temporal interference

Crowding and the Architecture of the Visual System

Classically, vision is seen as a cascade of local, feedforward computations. This framework has been tremendously successful, inspiring a wide range of ground-breaking findings in neuroscience and computer vision. Recently, feedforward Convolutional Neural Networks (ffCNNs), inspired by this classic framework, have revolutionized computer vision and been adopted as tools in neuroscience. However, despite these successes, there is much more to vision. I will present our work using visual crowding and related psychophysical effects as probes into visual processes that go beyond the classic framework. In crowding, perception of a target deteriorates in clutter. We focus on global aspects of crowding, in which perception of a small target is strongly modulated by the global configuration of elements across the visual field. We show that models based on the classic framework, including ffCNNs, cannot explain these effects for principled reasons and identify recurrent grouping and segmentation as a key missing ingredient. Then, we show that capsule networks, a recent kind of deep learning architecture combining the power of ffCNNs with recurrent grouping and segmentation, naturally explain these effects. We provide psychophysical evidence that humans indeed use a similar recurrent grouping and segmentation strategy in global crowding effects. In crowding, visual elements interfere across space. To study how elements interfere over time, we use the Sequential Metacontrast psychophysical paradigm, in which perception of visual elements depends on elements presented hundreds of milliseconds later. We psychophysically characterize the temporal structure of this interference and propose a simple computational model. Our results support the idea that perception is a discrete process. Together, the results presented here provide stepping-stones towards a fuller understanding of the visual system by suggesting architectural changes needed for more human-like neural computations.

EFFECTS OF STRIATAL TRANSCRANIAL TEMPORAL INTERFERENCE STIMULATION ON HAND MOTOR CONTROL IN STROKE SURVIVORS

FENS Forum 2026

MODULATING AVERSIVE MEMORY FORMATION WITH NON-INVASIVE THETA-BURST TEMPORAL INTERFERENCE STIMULATION

FENS Forum 2026

OPTIMIZATION OF TEMPORAL INTERFERENCE STIMULATION OF DEEP BRAIN TARGETS BASED ON A PRE-COMPUTED TRANSFER MATRIX

FENS Forum 2026

TEMPORAL INTERFERENCE STIMULATION MODULATES NEURON FIRING RATE DEPENDING ON NEURONAL SUBTYPE WITH SYNAPTIC NOISE: AN IN-SILICO STUDY

FENS Forum 2026

INVESTIGATING THE MECHANISM OF TEMPORAL INTERFERENCE STIMULATION USING FUNCTIONAL CALCIUM IMAGING IN HUMAN IPSC-DERIVED NEURONS

FENS Forum 2026

EFFECTS OF HIPPOCAMPAL TRANSCRANIAL TEMPORAL INTERFERENCE STIMULATION ON SPATIAL NAVIGATION AND RELATED BRAIN OSCILLATIONS IN TBI AND HEALTHY INDIVIDUALS

FENS Forum 2026

INCREASING SLOW-OSCILLATION-SPINDLE COUPLING WITH TEMPORAL INTERFERENCE STIMULATION OF THE THALAMUS IN A COMPUTATIONAL MODEL OF DEEP SLEEP

FENS Forum 2026

TARGETING THE INSULA WITH TEMPORAL INTERFERENCE STIMULATION

FENS Forum 2026

DEEP-BRAIN NEUROMODULATION BY TEMPORAL INTERFERENCE STIMULATION ENHANCES SENSORIMOTOR PLASTICITY AFTER EXPERIMENTAL STROKE

FENS Forum 2026

The neural circuit dynamics evoked by temporal interference (TI) electrical neurostimulation in vivo

Biophysical investigation of temporal interference stimulation mechanism