Publications by Jean-Pascal Théodor Pfister

Bayesian regression explains how human participants handle parameter uncertainty

Michael Herzog, Jean-Pascal Théodor Pfister, Mattew Pachai, Maya Anna Jastrzebowska

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 ...

2020

On 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 ...

2020

A 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 ...

National Academy of Sciences2011

STDP 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 Foundation2010

Optimality 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 Press2007

Optimal 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 Press2006

Theory of non-linear spike-time-dependent plasticity

Jean-Pascal Théodor Pfister

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 ...

EPFL2006

Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects

Wulfram Gerstner, Jean-Pascal Théodor Pfister

MIT Press Cambridge2006

Spike-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 Press2005