## 1 Introduction  
Advances in computational neuroscience and statistical learning have served as an inspiration for artificial neural networks, leading to a Cambrian explosion in the field of machine learning with deep learning architectures and algorithms. However, only a limited set of neural principles have been fully explored in deep learning architectures. Novel approaches which incorporate models closer to brain circuits and mechanisms into neural networks are key to extending the state-of-the-art in deep learning [1; 2; 3; 4; 5; 6; 7; 8; 9]. Brain based object recognition is one key area advancing the state-of-the-art in computer vision. The goal of this work is to understand how a spike based algorithm can detect edges in scenes compared to human engineered image processing techniques and introduce those ideas into deep learning architectures. Our general approach for improving deep learning is modular. We introduce one brain-inspired function that is, spike based computing, at a time or in one layer in the network and understand how it effects the overall network. Our approach does not result in a complete brain-like object recognition in one shot, but we hope it contributes to the transition process from artificial neural networks to brain-like computing neural networks which mimic neuron units as computing blocks.  
Artificial neural networks (ANNs) are based on neurons that use static and continuous valued non-linear activation functions. Biological neurons use spikes to encode, compute, and transmit information using sparse spiking processes, defined by spike timing and spike firing rates. As a result, biological neurons are mostly inactive and consume energy only when there are spike events. Spiking neural networks use neuron models that have a biologically inspired computational or functional representation and are biologically more realistic than ANNs. Spiking neural networks are event driven and are more energy-efficient than ANNs, and are thus suitable for lower Size, Weight and Power (SWaP) artificial intelligence devices based on neuromorphic computing architectures such as IBM TrueNorth [10]. The Hodgkin-Huxley neuron model is the most realistic model of the neuron [11]. While accurate, this model is computationally inefficient. The Izhikevich neuron model is a simplified model based on Hodgkin-Huxley equations. It can successfully reproduce all known neuron firing patterns with a better computational efficiency than the Hodgkin-Huxley model [12]. Hodgkin-Huxley model neurons also play an important role in the research aimed at understanding the efficient processing of images and learning visual orientations in the visual cortex [13].  
We have investigated the scope for computationally efficient use of Hodgkin-Huxley neurons in a spiking neural network for low-SWaP applications in image processing, such as edge detection and machine learning. The state-of-the-art in Spiking Neural Network (SNN) architectures is inspired by Leaky-Integrate-and-Fire type neurons [14; 15]. The Leaky-Integrate-and-Fire neuron model has been extensively used in research. Close variants of the Leaky-Integrate-and-Fire neuron mechanisms have been implemented in cognitive neuromorphic architectures (CNAs), for example in IBM-TrueNorth [10] and in Intel-Loihi [16] neuromorphic processors. The Leaky Integrate-and-Fire model and the Hodgkin-Huxley model are on the opposite ends of the spectrum in terms of accuracy, where the former is simple but lacks detail, and the latter more complicated but also much more accurate. Present models of neurons in CNAs solve a differential equation for the membrane potential at each time step, and it is important to explore ways to incorporate more biologically possible or accurate neuron models for cutting edge applications of artificial intelligence such as geospatial analytics, robotic delivery, transit safety and multi-modal intelligence fusion.