Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.
An adaptive DNN architecture by Calandra, et al, shows how DL can be applied to data unseen by a trained network @cite_23 . Their approach hinges on incrementally re-training deep belief networks (DBNs) whenever concept drift emerges in a monitored stream of data and operates within constant memory bounds. They utilize the generative capability of DBNs to provide training samples of previously learned classes. Class conditional sampling from trained networks has biological inspiration @cite_16 @cite_11 @cite_29 @cite_24 as well as historical and artificial neural network implementations @cite_15 @cite_4 @cite_7 @cite_14 .
@cite_14 @cite_4 @cite_7 @cite_29 @cite_24 @cite_23 @cite_15 @cite_16 @cite_11
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The concept of ‘sleeping on a problem’ is familiar to most of us. But with myriad stages of sleep, forms of memory and processes of memory encoding and consolidation, sorting out how sleep contributes to memory has been anything but straightforward. Nevertheless, converging evidence, from the molecular to the phenomenological, leaves little doubt that offline memory reprocessing during sleep is an important component of how our memories are formed and ultimately shaped. 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 it 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 somato sensory 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 pariet al 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 uncertainties in the reported assignment. Deep learning has proven to be beneficial for complex tasks such as classifying images. However, this approach has been mostly applied to static datasets. The analysis of non-stationary (e.g., concept drift) streams of data involves specific issues connected with the temporal and changing nature of the data. In this paper, we propose a proof-of-concept method, called Adaptive Deep Belief Networks, of how deep learning can be generalized to learn online from changing streams of data. We do so by exploiting the generative properties of the model to incrementally re-train the Deep Belief Network whenever new data are collected. This approach eliminates the need to store past observations and, therefore, requires only constant memory consumption. Hence, our approach can be valuable for life-long learning from non-stationary data streams. An unsupervised learning algorithm for a multilayer network of stochastic neurons is described. Bottom-up "recognition" connections convert the input into representations in successive hidden layers, and top-down "generative" connections reconstruct the representation in one layer from the representation in the layer above. In the "wake" phase, neurons are driven by recognition connections, and generative connections are adapted to increase the probability that they would reconstruct the correct activity vector in the layer below. In the "sleep" phase, neurons are driven by generative connections, and recognition connections are adapted to increase the probability that they would produce the correct activity vector in the layer above. Replay is the sequential reactivation of hippocampal place cells that represent previously experienced behavioral trajectories. Although first studied during sleep, recent work suggests that replay occurs frequently in the awake state and could be a potential substrate for memory consolidation and retrieval. Abstract Human dreaming occurs during rapid eye movement (REM) sleep. To investigate the structure of neural activity during REM sleep, we simultaneously recorded the activity of multiple neurons in the rat hippocampus during both sleep and awake behavior. We show that temporally sequenced ensemble firing rate patterns reflecting tens of seconds to minutes of behavioral experience are reproduced during REM episodes at an equivalent timescale. Furthermore, within such REM episodes behavior-dependent modulation of the subcortically driven theta rhythm is also reproduced. These results demonstrate that long temporal sequences of patterned multineuronal activity suggestive of episodic memory traces are reactivated during REM sleep. Such reactivation may be important for memory processing and provides a basis for the electrophysiological examination of the content of dream states.