Sounds are processed by the ear and central auditory pathway. These processing steps are biologically complex, and many aspects of the transformation from sound waveforms to cortical response remain unclear. To understand this transformation, we combined models of the auditory periphery with various encoding models to predict auditory cortical responses to natural sounds. The cochlear models ranged from detailed biophysical simulations of the cochlea and auditory nerve to simple spectrogram-like approximations of the information processing in these structures. For three different stimulus sets, we tested the capacity of these models to predict the time course of single-unit neural responses recorded in ferret primary auditory cortex. We found that simple models based on a log-spaced spectrogram with approximately logarithmic compression perform similarly to the best-performing biophysically detailed models of the auditory periphery, and more consistently well over diverse natural and synthetic sounds. Furthermore, we demonstrated that including approximations of the three categories of auditory nerve fiber in these simple models can substantially improve prediction, particularly when combined with a network encoding model. Our findings imply that the properties of the auditory periphery and central pathway may together result in a simpler than expected functional transformation from ear to cortex. Thus, much of the detailed biological complexity seen in the auditory periphery does not appear to be important for understanding the cortical representation of sound.

Auditory neurons encode stimulus history, which is often modelled using a span of time-delays in a spectro-temporal receptive field (STRF). We propose an alternative model for the encoding of stimulus history, which we apply to extracellular recordings of neurons in the primary auditory cortex of anaesthetized ferrets. For a linear-non-linear STRF model (LN model) to achieve a high level of performance in predicting single unit neural responses to natural sounds in the primary auditory cortex, we found that it is necessary to include time delays going back at least 200 ms in the past. This is an unrealistic time span for biological delay lines. We therefore asked how much of this dependence on stimulus history can instead be explained by dynamical aspects of neurons. We constructed a neural-network model whose output is the weighted sum of units whose responses are determined by a dynamic firing-rate equation. The dynamic aspect performs low-pass filtering on each unit’s response, providing an exponentially decaying memory whose time constant is individual to each unit. We find that this dynamic network (DNet) model, when fitted to the neural data using STRFs of only 25 ms duration, can achieve prediction performance on a held-out dataset comparable to the best performing LN model with STRFs of 200 ms duration. These findings suggest that integration due to the membrane time constants or other exponentially-decaying memory processes may underlie linear temporal receptive fields of neurons beyond 25 ms.

Abstract-Brain-Computer Interface (BCI) systems have become one of the valuable research area of ML (Machine Learning) and AI based techniques have brought significant change in traditional diagnostic systems of medical diagnosis. Specially; Electroencephalogram (EEG), which is measured electrical activity of the brain and ionic current in neurons is result of these activities. A brain-computer interface (BCI) system uses these EEG signals to facilitate humans in different ways. P300 signal is one of the most important and vastly studied EEG phenomenon that has been studied in Brain Computer Interface domain. For instance, P300 signal can be used in BCI to translate the subject's intention from mere thoughts using brain waves into actual commands, which can eventually be used to control different electro mechanical devices and artificial human body parts. Since low Signal-to-Noise-Ratio (SNR) in P300 is one of the major challenge because concurrently ongoing heterogeneous activities and artifacts of brain creates lots of challenges for doctors to understand the human intentions. In order to address above stated challenge this research proposes a system so called Adaptive Error Detection method for P300-Based Spelling using Riemannian Geometry, the system comprises of three main steps, in first step raw signal is cleaned by preprocessing. In second step most relevant features are extracted using xDAWN spatial filtering along with covariance matrices for handling high dimensional data and in final step elastic net classification algorithm is applied after converting from Riemannian manifold to Euclidean space using tangent space mapping. Results obtained by proposed method are comparable to state-of-the-art methods, as they decrease time drastically; as results suggest six times decrease in time and perform better during the inter-session and inter-subject variability.

Abstract. The e-commerce industry has seen significant growth over the past few years. One significant issue that has sprung up as a result of this growth is unstructured addresses during last mile delivery. These ambiguous addresses are an established issue, particularly in developing countries like Pakistan. They are difficult to read and locate by last mile delivery riders thereby increasing delivery times and cost, negatively impacting the business of the company. Increased delivery times are also detrimental to the environment. In this paper, we aim to quantify the effects of unstructured addresses on last mile logistics. Many attempts have been made to standardise addresses to tackle this problem. Deep learning based approaches using recurrent neural networks (RNN) as well as probabilistic approaches using hidden Markov models (HMM) have been used. However, the main downside to these approaches are the underlying variation in address schemes in housing societies. We present an end to end rule based pipeline using Levenshtein distance (LD) and regular expressions (RegEx) rules which takes those unstructured addresses and outputs their structured forms along with their Geo-coordinates. The pipeline also returns the optimized route to minimize the last mile distance traveled.

1 model can explain much of the neural sensitivity to stimulus 2 history in primary auditory cortex. ABSTRACT 14 Auditory neurons encode stimulus history, which is often modelled using a span of time-delays 15 in a spectro-temporal receptive field (STRF). We propose an alternative model for the encoding 16 of stimulus history, which we apply to extracellular recordings of neurons in the primary 17 auditory cortex of anaesthetized ferrets. For a linear-non-linear STRF model (LN model) to 18 achieve a high level of performance in predicting single unit neural responses to natural sounds 19 in the primary auditory cortex, we found that it is necessary to include time delays going back 20 at least 200 ms in the past. This is an unrealistic time span for biological delay lines. We 21 therefore asked how much of this dependence on stimulus history can instead be explained by 22 dynamical aspects of neurons. We constructed a neural-network model whose output is the 23 weighted sum of units whose responses are determined by a dynamic firing-rate equation. The 24 dynamic aspect performs low-pass filtering on each unit's response, providing an exponentially 25 decaying memory whose time constant is individual to each unit. We find that this dynamic 26 network (DNet) model, when fitted to the neural data using STRFs of only 25 ms duration, can 27 achieve prediction performance on a held-out dataset comparable to the best performing LN 28 model with STRFs of 200 ms duration. These findings suggest that integration due to the 29 membrane time constants or other exponentially-decaying memory processes may underlie 30 much of the dependence of the neural responses on stimulus history beyond 25 ms. 31 32 33 AUTHOR SUMMARY 34 The responses of neurons in the primary auditory cortex depend on the recent history of sounds 35 over seconds or less. Typically, this dependence on the past has been modelled by applying a 36 wide span of time delays to the input, although this is likely to be biologically unrealistic. Real 37 3 November 4, 2018 neurons integrate the history of their activity due to the dynamical properties of their cell 38 membranes and other components. We show that a network with a realistically narrow span of 39 delays and with units having dynamic characteristics like those found in neurons, succinctly 40 models neural responses recorded from ferret primary auditory cortex. Because these 41 integrative properties are widespread, our dynamic network provides a basis for modelling 42 responses in other neural systems. 43 44 45 105 Cochleagrams 106