You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Toward Silicon-Based Cognitive Neuromorphic ICs—A Survey 
Chapter Title:  Discussion 
1. Abstract of Cited Paper (BIB001):  Several analog and digital brain-inspired elec- tronic systems have been recently proposed as dedicated solutions for fast simulations of spiking neural networks. While these architectures are useful for exploring the computational properties of large-scale models of the nervous system, the challenge of building low-power compact physical artifacts that can behave intelligently in the real world and exhibit cognitive abilities still remains open. In this paper, we propose a set of neuromorphic engineering solutions to address this challenge. In particular, we review neuromorphic circuits for emulating neural and synaptic dynamics in real time and discuss the role of biophysically realistic temporal dynamics in hardware neural processing architectures; we review the challenges of realizing spike-based plasticity mechanisms in real physical systems and present examples of analog electronic circuits that implement them; we describe the computational properties of recurrent neural networks and show how neuromorphic winner-take-all circuits can implement working-memory and decision-making mechanisms. We validate the neuromorphic approach proposed with experimental results obtained from our own circuits and systems, and argue how the circuits and networks presented in this work represent a useful set of components for efficiently and elegantly implementing neuro- morphic cognition.

Literature Review:
In the low-power, mixed-signal, neuromorphic implementations surveyed herein, analog CMOS circuits are used for the design of SiNs and synapses. These silicon circuits typically operate in the subthreshold region and are, thus, susceptible to 1) noise and 2) mismatch, which become dominant when the device operating voltage falls well below the device threshold voltage. A common mismatch reduction technique is to increase device dimensions. However, in the case of large neuromorphic networks with many neurons and synapses, this is impractical, since the area required for integrating these structures into a single chip would become prohibitive. To alleviate this problem, processes that are dedicated to lowpower, subthreshold, system-on-chip (SoC) circuits, e.g., depleted silicon-on-insulator (SOI), thereby reducing the threshold voltage variation, have been available for implementing neuromorphic analog VLSI circuits BIB001 . In BIB002 , the inherent CMOS device mismatch is addressed at the network and system level, where it is argued that plasticity mechanisms (both STP and LTP) are robust to device mismatch and do not require precisely matched transistors. THE NE COMMUNITY has made a remarkable progress in building technology platforms for simulating neurobiological models. However, additional steps are required toward the ultimate goal of implementing hardware neural processing systems which are: 1) autonomous; 2) able to interact with the environment in real time; and 3) able to express cognitive abilities. These steps are not hinging upon scaling or hardware restrictions but, rather, on a better understanding of computational principles used by the brain and computational models that neurons can support. Thus, the role of neuroscience and psychology in identifying these principles is crucial, before neuromorphic cognitive systems can become a reality.