Publications by Henning Sprekeler

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Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic pla ...

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervise ...

Modeling work in neuroscience can be classified using two different criteria. The first one is the complexity of the model, ranging from simplified conceptual models that are amenable to mathematical analysis to detailed models that require simulations in ...

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning r ...

We present a model for the self-organized formation of place cells, head-direction cells, and spatial-view cells in the hippocampal formation based on unsupervised learning on quasi-natural visual stimuli. The model comprises a hierarchy of Slow Feature An ...

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 ...

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 ...

We develop a group-theoretical analysis of slow feature analysis for the case where the input data are generated by applying a set of continuous transformations to static templates. As an application of the theory, we analytically derive nonlinear visual r ...

In a typical experiment on decision making, one out of two possible stimuli is displayed and observers decide which one was presented. Recently, Stanford and colleagues (2010) introduced a new variant of this classical one-stimulus presentation paradigm to ...

Henning Sprekeler

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Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

Perceptual learning, roving and the unsupervised bias

Theory and Simulation in Neuroscience

Code-specific policy gradient rules for spiking neurons

Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells

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

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

A Theory of Slow Feature Analysis for Transformation-Based Input Signals with an Application to Complex Cells

The Silent Period of Evidence Integration in Fast Decision Making