalimaud.org Cited by 240 |
Training deep neural density estimators to identify mechanistic models of neural dynamics
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of
underlying causes. However, determining which model parameters agree with complex and …
underlying causes. However, determining which model parameters agree with complex and …
Mapping the function of neuronal ion channels in model and experiment
10.7554/eLife.22152.001 Ion channel models are the building blocks of computational
neuron models. Their biological fidelity is therefore crucial for the interpretation of simulations. …
neuron models. Their biological fidelity is therefore crucial for the interpretation of simulations. …
Approximating nonlinear functions with latent boundaries in low-rank excitatory-inhibitory spiking networks
WF Podlaski, CK Machens - arXiv preprint arXiv:2307.09334, 2023 - arxiv.org
Deep feedforward and recurrent rate-based neural networks have become successful
functional models of the brain, but they neglect obvious biological details such as spikes and …
functional models of the brain, but they neglect obvious biological details such as spikes and …
[PDF][PDF] Context-modular memory networks support high-capacity, flexible, and robust associative memories
Context, such as behavioral state, is known to modulate memory formation and retrieval, but
is usually ignored in associative memory models. Here, we propose several types of …
is usually ignored in associative memory models. Here, we propose several types of …
ICGenealogy: Mapping the function of neuronal ion channels in model and experiment
Ion channel models are the building blocks of computational neuron models. Their biological
fidelity is therefore crucial for the interpretability of simulations. However, the number of …
fidelity is therefore crucial for the interpretability of simulations. However, the number of …
Nonlinear computations in spiking neural networks through multiplicative synapses
M Nardin, JW Phillips, WF Podlaski… - Peer Community …, 2021 - peercommunityjournal.org
The brain e ciently performs nonlinear computations through its intricate networks of spiking
neurons, but how this is done remains elusive. While nonlinear computations can be …
neurons, but how this is done remains elusive. While nonlinear computations can be …
Amortised inference for mechanistic models of neural dynamics
…, PJ Gonçalves, C Chintaluri, WF Podlaski… - Computational and …, 2019 - pure.mpg.de
Bayesian statistical inference provides a principled framework for linking mechanistic models
of neural dynamics with empirical measurements. However, for many models of interest, in …
of neural dynamics with empirical measurements. However, for many models of interest, in …
[PDF][PDF] Supplementary Materials: Biological Credit Assignment through Dynamic Inversion of Feedforward Networks
WF Podlaski, CK Machens - proceedings.neurips.cc
Accurate convergence of the backward pass dynamics is crucial for the success of dynamic
inversion. We observe from Eq.(11) that the steady state of δl− 1 depends upon the control …
inversion. We observe from Eq.(11) that the steady state of δl− 1 depends upon the control …
High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating
Biological memory is known to be flexible — memory formation and recall depend on factors
such as the behavioral context of the organism. However, this property is often ignored in …
such as the behavioral context of the organism. However, this property is often ignored in …
[PDF][PDF] Training deep neural density estimators to
…, K Öcal, G Bassetto, C Chintaluri, WF Podlaski… - pure.mpg.de
New experimental technologies allow us to observe neurons, networks, brain regions and
entire systems at un-24 precedented scale and resolution, but using these data to understand …
entire systems at un-24 precedented scale and resolution, but using these data to understand …