Henrik Jörntell

The paper demonstrates how dynamic attractor trajectories in neuronal networks, influenced by sensory input and self-generated activity, enable behavioral flexibility and control through mutual interactions between neural and biomechanical systems.

A cortical-like network learns to process dynamic sensory inputs through autonomous learning, demonstrating robustness to sensor and connection removal.

This study shows D-amphetamine changes ECoG activity patterns in rat neocortex, reducing network organization and affecting multiple dimensions of principal component space.

The paper examines axonal projections of individual cortical neurons in the MouseLight database, finding that single neurons can innervate large proportions of neocortical areas.

The paper demonstrates how brain subnetworks generate dynamic activity patterns through self-governing vector field dynamics, explaining autonomous control of behavior.

This paper introduces a method to quantify neuronal network function using vector fields derived from synaptic weights and neuronal activity, showing how structural richness enables behavioral flexibility.

The paper examines high-dimensional cortical signals in S1 neuron populations and finds they carry multimodal and working memory-like representations.

The paper identifies local field potential sharp waves (LFP-SPWs) in somatosensory cortex and shows they modulate neuronal responses to tactile input differently, with potential global cortical signaling implications.

This study models skin dynamics to improve tactile sensing by incorporating mass into mechanical models, enhancing the separability of tactile inputs.

The paper uses ECoG recordings and PCA to detect changes in global cortical activity distribution following tactile stimulation, showing sensitivity even without somatosensory cortex data.

The paper demonstrates that a spinal cord model can learn rhythmic movements using the BCM learning rule, without requiring rhythm generating neurons or genetically defined connectivity.

The paper introduces the singular superlet transform (SST) to improve time-frequency resolution for separating complex neural signals, showing better performance than existing methods in isolating neuronal spikes and decomposing acoustic data.

The paper shows that visual input alters the temporal spike responses of S1 neurons to tactile input patterns in rats.

The paper shows that hippocampal output significantly affects how SI neurons in rats interpret tactile input patterns based on their current cortical state.

The paper investigates spinal interneuron connectivity in cats and finds that input patterns form a continuum rather than distinct classes.

The paper develops an artificial organism to test if spinal cord connectivity patterns can emerge from learning rather than preprogramming, finding that self-organization can produce functional neural circuits.

The paper shows that remote cortical perturbation alters the network solutions for tactile input responses in neocortical neurons, enabling new response states.

The paper introduces a non-spiking neuron model with dynamic leak to assess stability in recurrent networks, finding it reduces spurious high-frequency noise and scales with network size.

The paper investigates how internal cortical states interact with tactile inputs to produce distinct neuronal responses, revealing a multi-structure attractor dynamics mechanism for perception.

The paper questions the validity of self-inhibitory mechanisms in the half-center model of locomotion control using in vivo data and functional evaluations.