A synthesisof methods from cybernetics and AI yields a concept of intelligence for autonomous mobile systems that integrates closed-loop visual perception and goal-oriented action cycles using spatiotemporal models. In a layered architecture, systems dynamics methods with differential models prevail on the lower, data-intensive levels, but on higher levels, AI-type methods are used. Knowledge about the world is geared to classes of objects and subjects. Subjects are defined as objects with additional capabilities of sensing, data processing, decision making, and control application. Specialist processes for visual detection and efficient tracking of class members have been developed. On the upper levels, individual instantiations of these class members are analyzed jointly in the task context, yielding the situation for decision making. As an application, vertebrate-type vision for tasks in vehicle guidance in naturally perturbed environments was investigated with a distributed PC system. Experimental results with the test vehicle VAMORS are discussed.

How do cortical neurons represent the acoustic environment? This question isoften addressed by probing with simple stimuli such as clicks or tone pips. Such stimuli have the advantage of yielding easily interpreted answers, but have the disadvantage that they may fail to uncover complex or higher-order neuronal response properties. Here we adopt an alternative approach, probing neuronal responses with complex acoustic stimuli, including animal vocalizations and music. We have used in vivo whole cell methods in the rat auditory cortex to record subthreshold membrane potential fluctuations elicited by these stimuli.

A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real-time. We propose a new computational model that is based on principles of high dimensional dynamical systems in combination with statistical learning theory. It can be implemented on generic evolved or found recurrent circuitry.

We describe a model of short-term synaptic depression that is derived from a silicon circuit implementation. The dynamics of this circuit model are similar to the dynamics of some present theoretical models of shortterm depressionexcept that the recovery dynamics of the variable describing thedepression is nonlinear and it also depends on the presynaptic frequency. The equations describing the steady-state and transient responses ofthis synaptic model fit the experimental results obtained from a fabricated silicon network consisting of leaky integrate-and-fire neurons anddifferent types of synapses. We also show experimental data demonstrating the possible computational roles of depression. One possible roleof a depressing synapse is that the input can quickly bring the neuron up to threshold when the membrane potential is close to the resting potential.

We develop and test new machine learning methods for the prediction oftopological representations of protein structures in the form of coarse-or fine-grained contact or distance maps that are translation androtation invariant. The methods are based on generalized input-output hidden Markov models (GIOHMMs) and generalized recursive neural networks (GRNNs). The methods are used to predict topologydirectly in the fine-grained case and, in the coarsegrained case,indirectly by first learning how to score candidate graphs and then using the scoring function to search the space of possible configurations. Computer simulations show that the predictors achievestate-of-the-art performance.

We used two large clusters .

How do cortical neurons represent the acoustic environment? This question is often addressed by probing with simple stimuli such as clicks or tone pips. Such stimuli have the advantage of yielding easily interpreted answers, but have the disadvantage that they may fail to uncover complex or higher-order neuronal response properties. Here we adopt an alternative approach, probing neuronal responses with complex acoustic stimuli, including animal vocalizations and music. We have used in vivo whole cell methods in the rat auditory cortex to record subthreshold membrane potential fluctuations elicited by these stimuli.

A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real-time. We propose a new computational model that is based on principles of high dimensional dynamical systems in combination with statistical learning theory. It can be implemented on generic evolved or found recurrent circuitry.

We describe a model of short-term synaptic depression that is derived from a silicon circuit implementation. The dynamics of this circuit model are similar to the dynamics of some present theoretical models of shortterm depression except that the recovery dynamics of the variable describing the depression is nonlinear and it also depends on the presynaptic frequency. The equations describing the steady-state and transient responses of this synaptic model fit the experimental results obtained from a fabricated silicon network consisting of leaky integrate-and-fire neurons and different types of synapses. We also show experimental data demonstrating the possible computational roles of depression. One possible role of a depressing synapse is that the input can quickly bring the neuron up to threshold when the membrane potential is close to the resting potential.

We develop and test new machine learning methods for the prediction of topological representations of protein structures in the form of coarse-or fine-grained contact or distance maps that are translation and rotation invariant. The methods are based on generalized input-output hidden Markov models (GIOHMMs) and generalized recursive neural networks (GRNNs). The methods are used to predict topology directly in the fine-grained case and, in the coarsegrained case, indirectly by first learning how to score candidate graphs and then using the scoring function to search the space of possible configurations. Computer simulations show that the predictors achieve state-of-the-art performance.