Goods and Services Tax (GST) Implementation in India: A SAP-LAP-Twitter Analytic Perspective.

In a federal structure, India's determination to much-needed fiscal reforms has been widely applauded at its face value when she relinquished her previous complex and inefficient tax regime to embrace the long-awaited Goods and Services Tax (GST). It has been a significant economic move post-independence and requires validation of facts after its introduction. The present study aims to present a general macroeconomic analysis of the extent to which the adoption of GST has improved existing tax administration and resultant general economic well-being of a democratic political economy like India in light of innovation implementation theoretical perspective. Further, the study tried to determine how the stakeholders perceived such big-bang reform even after the three years of its adoption. The study attempted to assess to what extent the adoption of GST has indeed influenced the economy in general and citizens and/or consumers in particular while using a case-based qualitative inquiry. The present research applied the situation-actor-process; learning-action-performance analysis framework for the case analysis. The facts reveal that India has observed a tremendous increase in tax base vis-à-vis revenue collection. Yet, some efforts are desired to improve the low tax to GDP ratio, skewed GST payers base, negative stakeholders' perception of GST (revealed through Twitter sentiment analysis), and the evil of tax evasion. The other merits realized by the economy are presented as benefits to the consumers, MSMEs, improved ease of doing business ranking, and foster make-in-India and AatmanirbharBharat move by the government.

Motor cortex microcircuit simulation based on brain activity mapping.

The deceptively simple laminar structure of neocortex belies the complexity of intra- and interlaminar connectivity. We developed a computational model based primarily on a unified set of brain activity mapping studies of mouse M1. The simulation consisted of 775 spiking neurons of 10 cell types with detailed population-to-population connectivity. Static analysis of connectivity with graph-theoretic tools revealed that the corticostriatal population showed strong centrality, suggesting that would provide a network hub. Subsequent dynamical analysis confirmed this observation, in addition to revealing network dynamics that cannot be readily predicted through analysis of the wiring diagram alone. Activation thresholds depended on the stimulated layer. Low stimulation produced transient activation, while stronger activation produced sustained oscillations where the threshold for sustained responses varied by layer: 13% in layer 2/3, 54% in layer 5A, 25% in layer 5B, and 17% in layer 6. The frequency and phase of the resulting oscillation also depended on stimulation layer. By demonstrating the effectiveness of combined static and dynamic analysis, our results show how static brain maps can be related to the results of brain activity mapping.

Modeling molecular pathways of neuronal ischemia.

Neuronal ischemia, the consequence of a stroke (cerebrovascular accident), is a condition of reduced delivery of nutrients to brain neurons. The brain consumes more energy per gram of tissue than any other organ, making continuous blood flow critical. Loss of nutrients, most critically glucose and O2, triggers a large number of interacting molecular pathways in neurons and astrocytes. The dynamics of these pathways take place over multiple temporal scales and occur in multiple interacting cytosolic and organelle compartments: in mitochondria, endoplasmic reticulum, and nucleus. The complexity of these relationships suggests the use of computer simulation to understand the interplay between pathways leading to reversible or irreversible damage, the forms of damage, and interventions that could reduce damage at different stages of stroke. We describe a number of models and simulation methods that can be used to further our understanding of ischemia.

Mathematical analysis and algorithms for efficiently and accurately implementing stochastic simulations of short-term synaptic depression and facilitation.

The release of neurotransmitter vesicles after arrival of a pre-synaptic action potential (AP) at cortical synapses is known to be a stochastic process, as is the availability of vesicles for release. These processes are known to also depend on the recent history of AP arrivals, and this can be described in terms of time-varying probabilities of vesicle release. Mathematical models of such synaptic dynamics frequently are based only on the mean number of vesicles released by each pre-synaptic AP, since if it is assumed there are sufficiently many vesicle sites, then variance is small. However, it has been shown recently that variance across sites can be significant for neuron and network dynamics, and this suggests the potential importance of studying short-term plasticity using simulations that do generate trial-to-trial variability. Therefore, in this paper we study several well-known conceptual models for stochastic availability and release. We state explicitly the random variables that these models describe and propose efficient algorithms for accurately implementing stochastic simulations of these random variables in software or hardware. Our results are complemented by mathematical analysis and statement of pseudo-code algorithms.

Interaction of short-term depression and firing dynamics in shaping single neuron encoding.

We investigated how the two properties short-term synaptic depression of afferent input and postsynaptic firing dynamics combine to determine the operating mode of a neuron. While several computational roles have been ascribed to either, their interaction has not been studied. We considered two types of short-term synaptic dynamics (release-dependent and release-independent depression) and two classes of firing dynamics (regular firing and firing with spike-frequency adaptation). The input-output transformation of the four possible combinations of pre- and post-synaptic dynamics was characterized. Adapting neurons receiving input from release-dependent synapses functioned largely as coincidence detectors. The other three configurations showed properties consistent with integrators, each with distinct features. These results suggest that the operating mode of a neuron is determined by both the pre- and post-synaptic dynamics and that studying them together is necessary to understand emergent properties and their implications for neuronal coding.

Input-rate modulation of γ oscillations is sensitive to network topology, delays and short-term plasticity.

Simulated networks of excitatory and inhibitory neurons have previously been shown to reproduce critical features of experimental data regarding neural coding in V1, such as a positive relationship between thalamic input spike rate and the power of gamma frequency oscillations. This effect, referred to as modulated gamma power, could represent a neural code in V1 for stimulus characteristics that affect thalamic spike rate such as contrast or intensity. The simulated network's assumptions included homogeneous random connectivity, equal synaptic delays after spike arrival, and constant synaptic efficacies. Plausible alternative assumptions include small world connectivity, a wide distribution of axonal propagation delays, and short term synaptic plasticity, and here we assess the individual impact of each of these on the model's success in reproducing modulated gamma power. First, we developed several alternative algorithms for simulating directed networks with clustered connectivity and balanced excitation and inhibition. We found that modulated gamma power was absent in all small-world networks that had a relatively low abundance of reciprocal connectivity, which suggests that such motifs are present in V1 cortical networks at levels at least equal to those found in random networks. We also found in a different network type that the balance of excitation and inhibition could be destroyed when the network was in the small-world regime. Given all neurons had identical in-degrees, this result suggests that balance relies on motif distributions as well as mean connectivity. Second, altering the distribution of axonal delays had little effect, but increasing the mean delay led to a secondary gamma modulation at harmonics of the main peak, and since this is not observed experimentally, it suggests a mean delay in V1 networks less than 2 ms. Finally, we compared two types of excitatory synaptic plasticity, and found that modulated beta power emerged in addition to gamma power for one type, in the presence of short term depression in interneurons. This article is part of a Special Issue entitled "Neural Coding".