Wolfgang Maass

The paper develops a neural network model integrating cognitive maps, stochastic computing, and compositional coding to enable flexible planning and problem-solving in energy-efficient hardware.

The paper identifies how sparse cortical activity in mouse V1 is organized into flexible, condition-dependent spike-order codes through a combination of experimental data and a computational model.

The paper presents a deep-learning-assisted simulator of a mouse primary visual cortex model, integrating multimodal data to study circuit function and synaptic organization.

This paper develops a lightweight algorithm inspired by brain's contextual encoding of episodic memories for efficient goal-directed action selection.

The paper introduces BTSP, a mechanism for binding distributed representations that enables flexible retrieval, and demonstrates its applications in visual encoding, hierarchical binding, and NLP.

The paper links cognitive maps, successor-relation based reinforcement learning, and BTSP by showing how neural codes encode sequential structure and enable plan generation.

This paper presents a theoretical framework showing neurons can learn and represent order relations through synaptic plasticity, explaining multiple experimental findings and enabling efficient inference.

This paper shows that biologically realistic spiking neural networks can learn from few examples by storing priors in synaptic weights and using network state for rapid information absorption.

The paper shows that local synaptic plasticity enables neural networks to learn cognitive maps for planning, achieving performance comparable to AI offline planning algorithms without requiring backpropagation or large datasets.

The paper trains large-scale models of cortical microcircuits to understand their computational organization and finds they match brain performance and reproduce unique experimental traits.

The paper analyzes a large-scale V1 model using anatomical and neurophysiological data to show it can robustly multiplex visual tasks and exhibit brain-like noise resilience.

This work analyzes a detailed cortical microcircuit model's ability to solve an image-change-detection task and identifies its computational strategy.

The paper shows spike frequency adaptation improves spiking neural network performance on temporally dispersed tasks, reaching human brain levels.

This paper reviews spike-based symbolic computations in neural networks and shows small spiking networks can perform symbolic computations on bit sequences and numbers using biologically plausible learning rules.

The paper shows that networks of spiking neurons can have innate computational capabilities through their connectivity structure, not synaptic plasticity, when they include diverse neuron types.

The paper proposes a normative model where top-down inputs in apical dendrites provide probabilistic priors for sensory information, consistent with synaptic plasticity and NMDA-spikes.

This paper introduces a new learning method for spiking neural networks that enables biologically plausible online learning through gradient descent, achieving performance comparable to BPTT.

This paper introduces a new learning approach for spiking neural networks that enables one-shot learning through local synaptic plasticity, inspired by dopamine-based learning signals in the brain.

The paper proposes a computational model of brain function called the Assembly Calculus, which describes how neuronal assemblies perform operations like projection, association, and pattern-completion to support cognitive functions.

The paper investigates how spike-timing-dependent plasticity (STDP) enables the formation of associations between memory traces in spiking neuron networks, showing it can reproduce both fast overlap-based and new-assembly-based memory association patterns.

The paper discusses computational principles of brain computation using experimental data constraints from neuroscience methods.