Person
Jean-Pascal Théodor Pfister
Related publications (11)
Bayesian regression explains how human participants handle parameter uncertainty
Michael Herzog, Jean-Pascal Théodor Pfister, Maya Anna Jastrzebowska, Mattew Pachai
Author summary How do humans make prediction when the critical factor that influences the quality of the prediction is hidden? Here, we address this question by conducting a simple psychophysical experiment in which participants had to extrapolate a parabo ...
2020On the choice of metric in gradient-based theories of brain function
Wulfram Gerstner, Johanni Michael Brea, Jean-Pascal Théodor Pfister
The idea that the brain functions so as to minimize certain costs pervades theoretical neuroscience. Because a cost function by itself does not predict how the brain finds its minima, additional assumptions about the optimization method need to be made to ...
2020A triplet spike-timing-dependent plasticity model generalizes the Bienenstock-Cooper-Munro rule to higher-order spatiotemporal correlations
Jean-Pascal Théodor Pfister, Claudia Clopath, Juliette Audet
Synaptic strength depresses for low and potentiates for high activation of the postsynaptic neuron. This feature is a key property of the Bienenstock-Cooper-Munro (BCM) synaptic learning rule, which has been shown to maximize the selectivity of the postsyn ...
2011STDP in adaptive neurons gives close-to-optimal information transmission
Wulfram Gerstner, Jean-Pascal Théodor Pfister, Guillaume Hennequin
Spike-frequency adaptation is known to enhance the transmission of information in sensory spiking neurons by rescaling the dynamic range for input processing, matching it to the temporal statistics of the sensory stimulus. Achieving maximal information tra ...
Frontiers Research Foundation2010Optimality Model of Unsupervised Spike-Timing Dependent Plasticity: Synaptic Memory and Weight Distribution
Wulfram Gerstner, Jean-Pascal Théodor Pfister, Taro Toyoizumi
We studied the hypothesis that synaptic dynamics is controlled by three basic principles: (1) synapses adapt their weights so that neurons can effectively transmit information, (2) homeostatic processes stabilize the mean firing rate of the postsynaptic ne ...
Massachusetts Institute of Technology Press2007Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing in Supervised Learning
Wulfram Gerstner, Jean-Pascal Théodor Pfister, Taro Toyoizumi
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 optimizes by gradient ascent the likelihood of postsynaptic firing at one or severa ...
Massachusetts Institute of Technology Press2006Theory of non-linear spike-time-dependent plasticity
A fascinating property of the brain is its ability to continuously evolve and adapt to a constantly changing environment. This ability to change over time, called plasticity, is mainly implemented at the level of the connections between neurons (i.e. the s ...
EPFL2006Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity
Wulfram Gerstner, Jean-Pascal Théodor Pfister
Classical experiments on spike timing-dependent plasticity (STDP) use a protocol based on pairs of presynaptic and postsynaptic spikes repeated at a given frequency to induce synaptic potentiation or depression. Therefore, standard STDP models have express ...
2006Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model
Wulfram Gerstner, Jean-Pascal Théodor Pfister, Taro Toyoizumi
We derive an optimal learning rule in the sense of mutual information maximization for a spiking neuron model. Under the assumption of small fluctuations of the input, we find a spike-timing dependent plasticity (STDP) function which depends on the time co ...
MIT Press2005Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission
Wulfram Gerstner, Jean-Pascal Théodor Pfister, Taro Toyoizumi
Maximization of information transmission by a spiking-neuron model predicts changes of synaptic connections that depend on timing of pre- and postsynaptic spikes and on the postsynaptic membrane potential. Under the assumption of Poisson firing statistics, ...
2005