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:  Neuron circuits 
1. Abstract of Cited Paper (BIB001):  We present an electronic circuit modelling the spike generation process in the biological neuron. This simple circuit is capable of simulating the spiking behaviour of several different types of biological neurons. At the same time, the circuit is small so that many neurons can be implemented on a single silicon chip. This is important, as neural computation obtains its power not from a single neuron, but from the interaction between a large number of neurons. Circuits that model these interactions are also presented in this paper. They include the circuits for excitatory, inhibitory and shunting inhibitory synapses, a circuit which models the regeneration of spikes on the axon, and a circuit which models the reduction of input strength with the distance of the synapse to the cell body on the dendrite of the cell. Together these building blocks allow the implementation of electronic spiking neural networks. 
2. Abstract of Cited Paper (BIB002):  We present a mixed-mode analog/digital VLSI device comprising an array of leaky integrate-and-fire (I&F) neurons, adaptive synapses with spike-timing dependent plasticity, and an asynchronous event based communication infrastructure that allows the user to (re)configure networks of spiking neurons with arbitrary topologies. The asynchronous communication protocol used by the silicon neurons to transmit spikes (events) off-chip and the silicon synapses to receive spikes from the outside is based on the "address-event representation" (AER). We describe the analog circuits designed to implement the silicon neurons and synapses and present experimental data showing the neuron's response properties and the synapses characteristics, in response to AER input spike trains. Our results indicate that these circuits can be used in massively parallel VLSI networks of I&F neurons to simulate real-time complex spike-based learning algorithms. 
3. Abstract of Cited Paper (BIB003):  A mixed-signal very large scale integration (VLSI) chip for large scale emulation of spiking neural networks is presented. The chip contains 2400 silicon neurons with fully programmable and reconfigurable synaptic connectivity. Each neuron implements a discrete-time model of a single-compartment cell. The model allows for analog membrane dynamics and an arbitrary number of synaptic connections, each with tunable conductance and reversal potential. The array of silicon neurons functions as an address-event (AE) transceiver, with incoming and outgoing spikes communicated over an asynchronous event-driven digital bus. Address encoding and conflict resolution of spiking events are implemented via a randomized arbitration scheme that ensures balanced servicing of event requests across the array. Routing of events is implemented externally using dynamically programmable random-access memory that stores a postsynaptic address, the conductance, and the reversal potential of each synaptic connection. Here, we describe the silicon neuron circuits, present experimental data characterizing the 3 mm times 3 mm chip fabricated in 0.5-mum complementary metal-oxide-semiconductor (CMOS) technology, and demonstrate its utility by configuring the hardware to emulate a model of attractor dynamics and waves of neural activity during sleep in rat hippocampus 
4. Abstract of Cited Paper (BIB004):  Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips. 
5. Abstract of Cited Paper (BIB005):  Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks, with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm(2), and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.

Literature Review:
The history of artificial neurons extends back to the 1940s with the first model proposed in 1943 by McCulloch and Pitts and its implementation appearing soon after. A first approach to neurorobotics was made in the 1950s by Walter implementing an electronic tube-based, neuronlike element to control a simple mobile robot. In 1958, Rosenblatt introduced the perceptron, an artificial neuron with a learning rule for classification tasks, which was also implemented in hardware. Considering the neurobiological perspective, in 1952, Hodgkin and Huxley described the electrical activity of squid axons in a series of papers BIB001 , eventually receiving the Nobel Prize in 1963. They showed that two types of channels are essential to generate an action potential (neuronal voltage response, whose typical form is shown in Figure 2 from the snail helisoma trivolvis) and they developed an electrical model to describe them. This model, which is shown in Figure 3 , has become a typical circuit model simulating the physical neural activity. Despite its simplicity, this model has a key drawback for the circuit designer: the variable resistors (or conductances) g K and g Na are difficult to realize using simple circuit elements. Inspired by the natural neural performance, extensive research efforts have been invested on spiking neurons and their hardware implementations. Based on the conductance model of Hodgkin and Huxley, Mahowald and Douglas proposed a SiN circuit with properties quite similar to those of real cortical neurons  . Other implementations of conductance-based models have also been proposed BIB004 ,  . The major drawback of these models is the silicon area they require, which makes them impractical for large neural network implementations. Simpler models are the Axon-Hillock circuit (Figure 4 ), proposed by Mead in the late 1980s  . In this circuit, an integrating capacitor is connected to two inverters, a feedback capacitor, and a reset transistor driven by the output inverter. The Axon-Hillock circuit is very compact and allows for large dense SiN arrays, but it suffers from a drawback of large power consumption due to the slow transition time (time constants in the order of milliseconds) imposed to the internal digital inverters, which are used as amplifiers. A further drawback is that it has a spiking threshold that only depends on CMOS process parameters (i.e., the switching threshold of the inverter) and does not model additional neural characteristics, such as spikefrequency adaptation properties or refractory period mechanisms BIB003 . A newer implementation is found in BIB002 , consuming less power than previously proposed ones, but still lacking spikefrequency adaptation. Several other variants have also been proposed for modeling many additional neural characteristics which are not included in the simple initial model. A recent circuit introduces a compact leaky I&F circuit which is optimized for power consumption and which implements spike-frequency adaptation, as well as tunable refractory period and voltage threshold modulation BIB003 . An even more recent neuron circuit integrated in the ROLLS neuromorphic processor chip BIB006 is derived from the adaptive exponential I&F circuit and can exhibit a wide range of neural behaviors, such as spike-frequency adaptation properties, refractory period mechanism, and adjustable spiking threshold mechanism. Analog neuron implementations lying between the biology-inspired yet complex Hodgkin and Huxley model and the simplified Axon-Hillock circuit have also been developed. An extensive overview of such propositions can be found in BIB005 .