The Influence of Markov Decision Process Structure on the Possible Strategic Use of Working Memory and Episodic Memory

Researchers use a variety of behavioral tasks to analyze the effect of biological manipulations on memory function. This research will benefit from a systematic mathematical method for analyzing memory demands in behavioral tasks. In the framework of reinforcement learning theory, these tasks can be mathematically described as partially-observable Markov decision processes. While a wealth of evidence collected over the past 15 years relates the basal ganglia to the reinforcement learning framework, only recently has much attention been paid to including psychological concepts such as working memory or episodic memory in these models. This paper presents an analysis that provides a quantitative description of memory states sufficient for correct choices at specific decision points. Using information from the mathematical structure of the task descriptions, we derive measures that indicate whether working memory (for one or more cues) or episodic memory can provide strategically useful information to an agent. In particular, the analysis determines which observed states must be maintained in or retrieved from memory to perform these specific tasks. We demonstrate the analysis on three simplified tasks as well as eight more complex memory tasks drawn from the animal and human literature (two alternation tasks, two sequence disambiguation tasks, two non-matching tasks, the 2-back task, and the 1-2-AX task). The results of these analyses agree with results from quantitative simulations of the task reported in previous publications and provide simple indications of the memory demands of the tasks which can require far less computation than a full simulation of the task. This may provide a basis for a quantitative behavioral stoichiometry of memory tasks

Hyponatremia in visceral leishmaniasis

There are few reports linking hyponatremia and visceral leishmaniasis (kala-azar). This is a study of 55 consecutive kala-azar patients and 20 normal individuals as a control group. Hyponatremia and serum hypo-osmolality were detected in 100% of kala-azar patients. High first morning urine osmolality (750.0 ± 52.0 vs. 894.5 ± 30.0mOsm/kg H2O, p Existem poucos relatos relacionando hiponatremia com a leshmaniose visceral (calazar). Este é um estudo de 55 pacientes portadores de calazar e um grupo controle de 20 indivíduos normais. Hiponatremia e hipo-osmolalidade sérica foram detectados em 100% dos pacientes portadores de calazar. A presença de alta osmolalidade da primeira urina da manhã (750,0 ± 52,0 vs. 894,5 ± 30 mOsm/Kg H2O, p < 0,05) e da urina de 24h (426,0 ± 167,0 vs. 514,6 ± 132,0 mOsm/Kg H2O, p < 0,05), demonstraram a presença de persistente secreção de hormônio antidiurético. A concentração de sódio urinário foi elevada (82,3 ± 44,2 vs. 110,3 ± 34,7 mEq/L, p < 0,05). Hipouricemia ocorreu em 61,8% dos pacientes e aumento da fração de excreção urinária de ácido úrico foi detectada em 74,5% dos casos. Aumento da velocidade de filtração glomerular estava presente em 25,4% dos pacientes. Não havia evidência clínica de depleção de volume extracelular. Valores normais de ADH plasmático foram observados nos pacientes com calazar. Não foi detectada disfunção renal ou endócrina. É provável, que a maioria dos pacientes com calazar apresente uma síndrome de secreção inapropriada de hormônio antidiurético

Working Memory Cells' Behavior May Be Explained by Cross-Regional Networks with Synaptic Facilitation

Neurons in the cortex exhibit a number of patterns that correlate with working memory. Specifically, averaged across trials of working memory tasks, neurons exhibit different firing rate patterns during the delay of those tasks. These patterns include: 1) persistent fixed-frequency elevated rates above baseline, 2) elevated rates that decay throughout the tasks memory period, 3) rates that accelerate throughout the delay, and 4) patterns of inhibited firing (below baseline) analogous to each of the preceding excitatory patterns. Persistent elevated rate patterns are believed to be the neural correlate of working memory retention and preparation for execution of behavioral/motor responses as required in working memory tasks. Models have proposed that such activity corresponds to stable attractors in cortical neural networks with fixed synaptic weights. However, the variability in patterned behavior and the firing statistics of real neurons across the entire range of those behaviors across and within trials of working memory tasks are typical not reproduced. Here we examine the effect of dynamic synapses and network architectures with multiple cortical areas on the states and dynamics of working memory networks. The analysis indicates that the multiple pattern types exhibited by cells in working memory networks are inherent in networks with dynamic synapses, and that the variability and firing statistics in such networks with distributed architectures agree with that observed in the cortex

The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain

The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100–500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a “router” network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates