Krekelberg Neuroscience Laboratory

Our lab's long term goal is to understand how neuronal activity is related to perception. 

We  investigate the neural mechanisms of visual perception and use a number of complementary methods.  Our projects usually start with psychophysical investigations in human subjects that aim to quantify what we really see and to generate hypotheses about the underlying mechanisms.  We use functional imaging to determine which brain areas may be involved and how information flows between areas.  Extracellular recordings from single electrodes and permanently implanted multi-electrode arrays in awake, behaving monkeys allows us to investigate the underlying neural mechanisms. More recently, we have started using transcranial electric stimulation as a way to modulate the brain; this is not only a tool with clinical and/or practical applications, it can also help to understand the causal role of brain areas. Finally, computational methods provide us with a powerful tool to link the different levels of analysis and develop models that lead to testable predictions, and suggestions for new experiments.

Topics of Current Interest: (For recent publications, see Publications)

Perceptual Stability
We move our eyes about 3 times per second. Imagine what a video would look like if you moved the camera that often. How does the brain create a stable percept from its ever changing input?

Saccadic Suppression
When you look into a mirror and move your eyes from left to right and back, you will see that you cannot observe your own eye-movements. This simple experiment demonstrates the phenomenon of saccadic suppression: during saccadic eye movements, visual sensitivity is much reduced. Given that humans make more than 100.000 eye movements each day, it is clear why such a suppression mechanism is needed: without it, the continuous barrage of visual motion on the retina would prevent us from seeing anything at all.

Using psychophysics we have argued that saccadic suppression is dominated by a change in gain of visual detectors; and electrophysiological recordings in the macaque brain show that such gain changes can be found in dorsal areas (MT, MST,VIP, LIP) but also as early as V1. Functional imaging in humans also demonstrated a clear reduction of brain activity in the period surrounding rapid eye movements. This neural correlate of saccadic suppression was clearest in an area (hMT+)

Eye Position Signals
Every time you move your eyes, the retinal image of an object in the world changes dramatically. How then, do we know where things are, even when we move our eyes?

Our work supports the view that the answer lies in the presence of eye position signals in early visual areas. After all, if visual cortex knows where the eyes are pointing, and it knows where an object is on the retina, then it is only a small step to combine these pieces of information and infer where an object is relative to the head and body.

We have shown that the eye position signals present in a distributed manner across areas of the dorsal visual system are precise , accurate, and nimble enough to support perceptual stability most of the time. Sometimes (right around a rapid eye movement) these neural eye position signals are incorrect, and this explains why people report mislocalization errors at these times.

Recurrent Networks
Most cortical processing is dominated by local recurrent feedback; what does this feedback do?

Motion
Most motion models rely on feedforward connectivity. In real cortical tissue, however, neurons are part of many recurrent feedback loops. The model that we are developing starts from this recurrent connectivity and asks how such a network could become direction and speed selective.

To our surprise, we found that it is not only possible to train such a recurrent neural network, the network actually behaves like MT, and like a simple feedforward model in many unexpected ways. We believe this is an interesting example of a system that is inherently strongly nonlinear, but behaves like a linear system when probed in a limited manner.

Orientation
The importance of recurrent connectivity for orientation sensitivity in V1 is relatively well-established, but the consequences of this recurrency for the dynamics of V1 response has received relatively little attention. Our modeling efforts show that recurrence can generate effects that look a lot like short-term adaptation. This is important to better understand adaptation, and may also provide a way to measure effective recurrence in a network

Transcranial Electric Stimulation
Methods based on electric stimulation are gaining acceptance but little is known about how these applied currents affect neural processing. We combine non-invasive (fMRI and psychophysics) and invasive techniques (single cell recordings) to develop a better understanding of this exciting new technique. On the one hand our results show that some effects that were previously thought to be due to the transcranial entrainment of neurons are more likely caused by widespread current flow through the retina. On the other, we have also found the first clear evidence of stimulation-induced changes in adaptation of neurons in visual cortex.