Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

The primate visual system builds robust, multi-purpose representations of the external world in order to support several diverse downstream cortical processes.
Such representations are required to be invariant to the sensory inconsistencies caused by dynamically varying lighting, local texture distortion, etc.
A key architectural feature combating such environmental irregularities is ‘long-range horizontal connections’ that aid the perception of the global form of objects.
In this work, we explore the introduction of such horizontal connections into standard deep convolutional networks; we present V1Net -- a novel convolutional-recurrent unit that models linear and nonlinear horizontal inhibitory and excitatory connections inspired by primate visual cortical connectivity.
We introduce the Texturized Challenge -- a new benchmark to evaluate object recognition performance under perceptual noise -- which we use to evaluate V1Net against an array of carefully selected control models with/without recurrent processing.
Additionally, we present results from an ablation study of V1Net demonstrating the utility of diverse neurally inspired horizontal connections for state-of-the-art AI systems on the task of object boundary detection from natural images.
We also present the emergence of several biologically plausible horizontal connectivity patterns, namely center-on surround-off, association fields and border-ownership connectivity patterns in a V1Net model trained to perform boundary detection on natural images from the Berkeley Segmentation Dataset 500 (BSDS500).
Our findings suggest an increased representational similarity between V1Net and biological visual systems, and highlight the importance of neurally inspired recurrent contextual processing principles for learning visual representations that are robust to perceptual noise and furthering the state-of-the-art in computer vision.
Following Hubel and Wiesel's (Hubel & Wiesel, 1968) seminal work on characterizing receptive fields in the cat striate cortex and Fukushima's Neocognitron (Fukushima, 1980 ) (a hierarchical extension of this building block), two broad families of visual models have been developed by the neuroscience and computer vision communities respectively.
The former family of models aims to account for findings from single-cell neurophysiology, either by directly modeling types of neuronal responses (De Valois et al., 1982; Sullivan & De Sa, 2006; Chichilnisky, 2001; Pillow, 2007) or by proposing computational models that give rise to similar neural phenomena (Olshausen & Field, 1997; Schwartz et al., 2006) .
The latter family of models, particularly Deep Convolutional Networks (DCNs) (LeCun et al., 1998) , are also loosely inspired by Hubel & Wiesel (1968) and Fukushima (1980) ; they aim to optimize performance on a wide range of computer vision benchmarks including but not limited to image recognition (Krizhevsky et al., 2012; Hu et al., 2018; Simonyan & Zisserman, 2014) , contour detection (Xie & Tu, 2015; Shen et al., 2015) and object segmentation (He et al., 2017; Long et al., 2015) .
Despite their impressive performance on benchmarks in these areas, these models progressively deviate from biological vision and recent work highlights their interesting deficiencies and sensitivities relative to primate vision (Geirhos et al., 2019; Papernot et al., 2016; Kurakin et al., 2016; Eykholt et al., 2017) .
Our motivation is to reverse-engineer the cortical contextual processing principles that have been shown to be mediated by long-range horizontal connections using DCNs.
Horizontal connections are capable of systematically filling-in sensory inconsistencies and spatially binding neighbouring features together in order to provide stable perception.
In an attempt to complement DCNs with this property and to contribute towards bridging the gap between artificial and biological visual representations, we develop 'V1Net', a novel recurrent unit inspired by visual neuroscience and Gestalt psychology literature on cortical horizontal connectivity (Das & Gilbert, 1995; Grossberg & Mingolla, 1985) .
V1Net can be flexibly incorporated as a module in existing implementations of DCNs.
In the following Section 2, we briefly survey the existing literature on biologically plausible vision models that are related to V1Net.
In Section 3, we introduce the V1Net model along with it's mathematical formulation and an intuitive explanation of its working.
In Section 4, we demonstrate experimental results from: (1) Texturized Challenge, our proposed benchmark to evaluate object recognition ability under perceptual noise and (2) an ablation study of V1Net's horizontal connections using the BSDS500 boundary detection benchmark (Arbelaez et al., 2011) .
Subsequently, we demonstrate several biologically plausible horizontal connections emerging in a V1Net while learning the task of boundary detection.
We also share qualitative results of V1Net's zero-shot domain transfer on the task of object boundary detection from natural images (used for training) to stylized images (Gatys et al., 2015) .
Finally, we discuss our current plans for extending the work.
Horizontal connections have been a key interest to the field of visual neuroscience, owing to their diverse functionality that gives rise to invariant visual representations.
To this end, we develop a neurally-inspired recurrent neural network model of long-range horizontal connections and demonstrate its importance also to artificial vision systems for giving rise to robust visual representations through a systematic comparison against parameter-matched control models on our proposed Texturized challenge and the BSDS500 object boundary detection benchmark.
We subsequently demonstrate the emergence of biologically plausible horizontal connection patterns from V1Net suggesting a strong representational similarity between V1Net and primate early visual areas.
We also present qualitative results of a V1Net model generalizing in a zero-shot fashion to images with stylized texture.
Continuing in this direction of using inspiration from biological vision to advance artificial vision, we are currently working on incorporating learned top-down feedback connections in a deep convolutional network along with V1Net.
We believe that such re-entrant connections along with horizontal connections will allow smooth flow of information within-and across different spatial resolutions of the feature hierarchy contributing to more robust and efficient semantic segmentation models that generalize better than their feedforward counterparts.
In parallel, we are actively working on quantitatively analyzing the match between V1Net's internal representations and single-cell experimental recordings collected from the primate visual cortex.
A IMPLEMENTATION DETAILS
In this work, we present V1Net -- a novel recurrent neural network modeling cortical horizontal connections that give rise to robust visual representations through perceptual grouping.