Familiarity Breeds Plasticity: Distinct Effects of Experience on Putative Excitatory and Inhibitory Neurons in Inferior Temporal Cortex

Primates have a remarkable capacity to recognize a vast array of visual objects, an ability that depends on experience. In this issue of Neuron, Woloszyn and Sheinberg (2012) report that putative excitatory and inhibitory neurons in inferior temporal cortex exhibit distinct influences long-term visual experience

Wielding the Ax of Neutrality: The Constitutional Status of Charitable Choice in the Wake of Mitchell v. Helms

During the past decade, the Supreme Court loosened restraints that it had previously imposed upon government aid to religious institutions. In 1996, Congress and the President seized upon this phenomenon and implemented a controversial provision in the Personal Responsibility and Work Opportunity Reconciliation Act of 1996-also known as the Welfare Reform Act of 1996. Included among the various revolutionary provisions of this legislation is something known as Charitable Choice. This program authorizes states to contract with religious institutions to provide social welfare services on behalf ofthe states

Learning Deep Generative Models with Annealed Importance Sampling

Variational inference (VI) and Markov chain Monte Carlo (MCMC) are two main approximate approaches for learning deep generative models by maximizing marginal likelihood. In this paper, we propose using annealed importance sampling for learning deep generative models. Our proposed approach bridges VI with MCMC. It generalizes VI methods such as variational auto-encoders and importance weighted auto-encoders (IWAE) and the MCMC method proposed in (Hoffman, 2017). It also provides insights into why running multiple short MCMC chains can help learning deep generative models. Through experiments, we show that our approach yields better density models than IWAE and can effectively trade computation for model accuracy without increasing memory cost

Artificial Neuronal Ensembles with Learned Context Dependent Gating

Biological neural networks are capable of recruiting different sets of neurons to encode different memories. However, when training artificial neural networks on a set of tasks, typically, no mechanism is employed for selectively producing anything analogous to these neuronal ensembles. Further, artificial neural networks suffer from catastrophic forgetting, where the network's performance rapidly deteriorates as tasks are learned sequentially. By contrast, sequential learning is possible for a range of biological organisms. We introduce Learned Context Dependent Gating (LXDG), a method to flexibly allocate and recall `artificial neuronal ensembles', using a particular network structure and a new set of regularization terms. Activities in the hidden layers of the network are modulated by gates, which are dynamically produced during training. The gates are outputs of networks themselves, trained with a sigmoid output activation. The regularization terms we have introduced correspond to properties exhibited by biological neuronal ensembles. The first term penalizes low gate sparsity, ensuring that only a specified fraction of the network is used. The second term ensures that previously learned gates are recalled when the network is presented with input from previously learned tasks. Finally, there is a regularization term responsible for ensuring that new tasks are encoded in gates that are as orthogonal as possible from previously used ones. We demonstrate the ability of this method to alleviate catastrophic forgetting on continual learning benchmarks. When the new regularization terms are included in the model along with Elastic Weight Consolidation (EWC) it achieves better performance on the benchmark `permuted MNIST' than with EWC alone. The benchmark `rotated MNIST' demonstrates how similar tasks recruit similar neurons to the artificial neuronal ensemble.Comment: 13 pages, 9 figure

Categorization in IT and PFC: Model and Experiments

In a recent experiment, Freedman et al. recorded from inferotemporal (IT) and prefrontal cortices (PFC) of monkeys performing a "cat/dog" categorization task (Freedman 2001 and Freedman, Riesenhuber, Poggio, Miller 2001). In this paper we analyze the tuning properties of view-tuned units in our HMAX model of object recognition in cortex (Riesenhuber 1999) using the same paradigm and stimuli as in the experiment. We then compare the simulation results to the monkey inferotemporal neuron population data. We find that view-tuned model IT units that were trained without any explicit category information can show category-related tuning as observed in the experiment. This suggests that the tuning properties of experimental IT neurons might primarily be shaped by bottom-up stimulus-space statistics, with little influence of top-down task-specific information. The population of experimental PFC neurons, on the other hand, shows tuning properties that cannot be explained just by stimulus tuning. These analyses are compatible with a model of object recognition in cortex (Riesenhuber 2000) in which a population of shape-tuned neurons provides a general basis for neurons tuned to different recognition tasks

PC tools for project management: Programs and the state-of-the-practice

The use of microcomputer tools for NASA project management; which features are the most useful; the impact of these tools on job performance and individual style; and the prospects for new features in project management tools and related tools are addressed. High, mid, and low end PM tools are examined. The pro's and con's of the tools are assessed relative to various tasks. The strengths and weaknesses of the tools are presented through cases and demonstrations

Visual categorization and the parietal cortex

The primate brain is adept at rapidly grouping items and events into functional classes, or categories, in order to recognize the significance of stimuli and guide behavior. Higher cognitive functions have traditionally been considered the domain of frontal areas. However, increasing evidence suggests that parietal cortex is also involved in categorical and associative processes. Previous work showed that the parietal cortex is highly involved in spatial processing, attention, and saccadic eye movement planning, and more recent studies have found decision-making signals in lateral intraparietal area (LIP). We recently found that a subdivision of parietal cortex, LIP, reflects learned categories for multiple types of visual stimuli. Additionally, a comparison of categorization signals in parietal and frontal areas found stronger and earlier categorization signals in parietal cortex arguing that, in trained animals, parietal abstract association or category signals are unlikely to arise via feedback from prefrontal cortex (PFC)

Alterations of neural activity in the prefrontal cortex associated with deficits in working memory performance

Working memory (WM), a core cognitive function, enables the temporary holding and manipulation of information in mind to support ongoing behavior. Neurophysiological recordings conducted in nonhuman primates have revealed neural correlates of this process in a network of higher-order cortical regions, particularly the prefrontal cortex (PFC). Here, we review the circuit mechanisms and functional importance of WM-related activity in these areas. Recent neurophysiological data indicates that the absence of these neural correlates at different stages of WM is accompanied by distinct behavioral deficits, which are characteristic of various disease states/normal aging and which we review here. Finally, we discuss emerging evidence of electrical stimulation ameliorating these WM deficits in both humans and non-human primates. These results are important for a basic understanding of the neural mechanisms supporting WM, as well as for translational efforts to developing therapies capable of enhancing healthy WM ability or restoring WM from dysfunction