Our research

Visual attention

Our long standing  interest are the perceptual consequences and neural correlates of visual attention. Here, we are particularly interested in visual experience outside the current attention focus. Currently, we are studying the relation between attention and motion grouping and the neural responses to stimuli outside the attention focus (using fMRI).

Enhanced brain activity under dual-task conditions

• Festman, Yariv, and Braun, Jochen. Feature-based attention spreads preferentially in an object-specific manner. Vision Research.
• Houtkamp R, Braun J. Cortical response to task-relevant stimuli presented outside the primary focus of attention. Journal of cognitive neuroscience. 2010;22:1980–92.
• Festman Y, Braun J. Does feature similarity facilitate attentional selection?. Attention, perception & psychophysics. 2010;72:2128–43.
• Pastukhov A, Braun J. Rare but precious: Microsaccades are highly informative about attentional allocation. Vision research. 2010;50:1173–1184
• Pastukhov A, Fischer L, Braun J. Visual attention is a single, integrated resource. Vision research. 2009;49:1166–73.

Multistable perception

Certain visual displays are not perceived in a stable way but, from time to time and seeemingly spontaneously, their appearance wavers and settles in a distinctly different form. This phenomenon is called multistable perception and occurs with a variety of visual displays (as well as with ambiguous stimuli in the auditory and tactile domains). By studying the details of this perceptual dynamic, we can learn much about the nature of perceptual representations. For example, it seems that bistable perception reflects competing processes of stochastic integration at two levels of representation. Helping to interpret these results are computational models developed by our collaborators in Rome and Barcelona.

Kinectic-depth effect stimulus

• Pastukhov, Alexander, Vonau, Victoria, and Braun, Jochen. Believable change: bistable reversals are governed by physical plausibility. Journal of vision.
• Hudak, M., Gervan, P., Friedrich, B., Pastukhov, A., Braun, J., & Kovacs, I. (2011). Increased readiness for adaptation and faster alternation rates under binocular rivalry in children. Frontiers in Human Neuroscience, 5(128). doi:10.3389/fnhum.2011.00128
• Pastukhov A, Braun J. Cumulative history quantifies the role of neural adaptation in multistable perception. Journal of Vision. 2011;11.
• Braun J, Mattia M. Attractors and noise: twin drivers of decisions and multistability. NeuroImage. 2010;52:740–51.
• Pastukhov A, Braun J. A short-term memory of multi-stable perception. Journal of vision. 2008;8:7.1–14.
• Pastukhov A, Braun J. Perceptual reversals need no prompting by attention. Journal of vision. 2007;7:5.1–17.

Neuromorphic VLSI

We collaborate with partners in Rome and Zurich on a hardware implementation of spiking neurons and plastic synapses (neuromorphic VLSI). Our immediate aim is to port established models of plasticity, learning, or memory to this platform and to thereby extend the applicability of neuromorphic devices. More philosophically, we believe that ‘building brains’ is a necessary precondition for ‘understanding brains.

• Giulioni M, Camilleri P, Mattia M, Dante V, Braun J, and Del Giudice P. Robust working memory in an asynchronously spiking neural network realized in neuromorphic VLSI. Frontiers in Neuroscience
• Camilleri P, Giulioni M, Mattia M, Braun J, Del Giudice P. Self-sustained activity in attractor networks using neuromorphic VLSI. In: The 2010 International Joint Conference on Neural Networks (IJCNN): 1–6.
• Camilleri P, Giulioni M, Mattia M, Braun J, Del Giudice P. Attractor Dynamics in VLSI. In: BCCN Frankfurt, 30 Sep-1 Oct 2009.
• Giulioni M, Camilleri P, Dante V, et al. A VLSI network of spiking neurons with plastic fully configurable “stop-learning” synapses. In: 2008 15th IEEE International Conference on Electronics Circuits and Systems. 2008:678–681.
• Camilleri P, Giulioni M, Dante V, et al. A Neuromorphic aVLSI network chip with configurable plastic synapses. In: 7th International Conference on Hybrid Intelligent Systems (HIS 2007) :296–301.

Synapse layout on chip

Synapse circuit with Hebb-like plasticity

Simulation of neural populations interconnected by plastic synapses

Learning of goal-directed behaviour

More recently, we have begun to experiment on the learning of goal-directed behaviour and to interret the results with models of reinforcement learning. We are particularly interested in the susceptibility of habitual learning to temporal context. We suspect that this susceptibility is important for developing more flexible, goal-directed behaviour control.

Fractal patterns used in associative learning

• Hamid OH, Wendemuth A, Braun J. Temporal context and conditional associative learning. BMC neuroscience. 2010;11:45.