https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

In recent years, many new cortical areas have been identified in the macaque monkey. The number of identified connections between areas has increased even more dramatically. We report here on (1) a summary of the layout of cortical areas associated with vision and with other modalities, (2) a computerized database for storing and representing large amounts of information on connectivity patterns, and (3) the application of these data to the analysis of hierarchical organization of the cerebral cortex. Our analysis concentrates on the visual system, which includes 25 neocortical areas that are predominantly or exclusively visual in function, plus an additional 7 areas that we regard as visual-association areas on the basis of their extensive visual inputs. A total of 305 connections among these 32 visual and visual-association areas have been reported. This represents 31% of the possible number of pathways if each area were connected with all others. The actual degree of connectivity is likely to be closer to 40%. The great majority of pathways involve reciprocal connections between areas. There are also extensive connections with cortical areas outside the visual system proper, including the somatosensory cortex, as well as neocortical, transitional, and archicortical regions in the temporal and frontal lobes. In the somatosensory/motor system, there are 62 identified pathways linking 13 cortical areas, suggesting an overall connectivity of about 40%. Based on the laminar patterns of connections between areas, we propose a hierarchy of visual areas and of somatosensory/motor areas that is more comprehensive than those suggested in other recent studies. The current version of the visual hierarchy includes 10 levels of cortical processing. Altogether, it contains 14 levels if one includes the retina and lateral geniculate nucleus at the bottom as well as the entorhinal cortex and hippocampus at the top. Within this hierarchy, there are multiple, intertwined processing streams, which, at a low level, are related to the compartmental organization of areas V1 and V2 and, at a high level, are related to the distinction between processing centers in the temporal and parietal lobes. However, there are some pathways and relationships (about 10% of the total) whose descriptions do not fit cleanly into this hierarchical scheme for one reason or another. In most instances, though, it is unclear whether these represent genuine exceptions to a strict hierarchy rather than inaccuracies or uncertainities in the reported assignment.

/pdf/distributed-hierarchical-processing-in-the-primate-cerebral-1dvblm6d82.pdf

Distributed Hierarchical Processing in the Primate Cerebral Cortex

The receptive fields of simple cells in mammalian primary visual cortex can be characterized as being spatially localized, oriented and bandpass (selective to structure at different spatial scales), comparable to the basis functions of wavelet transforms. One approach to understanding such response properties of visual neurons has been to consider their relationship to the statistical structure of natural images in terms of efficient coding. Along these lines, a number of studies have attempted to train unsupervised learning algorithms on natural images in the hope of developing receptive fields with similar properties, but none has succeeded in producing a full set that spans the image space and contains all three of the above properties. Here we investigate the proposal that a coding strategy that maximizes sparseness is sufficient to account for these properties. We show that a learning algorithm that attempts to find sparse linear codes for natural scenes will develop a complete family of localized, oriented, bandpass receptive fields, similar to those found in the primary visual cortex. The resulting sparse image code provides a more efficient representation for later stages of processing because it possesses a higher degree of statistical independence among its outputs.

/pdf/emergence-of-simple-cell-receptive-field-properties-by-bulmogedmu.pdf

Emergence of simple-cell receptive field properties by learning a sparse code for natural images

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components - not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.

/pdf/perceptual-symbol-systems-4spsk97r61.pdf

Perceptual symbol systems.

Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1 ?

http://hopf.cns.nyu.edu/~tony/vns/readings/devalois-albrecht-thorell-1982.pdf

Spatial frequency selectivity of cells in macaque visual cortex

The orientation and direction selectivity of cells in macaque visual cortex

A detailed analysis was made of the response characteristics of single cells in the lateral geniculate nucleus of the macaque monkey. The goal was to understand how these cells contribute to the processing of visual information. Data were analyzed from a representative sample of 147 cells, whose responses to equal-energy spectra (presented as diffuse flashes of monochromatic light) were recorded at three radiance levels. On the basis of their responses, the cells were divided into two general classes: (a) spectrally nonopponent cells which respond to all wavelengths with either an increase or decrease in firing rate, (b) spectrally opponent cells (about two-thirds of the sample) which respond with an increase in firing rate to some parts of the spectrum and a decrease to other parts. Four types of opponent cells were found: (i) red excitatory and green inhibitory (+R−G), (ii) green excitatory and red inhibitory (+G−R), (iii) yellow excitatory and blue inhibitory (+Y−B), (iv) blue excitatory and yellow inhibitory (+B−Y). Comparisons with psychophysical data indicated that nonopponent cells transmit brightness information; opponent cells, however, carry information about color, the hue of a light being determined by the relative responses of the four types. The saturation of spectral lights appears to be related to the differences in responses of opponent and non-opponent cells.

Analysis of response patterns of LGN cells.

We have anatomically analyzed retinotopic organization using the 14C-labeled 2-deoxy-D-glucose method. The method has several advantages over conventional electrophysiological mapping techniques. In the striate cortex, the anatomical substrate for retinotopic organization is surprisingly well ordered, and there seems to be a systematic relationship between ocular dominance strips and cortical magnification. The 2-deoxyglucose maps in this area appear to be largely uninfluenced by known differences in long-term metabolic activity. This method should prove useful in analyzing retinotopic organization in various visual areas of the brain and in different species.

http://science.sciencemag.org/content/sci/218/4575/902.full.pdf

Russell L. De Valois

Papers

Spatial frequency selectivity of cells in macaque visual cortex

The orientation and direction selectivity of cells in macaque visual cortex

Analysis of response patterns of LGN cells.

Deoxyglucose analysis of retinotopic organization in primate striate cortex

Psychophysical studies of monkey Vision-III. Spatial luminance contrast sensitivity tests of macaque and human observers