Publications by Wulfram Gerstner

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The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienen- ...

Experimental measurements of pairwise connection probability of pyramidal neurons together with the distribution of synaptic weights have been used to construct randomly connected model networks. However, several experimental studies suggest that both wiri ...

We propose a simple method to map a generic threshold model, namely the Spike Response Model, to artificial data of neuronal activity using a minimal amount of a priori information. Here, data are generated by a detailed mathematical model of neuronal activity. The model neuron is driven with in-vivo-like current injected, and we test to which extent it is possible to predict the spike train of the detailed neuron model from that of the Spike Response Model. In particular, we look at the number of spikes co

Action Potential (APs) patterns of sensory cortex neurons encode a variety of stimulus features, but how can a neuron change the feature to which it responds? Here, we show that in vivo a spike-timing-dependent plasticity (STDP) protocol-consisting of pair ...

Cortical neurons receive balanced excitatory and inhibitory synaptic currents. Such a balance could be established and maintained in an experience-dependent manner by synaptic plasticity at inhibitory synapses. We show that this mechanism provides an expla ...

The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent sp ...

Novelty signals in the brain drive exploration and learning. While the perceived novelty of a stimulus is known to depend on previous experience, it remains elusive how generalization between familiar and novel stimuli impacts novelty computation. Specifically, existing models of novelty computation fail to account for the effects of stimulus similarities that are abundant in naturalistic tasks. Here, we present a biologically plausible model that captures how stimulus similarities modulate novelty signals

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 ...

The response of a neuron to a time-dependent stimulus, as measured in a Peri-Stimulus-Time-Histogram (PSTH), exhibits an intricate temporal structure that reflects potential temporal coding principles. Here we analyze the encoding and decoding of PSTHs for ...

Perceptual learning is reward-based. A recent mathematical analysis showed that any reward-based learning system can learn two tasks only when the mean reward is identical for both tasks [Frémaux, Sprekeler and Gerstner, 2010, The Journal of Neuroscience, 30(40): 13326-13337]. This explains why perceptual learning fails when two differing stimulus types are presented randomly interleaved from trial to trial (i.e. roving), even though learning occurs efficaciously when the stimuli are presented in separate s

Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulators on synaptic plastic ...

Synaptic plasticity is thought to be the neuronal correlate of learning. Moreover, modification of synapses contributes to the activity-dependent homeostatic maintenance of neurons and neural networks. In this chapter, we review theories of synaptic plasticity and show that both homeostatic control of activity and detection of correlations in the presynaptic input can arise from spike-timing dependent plasticity (STDP). Relations to classical rate-based Hebbian learning are discussed.

Synaptic plasticity is considered to be the biological substrate of learning and memory. In this document we review phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP). The aim of ...

We derive and study two-dimensional generalizations of integrate-and-fire models which can be found from a piecewise linear idealization of the FitzHugh-Nagumo or Morris-Lecar model. These models give rise to new properties not present in one-dimensional integrate-and-fire models. A detailed analytical study of the models is presented. In particular, (i) for the piecewise linear FitzHugh-Nagumo model, we determine analytically the bistability regime between stationary solutions and oscillations, that is, ty

We link Ivancovsky et al.'s novelty-seeking model (NSM) to computational models of intrinsically motivated behavior and learning. We argue that dissociating different forms of curiosity, creativity, and memory based on the involvement of distinct intrinsic ...

Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we d ...

Here, we provide model simulation data accompanying the analysis of neural and behavioral data in our article 'Representational similarity modulates neural and behavioral signatures of novelty'. This data set contains (i) simulations of similarity-based novelty models for fitting of V1 novelty responses: grid_search_results.zip (ii) simulations for parameter robustness analysis of similarity-based novelty, fit to V1 novelty responses: gridsearch_robustness_width.zip (iii) simulations of novelty-RL agents

Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical resul ...

Recent experiments have shown that spike-timing-dependent plasticity is influenced by neuromodulation. We derive theoretical conditions for successful learning of reward-related behavior for a large class of learning rules where Hebbian synaptic plasticity ...

Wulfram Gerstner

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Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation

Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons

The Spike Response Model: A Framework to Predict Neuronal Spike Trains

Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo

Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

Stochastic variational learning in recurrent spiking networks

Representational similarity modulates neural and behavioral signatures of novelty

Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing in Supervised Learning

Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram

Voltage and spike timing interact in STDP – a unified model

Perceptual Learning, Roving and the Unsupervised Bias

Neuromodulated-Spike-Timing-Dependent Pasticity, and Theory of Three-Factor Learning Rules

Homeostasis and Learning through Spike-Timing Dependent Plasticity

Phenomenological models of synaptic plasticity based on spike timing

Piecewise linear differential equations and integrate-and-fire neurons : insights from two-dimensional membrane models

Computational models of intrinsic motivation for curiosity and creativity

Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models

Representational similarity modulates neural and behavioral signatures of novelty: Model simulations

High-performance deep spiking neural networks with 0.3 spikes per neuron

Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity