The mystery of structure and function of sensory processing areas of the neocortex: a resolution

Semi-Supervised Learning of Cartesian Factors: A Top-Down Model of the Entorhinal Hippocampal Complex

The existence of place cells (PCs), grid cells (GCs), border cells (BCs), and head direction cells (HCs) as well as the dependencies between them have been enigmatic. We make an effort to explain their nature by introducing the concept of Cartesian Factors. These factors have specific properties: (i) they assume and complement each other, like direction and position and (ii) they have localized discrete representations with predictive attractors enabling implicit metric-like computations. In our model, HCs make the distributed and local representation of direction. Predictive attractor dynamics on that network forms the Cartesian Factor "direction." We embed these HCs and idiothetic visual information into a semi-supervised sparse autoencoding comparator structure that compresses its inputs and learns PCs, the distributed local and direction independent (allothetic) representation of the Cartesian Factor of global space. We use a supervised, information compressing predictive algorithm and form direction sensitive (oriented) GCs from the learned PCs by means of an attractor-like algorithm. Since the algorithm can continue the grid structure beyond the region of the PCs, i.e., beyond its learning domain, thus the GCs and the PCs together form our metric-like Cartesian Factors of space. We also stipulate that the same algorithm can produce BCs. Our algorithm applies (a) a bag representation that models the "what system" and (b) magnitude ordered place cell activities that model either the integrate-and-fire mechanism, or theta phase precession, or both. We relate the components of the algorithm to the entorhinal-hippocampal complex and to its working. The algorithm requires both spatial and lifetime sparsification that may gain support from the two-stage memory formation of this complex.

Competitive spiking and indirect entropy minimization of rate code: efficient search for hidden components

Our motivation, which originates from the psychological and physiological evidences of component-based representations in the brain, is to find neural methods that can efficiently search for structures. Here, an architecture made of coupled parallel working reconstruction subnetworks is presented. Each subnetwork utilizes non-negativity constraint on the generative weights and on the internal representation. 'Spikes' are generated within subnetworks via winner take all mechanism. Memory components are modified in order to directly minimize the reconstruction error and to indirectly minimize the entropy of the spike rate distribution, via a combination of a stochastic gradient search and a novel tuning method. This tuning dynamically changes the learning rate: the higher the entropy of the spike rate, the higher the learning rate of the gradient search in the subnetworks. This method effectively reduces the search space and increases the escape probability from high entropy local minima. We demonstrate that one subnetwork can develop localized and oriented components. Coupled networks can discover and sort components into the subnetworks; a problem subject to combinatorial explosion. Synergy between spike code and rate code is discussed.

Kalman Filter Control Embedded into the Reinforcement Learning Framework

There is a growing interest in using Kalman filter models in brain modeling. The question arises whether Kalman filter models can be used on-line not only for estimation but for control. The usual method of optimal control of Kalman filter makes use of off-line backward recursion, which is not satisfactory for this purpose. Here, it is shown that a slight modification of the linear-quadratic-gaussian Kalman filter model allows the on-line estimation of optimal control by using reinforcement learning and overcomes this difficulty. Moreover, the emerging learning rule for value estimation exhibits a Hebbian form, which is weighted by the error of the value estimation.

Two reentrant pathways in the hippocampal-entorhinal system

The entorhinal cortex has long been recognized as an important interface between the hippocampal formation and the neocortex. The notion of bidirectional connections between the entorhinal cortex and the hippocampal formation have led to the suggestion that hippocampal output originating in CA1 and subiculum may reenter hippocampal subfields via the entorhinal cortex. To investigate this, we used simultaneous multi-site field potential recordings and current source density analysis in the entorhinal cortex and hippocampal formation of the rat in vivo. Under ketamine/xylazine anesthesia, we found that repetitive stimulation of subiculum or Schaffer collaterals facilitated entorhinal responses, such that a population spike appeared in layer III. In addition, a current sink in stratum lacunosum-moleculare of area CA1 was found, that followed responses in the entorhinal cortex, indicating reentrance into this area. Responses indicating reentrance in the dentate gyrus were not found under ketamine/xylazine anesthesia, but were readily evoked under urethane anesthesia. Reentrance into CA1 was also encountered under urethane anesthesia. These results suggest that parallel, but possibly functionally distinct, connections are present between the output of the hippocampal formation and cells in layers III and II of the entorhinal cortex that project to area CA1 and the dentate gyrus, respectively.