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Theoretical studies have long proposed that adaptation allows the brain to effectively use the limited response range of sensory neurons to encode widely varying natural inputs. However, despite this influential view, experimental studies have exclusively focused on how the neural code adapts to a range of stimuli lying along a single feature axis, such as orientation or contrast. Here, we performed electrical recordings in macaque visual cortex (area V4) to reveal significant adaptive changes in the neural code of single cells and populations across multiple feature axes. Both during free viewing and passive fixation, populations of cells improved their ability to encode image features after rapid exposure to stimuli lying on orthogonal feature axes even in the absence of initial tuning to these stimuli. These results reveal a remarkable adaptive capacity of visual cortical populations to improve network computations relevant for natural viewing despite the modularity of the functional cortical architecture.

The complete characterization of the activity of a neural population is a challenging task that is complicated by the number of potential interactions that grows exponentially as the size of the population increases. One influential view that has emerged several decades ago is that simple pairwise interactions between neurons can account for the observed firing patterns of large networks. However, despite its prevalence, this view originates from computational and electrophysiological studies in the retina and the primary visual cortex (V1) of anesthetized animals. Therefore, whether or not pairwise interactions predict the observed distribution of firing patterns across multiple brain areas in behaving animals remains unknown. Here we performed multi-electrode recordings from 3 cortical areas to report that second-order neuronal interactions can explain a high fraction of the entropy of the population response in early and mid-level visual cortex (areas V1 and V4) while providing a good approximation of the probability of spiking for groups of neurons. Surprisingly, despite the fact the firing pattern of neurons is controlled by brain state, the model based on pairwise interactions captures more than 90% of the structure in the detailed patterns of spiking observed during wakefulness and sleep. In contrast, regardless of brain state, pairwise interactions fail to explain experimentally observed entropy in neural populations from executive brain areas, such as the dorsolateral prefrontal cortex (dlPFC). These results indicate that higher-order interactions explain the population dynamics in downstream, executive areas, whereas simple pairwise interactions account for the dynamic behavior of neuronal networks in sensory cortical areas.

Neural responses in the cerebral cortex change dramatically between the ‘synchronized’ state during sleep and ‘desynchronized’ state during wakefulness. Our understanding of cortical state emerges largely from experiments performed in sensory areas of head-fixed or tethered rodents due to technical limitations of recording from larger freely-moving animals for several hours. Here, we report a system integrating wireless electrophysiology, wireless eye tracking, and real-time video analysis to examine the dynamics of population activity in a high-level, executive area – dorsolateral prefrontal cortex (dlPFC) of unrestrained monkey. This technology allows us to identify cortical substates during quiet and active wakefulness, and transitions in population activity during rest. We further show that narrow-spiking neurons exhibit stronger synchronized fluctuations in population activity than broad-spiking neurons regardless of state. Our results show that cortical state is controlled by behavioral demands and arousal by asymmetrically modulating the slow response fluctuations of local excitatory and inhibitory cell populations., Authors develop an integrated wireless system to examine brain states in freely-moving monkeys. They show that neural population activity in prefrontal cortex covaries with natural behavioral dynamics. Active behavior is associated with elevated arousal and increases in spiking activity while reducing low-frequency synchrony within cortical populations.

Summary One influential view in neuroscience is that pairwise cell interactions explain the firing patterns of large populations. Despite its prevalence, this view originates from studies in the retina and visual cortex of anesthetized animals. Whether pairwise interactions predict the firing patterns of neurons across multiple brain areas in behaving animals remains unknown. Here, we performed multi-area electrical recordings to find that 2nd-order interactions explain a high fraction of entropy of the population response in macaque cortical areas V1 and V4. Surprisingly, despite the brain-state modulation of neuronal responses, the model based on pairwise interactions captured ∼90% of the spiking activity structure during wakefulness and sleep. However, regardless of brain state, pairwise interactions fail to explain experimentally observed entropy in neural populations from the prefrontal cortex. Thus, while simple pairwise interactions explain the collective behavior of visual cortical networks across brain states, explaining the population dynamics in downstream areas involves higher-order interactions.

In the hippocampus, a network of place cells generates a cognitive map of space, in which each cell is responsive to a particular area of the environment -- its place field. The peak response of each cell and the size of each place field have considerable variability. Experimental evidence suggests that place cells encode a topological map of space that serves as a basis of spatial memory and spatial awareness. Using a computational model based on Persistent Homology Theory we demonstrate that if the parameters of the place cells spiking activity fall inside of the physiological range, the network correctly encodes the topological features of the environment. We next introduce parameters of synaptic connectivity into the model and demonstrate that failures in synapses that detect coincident neuronal activity lead to spatial learning deficiencies similar to the ones that are observed in rodent models of neurodegenerative diseases. Moreover, we show that these learning deficiencies may be mitigated by increasing the number of active cells and/or by increasing their firing rate, suggesting the existence of a compensatory mechanism inherent to the cognitive map.

Stem cell therapy has a place for future application in the treatment of degenerative diseases. Regardless of the origin of the stem cell, when placed within a milieu of inflammatory mediator, they will show varied functions. This review focuses on human mesenchymal stem cells (MSCs) and discusses neuronal replacement using multi- and inter-disciplinary approaches. We caution the enthusiasm of scientists since there is always the potential for tumor formation, even for adult stem cells. The review places RE-1 silencing transcription factor (REST) gene as central to the understanding of stem cell behavior in the microenvironment of tissue injury. REST is relevant in the development of dopaminergic and peptidergic neurons from MSCs. Premature downregulation of REST by the pro-inflammatory mediator, IL-1alpha, can prematurely lead to the expression of neurotransmitters, which in turn, could develop rapid crosstalk with immune cells. In-depth inter- and multi-disciplinary research will lead to rapid and safe translation of MSCs to patients. An understanding of the changes induced in MSCs by cytokines and other mediators will establish future application of MSCs and other stem cells for safe and effective treatments. This study also alludes to the potential of personalized medicine through engineering and mathematics.

Benefit-cost analysis can play a very important role in legislative and regulatory policy debates on improving the environment, health, and safety. It can help illustrate the tradeoffs that are inherent in public policymaking as well as make those tradeoffs more transparent. It can also help agencies set regulatory priorities. Benefit-cost analysis should be used to help decisionmakers reach a decision. Contrary to the views of some, benefit-cost analysis is neither necessary nor sufficient for designing sensible public policy. If properly done, it can be very helpful to agencies in the decisionmaking process. Decisionmakers should not be precluded from considering the economic benefits and costs of different policies in the development of regulations. Laws that prohibit costs or other factors from being considered in administrative decisionmaking are inimical to good public policy. Currently, several of the most important regulatory statutes have been interpreted to imply such prohibitions. Benefit-cost analysis should be required for all major regulatory decisions, but agency heads should not be bound by a strict benefit-cost test. Instead, they should be required to consider available benefit-cost analyses and to justify the reasons for their decision in the event that the expected costs of a regulation far exceed the expected benefits. Agencies should be encouraged to use economic analysis to help set regulatory priorities. Economic analyses prepared in support of particularly important decisions should be subjected to peer review both inside and outside government. Benefits and costs of proposed major regulations should be quantified wherever possible. Best estimates should be presented along with a description of the uncertainties. Not all benefits or costs can be easily quantified, much less translated into dollar terms. Nevertheless, even qualitative descriptions of the pros and cons associated with a contemplated action can be helpful. Care should be taken to ensure that quantitative factors do not dominate important qualitative factors in decisionmaking. The Office of Management and Budget, or some other coordinating agency, should establish guidelines that agencies should follow in conducting benefit-cost analyses. Those guidelines should specify default values for the discount rate and certain types of benefits and costs, such as the value of a small reduction in mortality risk. In addition, agencies should present their results using a standard format, which summarizes the key results and highlights major uncertainties.

The recurrence of tumors after years of disease-free survival has spurred interest in the concept that cancers may have a stem cell basis. Current speculation holds that as few as 0.1% of the tumor mass may be chemoresistant and radioresistant, harboring stem-like properties that drive tumor survival, development, and metastasis. There are intense investigations to characterize cancer stem cells on the basis of self-renewal and multi-lineage differentiation. Thus far, no successful targeted therapies have been developed and reached the clinic, but as these cells are isolated and characterized, insights may be unraveled. In this review, we discuss the controversy over the origins of the cancer stem cell hypothesis and the unforeseen factors that may facilitate breast cancer stem cell survival and metastasis. We discuss the role of tumor microenvironment, including carcinoma-associated fibroblasts, epigenetic factors, and the Th1/Th2 balance, in supporting breast cancer stem cells. In addition, we have incorporated ideas on the epithelial-to-mesenchymal transition in metastatic dissemination of epithelial malignancies. This area is relevant since breast cancer stem cells have been suggested to revert to a mesenchymal phenotype during the progression of cancer. Finally we discuss prospects of developing targeted therapy including novel treatment modalities such as oncolytic viral therapy, differentiation therapy, and nanotechnology.