"Interior view of a hut ...": Stereography and the depiction of an Interior Architecture in 1930

This paper is an exploration of a stereographic photograph taken inside a New Zealand backcountry hut. Matter-of-factly entitled, "Interior view of a hut, with mugs, a bottle, plate and cutlery on a table, looking through door to another hut, location unidentified," the photograph is attributed by the Alexander Turnbull Library to keen amateur photographer Edgar Richard Williams. The image gives little detail away in its depiction of the hut interior, except for a utilitarian table tableau that begins to suggest a nascent New Zealand interior defined by no-nonsense pragmaticism and Lea & Perrins. But, far from being a scene of Depression-era poverty and deprivation, close examination of the photographed situation and its broader context provides a glimpse into a monied amateurism that heralded an emergent leisure class. As a stereoscopic image, the photograph does more than depict a scene. By placing us within a spatial view, we become immersed in questions concerning interiority and exteriority. We are presented with two spatial contrasts: one in the subject of the image, the other in the object of the image. By taking a close reading of both contrasts, this paper is an attempt to make some architectural sense of these dualities.

The Hippocampus uses cryptographic principles rather than plasticity to enable animals to secretly cache and retrieve their food

For animals, the ability to hide and retrieve valuable information, such as the location of food, can mean the difference between life and death. Here, we propose that to achieve this, their brain uses spatial cells similarly to how we utilize encryption for data security. Some animals are able to cache hundreds of thousands of food items annually by each individual and later retrieve most of what they themselves stashed. Rather than memorizing their cache locations as previously suggested, we propose that they use a single cryptographic-like mechanism during both caching and retrieval. The model we developed is based on hippocampal spatial cells, which respond to an animal's positional attention, such as when the animal enters a specific region (place-cells) or gazes at a particular location (spatial-view-cells). We know that the region that activates each spatial cell remains consistent across subsequent visits to the same area but not between areas. This remapping, combined with the uniqueness of cognitive maps, produces a persistent crypto-hash function for both food caching and retrieval. We also show that the model stores temporal information that helps animals in food caching order preference as previously observed. This behavior, which we refer to as crypto-taxis, might also explain consistent differences in decision-making when animals are faced with a large number of alternatives such as in foraging.

Where Is Your Place, Visual Place Recognition?

Visual Place Recognition (VPR) is often characterized as being able to recognize the same place despite significant changes in appearance and viewpoint. VPR is a key component of Spatial Artificial Intelligence, enabling robotic platforms and intelligent augmentation platforms such as augmented reality devices to perceive and understand the physical world. In this paper, we observe that there are three "drivers" that impose requirements on spatially intelligent agents and thus VPR systems: 1) the particular agent including its sensors and computational resources, 2) the operating environment of this agent, and 3) the specific task that the artificial agent carries out. In this paper, we characterize and survey key works in the VPR area considering those drivers, including their place representation and place matching choices. We also provide a new definition of VPR based on the visual overlap - akin to spatial view cells in the brain - that enables us to find similarities and differences to other research areas in the robotics and computer vision fields. We identify several open challenges and suggest areas that require more in-depth attention in future works.

Learning Invariant Object and Spatial View Representations in the Brain Using Slow Unsupervised Learning

First, neurophysiological evidence for the learning of invariant representations in the inferior temporal visual cortex is described. This includes object and face representations with invariance for position, size, lighting, view and morphological transforms in the temporal lobe visual cortex; global object motion in the cortex in the superior temporal sulcus; and spatial view representations in the hippocampus that are invariant with respect to eye position, head direction, and place. Second, computational mechanisms that enable the brain to learn these invariant representations are proposed. For the ventral visual system, one key adaptation is the use of information available in the statistics of the environment in slow unsupervised learning to learn transform-invariant representations of objects. This contrasts with deep supervised learning in artificial neural networks, which uses training with thousands of exemplars forced into different categories by neuronal teachers. Similar slow learning principles apply to the learning of global object motion in the dorsal visual system leading to the cortex in the superior temporal sulcus. The learning rule that has been explored in VisNet is an associative rule with a short-term memory trace. The feed-forward architecture has four stages, with convergence from stage to stage. This type of slow learning is implemented in the brain in hierarchically organized competitive neuronal networks with convergence from stage to stage, with only 4-5 stages in the hierarchy. Slow learning is also shown to help the learning of coordinate transforms using gain modulation in the dorsal visual system extending into the parietal cortex and retrosplenial cortex. Representations are learned that are in allocentric spatial view coordinates of locations in the world and that are independent of eye position, head direction, and the place where the individual is located. This enables hippocampal spatial view cells to use idiothetic, self-motion, signals for navigation when the view details are obscured for short periods.

The position of borderline areas of Southern Siberia in socio-economic ratings of the Russian regions

Subject. The article discusses the position of borderline areas of Southern Siberia in terms of the socio-economic development and geographical position. Objectives. We perform the comparative analysis of the regions’ position, determine the place and status of each borderline area, find identical regions in Russia in terms of the economic situation and difficulties. Methods. The study addresses the multivariate classification of data through the method of grouping and cluster analysis. Results. We suggest using the methodological approach to determining the status of the regions from three dimensions, i.e. social, economic and geographical, and apply some indicators, such as the ratio of average income per capita and the subsistence level for the social view, the real fiscal capacity for the economic view, and the density of the population (man per km2) for the spatial view. To present the data conveniently and clearly, we conducted the cluster analysis, set the dendrogram of the borderline areas of Southern Siberia. Conclusions. Determining the regions’ status by three anchors is more beneficial than traditional types of ranking and grouping, since it provides an unbiased view of the region, helps analyze the current socio-economic difficulties.

The hippocampus, memory, and spatial function

The hippocampal system provides a beautiful example of how different classes of neuronal network in the brain work together as a system to implement episodic memory, the memory for particular recent events. The hippocampus contains spatial view neurons in primates including humans, which provide a representation of locations in viewed space. These representations can be combined with object and temporal representations to provide an episodic memory about what happened where and when. A key part of the system is the CA3 system with its recurrent collateral connections that provide a single attractor network for these associations to be learned. The computational generation of time, encoded by time cells in the hippocampus, is described, and this leads to a theory of hippocampal replay and reverse replay. The computational operation of a key part of the architecture, the recall of memories to the neocortex, is described.

The parietal cortex, spatial functions, and navigation

The parietal areas that are involved in the dorsal visual stream are described in Chapter 3. This Chapter builds on that, and considers the functions of spatial representations in the parietal cortex and areas to which it projects the retrosplenial and posterior cingulate cortex, which in turn project to the hippocampus, in navigation. It is hypothesized that human navigation is likely to often depend on spatial view neurons, which with a list of landmarks provides a common method of navigation. This may be complemented by the use of allocentric bearing to a landmark cells, which provide a basis for navigation that is not based on approach to landmarks, but instead on bearings to landmarks. Models for both types of navigation are provided with Matlab code. Idiothetic (self-motion) update of hippocampal representations is likely to be performed by the operations of the coordinate transform systems in the dorsal visual system described in Chapter 3, which provides inputs to the hippocampus.