15 citations · 48 across the 18 of their papers we have counts for
8 papers · 1 filter
Dendritic structure enables powerful plasticity
Ben von Hünerbein, Federico Benitez, Kevin Max +10
Over the past decades, it has become increasingly clear that the complex morphology of cortical neurons is more than just a quirk of evolution, and that dendritic compartments serv…
Learning as filtering: implications for spike-based plasticity
Jannes Jegminat, Jean-Pascal Pfister
Most normative models in computational neuroscience describe the task of learning as the optimisation of a cost function with respect to a set of parameters. However, learning as o…
Bayesian regression explains how human participants handle parameter uncertainty
Jannes Jegminat, Maya Jastrzebowska, Matt Pachai +2
The human brain copes with sensory uncertainty in accordance with Bayes' rule. However, it is unknown how the brain makes predictions in the presence of parameter uncertainty. Here…
Propagation of spiking moments in linear Hawkes networks
Matthieu Gilson, Jean-Pascal Pfister
The present paper provides exact mathematical expressions for the high-order moments of spiking activity in a recurrently-connected network of linear Hawkes processes. It extends p…
On the choice of metric in gradient-based theories of brain function
Simone Carlo Surace, Jean-Pascal Pfister, Wulfram Gerstner +1
The idea that the brain functions so as to minimize certain costs pervades theoretical neuroscience. Since a cost function by itself does not predict how the brain finds its minima…
Particle-filtering approaches for nonlinear Bayesian decoding of neuronal spike trains
Anna Kutschireiter, Jean-Pascal Pfister
The number of neurons that can be simultaneously recorded doubles every seven years. This ever increasing number of recorded neurons opens up the possibility to address new questio…
A statistical model for in vivo neuronal dynamics
Simone Carlo Surace, Jean-Pascal Pfister
Single neuron models have a long tradition in computational neuroscience. Detailed biophysical models such as the Hodgkin-Huxley model as well as simplified neuron models such as t…
Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing
Jean-Pascal Pfister, Taro Toyoizumi, David Barber +1
In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimiz…