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We have used simple learning rules to study how firing maps containing triangular grids-as found in in vivo experiments-can be developed by Hebbian means in realistic robotic simulations. We started from typical non-local postrhinal neuronal responses. We found that anti-Hebbian weight pruning can develop triangular grids under certain conditions. Experimental evidences and the present study suggest that within this model, whitening is a bottom-up process, whereas weight pruning and possibly the non-linear extension of whitening to bottom-up information maximization are guided by top-down influences that reorganize entorhinal responses. We connect our model to the computational model of the entorhinal-hippocampal region of Lorincz and Buzsaki. In the joined model, the hippocampus is the origin of response reorganization. The joined model may provide insights for memory reorganization guided by hippocampal supervision.

It has been argued that the processing of sensory information in the entorhinal-hippocampal loop involves independent component analysis (ICA) on temporally concatenated inputs. Here, we demonstrate that ICA in a realistic robot simulation on a U-shaped track forms place fields similar to those found in rat experiments in vivo.

It has been suggested that sensory information processing makes use of a factorial code. It has been shown that the major components of the hippocampal-entorhinal loop can be derived by conjecturing that the task of this loop is forming and encoding independent components (ICs), one type of factorial codes. However, continuously changing environment poses additional requirements on the coding that can be (partially) satisfied by extending the analysis to the temporal domain and performing IC analysis on concatenated inputs of time slices. We use computer simulations to decide whether IC analysis on temporal sequences can produce place fields in labyrinths or not.

For interacting agents in time-critical applications, learning whether a subtask can be scheduled reliably is an important issue. The identification of sub-problems of this nature may promote e.g., planning, scheduling and segmenting in Markov decision processes. We define a subtask to be schedulable if its execution time has a small variance. We present an algorithm for finding such subtasks.

We subseribe to the idea that the brain employs generative networks. In turn, we conclude that channel capacity constraints form the main obstacle for effective information transfer in the brain. Robust and fast information flow processing methods warranting efficient information transfer, e.g. grouping of inputs and information maximization principles need to be applied. For this reason, indepent component analyses on groups of patterns were conducted using (a) model labyrinth, (b) movies on highway traffic and (c) mixed acoustical signals. We found that in all cases ‘familiar’ inputs give rise to cumulated firing histograms close to exponential distributions, whereas ‘novel’ information are better deseribed by broad, sometimes truncated Gaussian distributions. It can be shown that upon minimization of mutual information between processing channels, noise can reveal itself locally. Therefore, we conjecture that novelty - as opposed to noise - can be recognized by means of the statistics of neuronal firing in brain areas.