Neural coding of action in three dimensions: Task- and time-invariant reference frames for visuospatial and motor-related activity in parietal area V6A

Goal-directed movements involve a series of neural computations that compare the sensory representations of goal location and effector position, and transform these into motor commands. Neurons in posterior parietal cortex (PPC) control several effectors (e.g., eye, hand, foot) and encode goal location in a variety of spatial coordinate systems, including those anchored to gaze direction, and to the positions of the head, shoulder, or hand. However, there is little evidence on whether reference frames depend also on the effector and/or type of motor response. We addressed this issue in macaque PPC area V6A, where previous reports using a fixate-to-reach in depth task, from different starting arm positions, indicated that most units use mixed body/hand-centered coordinates. Here, we applied singular value decomposition and gradient analyses to characterize the reference frames in V6A while the animals, instead of arm reaching, performed a nonspatial motor response (hand lift). We found that most neurons used mixed body/hand coordinates, instead of \u201cpure\u201d body-, or hand-centered coordinates. During the task progress the effect of hand position on activity became stronger compared to target location. Activity consistent with body-centered coding was present only in a subset of neurons active early in the task. Applying the same analyses to a population of V6A neurons recorded during the fixate-to-reach task yielded similar results. These findings suggest that V6A neurons use consistent reference frames between spatial and nonspatial motor responses, a functional property that may allow the integration of spatial awareness and movement control

Estimation of MSY on six species of commercially important demersal fishes in the Persian Gulf & Oman Sea (Hormuzgan province)

Today, calculation of MSY is one of the necessary fisheries management in control and prevention of the fish population reduction and is obtained with different methods. This study has focused on six species of commercial fish, including Tiger-toothed croaker, Javelin grunter, John`s snapper, Indian spiny turbot, Yellowfin seabream and Silver pomfret. The study was done monthly, from January 2007 to March 2008, in three fish landing regions including: Bandar Lengeh, Bandar Abbas and Qeshm Island (Slakh, Basydu and Chahoshrqy). Total 5163 Silver pomfret (Pompus argenteus), 1766 Javelin grunter (Pomadasys kaakan), 2151 John`s snapper (Lutjanus johnii), 3280 Tiger-toothed croaker (Otolithes ruber), 1628 Indian spiny turbot (Psettodes erumei) and the number of 759 Yellowfin seabream (Acnthopagrus latus) were assessed and length biometry has been done, monthly. In this study, two methods were used to determine the maximum sustainable yield (MSY): 1- virtual population analysis (Cohort analysis) 2- use of statistics and information that was estimated with two method, catch prediction and biomass (Standing stock). The results showed that in 2007, MSY value was estimated through catch prediction for Silver pomfret, Tigertoothed croaker, Javelin grunter, John`s snapper, Indian spiny turbot and Yellowfin seabream 1354, 1116, 1099.6, 1045.5, 914.5 and 529.5 tons, respectively. Moreover, this estimation have been done through standing stock for Silver pomfret, Tiger-toothed croaker, Javelin grunter, John`s snapper, Indian spiny turbot and Yellowfin seabream 1215, 633, 1304, 878, 1095 and 441 tons, respectively; and through VPA for Silver pomfret, Tiger-toothed croaker, Javelin grunter, John`s snapper, Indian spiny turbot and Yellowfin seabream 1100, 850, 920, 732.5, 1002.3 and 403 tons, respectively. Amount of biomass (Standing Stock) was estimated for Silver pomfret, Tiger-toothed croaker, Javelin grunter, John`s snapper, Indian spiny turbot and Yellowfin seabream 2530, 1172, 1738, 1689, 1470 and 1110 tons, respectively. In general, by assessing the obtained results for the studied species, except the fishing prediction results of the Javelin grunter and Tiger-toothed croaker species, which is less valuable due to the low correlation coefficient, it can be stated that except fishing pressure on John`s snapper and Tiger-toothed croaker, in other species studied, fishing conditions are in optimal situation

A Stable Biologically Motivated Learning Mechanism for Visual Feature Extraction to Handle Facial Categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task

Monitoring of Cochlodinium sp for shrimp farms in Hormozgan Province

The Blooming due to the some species of phytoplanktons especially Dynoflagellates has made some problems for water ecosystems and aquaculture. In this study, the density of phytoplanktons specially Cochlodinium sp. and also environmental factors such as temperature, pH, dissolved Oxygen, and transparency were recorded two weekly in 18 stations of Hormozgan province, Iran in order to monitoring of the possibility of phytoplankton blooming. During six months monitoring, the target phytoplankton, Cochlodinium sp was not observed in shrimp farms. But, other phytoplanktons and zooplanktons were observed as follow: 13 genus of phytoplankton and six genuses of zooplanktons has found in ponds, main water channel and sea. The diatoms with 10 genuses had the highest abundance and Dynoflagellates with 3 genuses had the lowest abundance and blue-green phytoplankton with one genus was in lowest group. Totally, diatoms with 77%, Dynoflagellate with 15% and blue-green alga with 8% abundance were the main populations of planktons in the studied area

How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?

Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition

Intermediate scalings in holographic RG flows and conductivities

We construct numerically finite density domain-wall solutions which interpolate between two AdS 4 fixed points and exhibit an intermediate regime of hyperscaling violation, with or without Lifshitz scaling. Such RG flows can be realized in gravitational models containing a dilatonic scalar and a massive vector field with appropriate choices of the scalar potential and couplings. The infrared AdS 4 fixed point describes a new ground state for strongly coupled quantum systems realizing such scalings, thus avoiding the well-known extensive zero temperature entropy associated with AdS2×R2. We also examine the zero temperature behavior of the optical conductivity in these backgrounds and identify two scaling regimes before the UV CFT scaling is reached. The scaling of the conductivity is controlled by the emergent IR conformal symmetry at very low frequencies, and by the intermediate scaling regime at higher frequencies