Event Abstract Back to Event A columnar model of bottom-up and top-down processing in the neocortex Sven Schrader1*, Marc-Oliver Gewaltig1, 2, Ursula Körner1 and Edgar Körner1 1 Honda Research Institute Europe GmbH, Germany 2 Bernstein Center for Computational Neuroscience, Germany Thorpe et al. (1996) demonstrated that our brains are able to process visual stimuli within the first 150 ms, without considering all possible interpretations. It is therefore likely that a first coarse hypothesis, which captures the most relevant features of the stimulus, is made in a pure feed-forward manner. Details and less relevant features are postponed to a later, feedback-mediated stage. Based on our assumptions (Körner et al., 1999), we present a columnar model of cortical processing that demonstrates the formation of a fast initial hypothesis and its subsequent disambiguation by inter-columnar communication. Neural representation occurs by forming coherent spike waves (volleys) as local decisions. The model consists of three areas, each representing more abstract features of the stimulus hierarchy. The areas are connected with converging bottom-up projections that propagate activity to the next higher level. During this forward propagation, the initial hypothesis is generated. Top-down feedback is mediated by modulatory connections that amplify the correct representation and suppress the incorrect ones, until only the most compact representation of the object remains active. Our model foots on three postulates that interpret the cortical architecture in terms of the algorithm it implements. First, we argue that the columnar modularization reflects a functional modularization. We interpret columns as computational units that use the same set of powerful processing strategies over and over again. Second, each cortical column hosts the circuitry of two processing streams, a fast feed-forward "A-", and a slower modulatory "B-" system that refines the decision taken in the A-system by mixing experience with the afferent stimulus stream (predictive coding). Third, time is too short to estimate the reliability of a neuron's response in a rate-coded manner. We therefore argue that cortical neurons code reliability in their relative response latencies. By receiving the fastest response, a target neuron automatically picks up the most reliable one. At first, our model generates a sequence of spike volleys, each being a possible representation of the stimulus. These candidates comprise about one percent of all 300 learned objects. The correctness of a response is directly expressed in its latency: the better a representation matches the stimulus, the earlier the response occurs. The B-system implements top-down predictive coding: Based on the stored knowledge, responses are modified until the set of candidates is on average reduced to one. Thus, the network makes a unique decision on the stimulus. It is correct in 95% of the trials, even with degraded stimuli. We analyze the spike volleys in terms of their occurrence times and precision, and give a functional interpretation to rhythmic activity such as gamma oscillations. Our model has been simulated with the simulation tool NEST (Gewaltig and Diesmann, 2007).