Modeling the development of cortical responses in primate dorsal (“where”) pathway to optic flow using hierarchical neural field models

Although there is a plethora of modeling literature dedicated to the object recognition processes of the ventral (“what”) pathway of primate visual systems, modeling studies on the motion-sensitive regions like the Medial superior temporal area (MST) of the dorsal (“where”) pathway are relatively scarce. Neurons in the MST area of the macaque monkey respond selectively to different types of optic flow sequences such as radial and rotational flows. We present three models that are designed to simulate the computation of optic flow performed by the MST neurons. Model-1 and model-2 each composed of three stages: Direction Selective Mosaic Network (DSMN), Cell Plane Network (CPNW) or the Hebbian Network (HBNW), and the Optic flow network (OF). The three stages roughly correspond to V1-MT-MST areas, respectively, in the primate motion pathway. Both these models are trained stage by stage using a biologically plausible variation of Hebbian rule. The simulation results show that, neurons in model-1 and model-2 (that are trained on translational, radial, and rotational sequences) develop responses that could account for MSTd cell properties found neurobiologically. On the other hand, model-3 consists of the Velocity Selective Mosaic Network (VSMN) followed by a convolutional neural network (CNN) which is trained on radial and rotational sequences using a supervised backpropagation algorithm. The quantitative comparison of response similarity matrices (RSMs), made out of convolution layer and last hidden layer responses, show that model-3 neuron responses are consistent with the idea of functional hierarchy in the macaque motion pathway. These results also suggest that the deep learning models could offer a computationally elegant and biologically plausible solution to simulate the development of cortical responses of the primate motion pathway

Role of non-lipid risk factors like hs-CRP, uric acid and thyroid stimulating hormone in metabolic syndrome

Background: Metabolic syndrome is the cluster of diseases which arises due to excess of plasma glucose, cholesterol, fatty acids, blood pressure and obesity. The role of lipids in the development of MetS had been extensively studied. Though some non-lipid factors like hsCRP, uric acid and TSH level also remain elevated in the serum of the MetS patients, the role of these non-lipid risk factors remain incompletely understood. The objective of this study was to investigate which of these factors better predicts Mets, in order to help prevention and early detection of MetS and its associated type 2 diabetes mellitus and cardiovascular diseases. Aim and objectives was to study the significance of serum highly sensitive C-reactive protein (hs-CRP), serum uric acid (SUA) and thyroid stimulating hormone (TSH) levels in metabolic syndrome.Methods: A total of four hundred and fifty subjects (211 men and 239 women) aged ≥35 years attending the hospital were divided into three groups based on the components level of MetS as control (CS), normal (MS) and severe (SMS) MetS groups. Their fasting blood sample were taken and analyzed for the serum hs-CRP, uric acid and TSH levels. The result showed that the mean hs-CRP and uric acid levels were significantly higher in Metabolic Syndrome group (MS) and in Severe Metabolic Syndrome group (SMS) when compared to control group. But the mean TSH levels were more in MS group and in SMS group than the control which was statistically not significant. The analysis of relative significance of these risk factors showed that serum hs-CRP level had a positive linear correlation with the severity of MetS whereas, the TSH level was significantly high only in SMS and the uric acid level was not correlated with the MetS.Results: Our study revealed that type II (absence of sutural bones) was commoner than type I (presence of type I) asterion. The asterion was 4.82±0.58 cm from tip of the mastoid process on the right side and 4.70±0.70 cm on the left. It was greater in males than in females, p value being statistically significant (P = 0.00 and P = 0.02 for right and left sides respectively). The distance of asterion from supramastoid crest was 4.22±0.73 cm on the right and 4.23±0.58 cm on the left. The distance in males was more than in females. The P value 0.00 was statistically significant on the right side. Regarding the position of the asterion in relation to transverse sinus, it was on the transverse sinus in 62% cases, below it in 32% and above in 6%.Conclusions: In the present study, there was higher mean serum hs-CRP level in patients with metabolic syndrome which showed a linear increase with increasing number of components of the metabolic syndrome. Though available literature indicated that hyperuricemia adult subjects tend to develop MetS more frequently our findings showed this increase was not dependent on the severity of MetS. Also, significantly high TSH levels were found only in severe MetS suggest that as per this study the serum hs-CRP values may be consider as the diagnostic criteria for metabolic syndrome and helps to improve future prediction of development of type 2 DM and cardiovascular diseases

An end-to-end, interactive Deep Learning based Annotation system for cursive and print English handwritten text

With the surging inclination towards carrying out tasks on computational devices and digital mediums, any method that converts a task that was previously carried out manually, to a digitized version, is always welcome. Irrespective of the various documentation tasks that can be done online today, there are still many applications and domains where handwritten text is inevitable, which makes the digitization of handwritten documents a very essential task. Over the past decades, there has been extensive research on offline handwritten text recognition. In the recent past, most of these attempts have shifted to Machine learning and Deep learning based approaches. In order to design more complex and deeper networks, and ensure stellar performances, it is essential to have larger quantities of annotated data. Most of the databases present for offline handwritten text recognition today, have either been manually annotated or semi automatically annotated with a lot of manual involvement. These processes are very time consuming and prone to human errors. To tackle this problem, we present an innovative, complete end-to-end pipeline, that annotates offline handwritten manuscripts written in both print and cursive English, using Deep Learning and User Interaction techniques. This novel method, which involves an architectural combination of a detection system built upon a state-of-the-art text detection model, and a custom made Deep Learning model for the recognition system, is combined with an easy-to-use interactive interface, aiming to improve the accuracy of the detection, segmentation, serialization and recognition phases, in order to ensure high quality annotated data with minimal human interaction.Comment: 17 pages, 8 figures, 2 table

Electrode Position and Current Amplitude Modulate Impulsivity after Subthalamic Stimulation in Parkinsons Disease—A Computational Study

Background: Subthalamic Nucleus Deep Brain Stimulation (STN-DBS) is highly effective in alleviating motor symptoms of Parkinson’s disease (PD) which are not optimally controlled by dopamine replacement therapy. Clinical studies and reports suggest that STN-DBS may result in increased impulsivity and de novo impulse control disorders (ICD)Objective/Hypothesis: We aimed to compare performance on a decision making task, the Iowa Gambling Task (IGT), in healthy conditions (HC), untreated and medically-treated PD conditions with and without STN stimulation. We hypothesized that the position of electrode and stimulation current modulate impulsivity after STN-DBS.Methods: We built a computational spiking network model of basal ganglia (BG) and compared the model’s STN output with STN activity in PD. Reinforcement learning methodology was applied to simulate IGT performance under various conditions of dopaminergic and STN stimulation where IGT total and bin scores were compared among various conditions.Results: The computational model reproduced neural activity observed in normal and PD conditions. Untreated and medically-treated PD conditions had lower total IGT scores (higher impulsivity) compared to HC (P<0.0001). The electrode position that happens to selectively stimulate the part of the STN corresponding to an advantageous panel on IGT resulted in de-selection of that panel and worsening of performance (P<0.0001). Supratherapeutic stimulation amplitudes also worsened IGT performance (P<0.001). Conclusion(s): In our computational model, STN stimulation led to impulsive decision making in IGT in PD condition. Electrode position and stimulation current influenced impulsivity which may explain the variable effects of STN-DBS reported in patients

Saccade Velocity Driven Oscillatory Network Model of Grid Cells

Grid cells and place cells are believed to be cellular substrates for the spatial navigation functions of hippocampus as experimental animals physically navigated in 2D and 3D spaces. However, a recent saccade study on head fixated monkey has also reported grid-like representations on saccadic trajectory while the animal scanned the images on a computer screen. We present two computational models that explain the formation of grid patterns on saccadic trajectory formed on the novel Images. The first model named Saccade Velocity Driven Oscillatory Network -Direct PCA (SVDON—DPCA) explains how grid patterns can be generated on saccadic space using Principal Component Analysis (PCA) like learning rule. The model adopts a hierarchical architecture. We extend this to a network model viz. Saccade Velocity Driven Oscillatory Network—Network PCA (SVDON-NPCA) where the direct PCA stage is replaced by a neural network that can implement PCA using a neurally plausible algorithm. This gives the leverage to study the formation of grid cells at a network level. Saccade trajectory for both models is generated based on an attention model which attends to the salient location by computing the saliency maps of the images. Both models capture the spatial characteristics of grid cells such as grid scale variation on the dorso-ventral axis of Medial Entorhinal cortex. Adding one more layer of LAHN over the SVDON-NPCA model predicts the Place cells in saccadic space, which are yet to be discovered experimentally. To the best of our knowledge, this is the first attempt to model grid cells and place cells from saccade trajectory

A Model of Motion Processing in the Visual Cortex Using Neural Field With Asymmetric Hebbian Learning

Neurons in the dorsal pathway of the visual cortex are thought to be involved in motion processing. The first site of motion processing is the primary visual cortex (V1), encoding the direction of motion in local receptive fields, with higher order motion processing happening in the middle temporal area (MT). Complex motion properties like optic flow are processed in higher cortical areas of the Medial Superior Temporal area (MST). In this study, a hierarchical neural field network model of motion processing is presented. The model architecture has an input layer followed by either one or cascade of two neural fields (NF): the first of these, NF1, represents V1, while the second, NF2, represents MT. A special feature of the model is that lateral connections used in the neural fields are trained by asymmetric Hebbian learning, imparting to the neural field the ability to process sequential information in motion stimuli. The model was trained using various traditional moving patterns such as bars, squares, gratings, plaids, and random dot stimulus. In the case of bar stimuli, the model had only a single NF, the neurons of which developed a direction map of the moving bar stimuli. Training a network with two NFs on moving square and moving plaids stimuli, we show that, while the neurons in NF1 respond to the direction of the component (such as gratings and edges) motion, the neurons in NF2 (analogous to MT) responding to the direction of the pattern (plaids, square object) motion. In the third study, a network with 2 NFs was simulated using random dot stimuli (RDS) with translational motion, and show that the NF2 neurons can encode the direction of the concurrent dot motion (also called translational flow motion), independent of the dot configuration. This translational RDS flow motion is decoded by a simple perceptron network (a layer above NF2) with an accuracy of 100% on train set and 90% on the test set, thereby demonstrating that the proposed network can generalize to new dot configurations. Also, the response properties of the model on different input stimuli closely resembled many of the known features of the neurons found in electrophysiological studies

An Oscillatory Neural Autoencoder Based on Frequency Modulation and Multiplexing

Oscillatory phenomena are ubiquitous in the brain. Although there are oscillator-based models of brain dynamics, their universal computational properties have not been explored much unlike in the case of rate-coded and spiking neuron network models. Use of oscillator-based models is often limited to special phenomena like locomotor rhythms and oscillatory attractor-based memories. If neuronal ensembles are taken to be the basic functional units of brain dynamics, it is desirable to develop oscillator-based models that can explain a wide variety of neural phenomena. Autoencoders are a special type of feed forward networks that have been used for construction of large-scale deep networks. Although autoencoders based on rate-coded and spiking neuron networks have been proposed, there are no autoencoders based on oscillators. We propose here an oscillatory neural network model that performs the function of an autoencoder. The model is a hybrid of rate-coded neurons and neural oscillators. Input signals modulate the frequency of the neural encoder oscillators. These signals are then multiplexed using a network of rate-code neurons that has afferent Hebbian and lateral anti-Hebbian connectivity, termed as Lateral Anti Hebbian Network (LAHN). Finally the LAHN output is de-multiplexed using an output neural layer which is a combination of adaptive Hopf and Kuramoto oscillators for the signal reconstruction. The Kuramoto-Hopf combination performing demodulation is a novel way of describing a neural phase-locked loop. The proposed model is tested using both synthetic signals and real world EEG signals. The proposed model arises out of the general motivation to construct biologically inspired, oscillatory versions of some of the standard neural network models, and presents itself as an autoencoder network based on oscillatory neurons applicable to time series signals. As a demonstration, the model is applied to compression of EEG signals