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:  Learning algorithms 
1. Abstract of Cited Paper (BIB001):  Neuromorphic engineering (NE) is an emerging research field that has been attempting to identify neural types of computational principles, by implementing biophysically realistic models of neural systems in Very Large Scale Integration (VLSI) technology. Remarkable progress has been made recently, and complex artificial neural sensory-motor systems can be built using this technology. Today, however, NE stands before a large conceptual challenge that must be met before there will be significant progress toward an age of genuinely intelligent neuromorphic machines. The challenge is to bridge the gap from reactive systems to ones that are cognitive in quality. In this paper, we describe recent advancements in NE, and present examples of neuromorphic circuits that can be used as tools to address this challenge. Specifically, we show how VLSI networks of spiking neurons with spike-based plasticity mechanisms and soft winner-take-all architectures represent important building blocks useful for implementing artificial neural systems able to exhibit basic cognitive abilities. 
2. Abstract of Cited Paper (BIB002):  Real-time classification of patterns of spike trains is a difficult computational problem that both natural and artificial networks of spiking neurons are confronted with. The solution to this problem not only could contribute to understanding the fundamental mechanisms of computation used in the biological brain, but could also lead to efficient hardware implementations of a wide range of applications ranging from autonomous sensory-motor systems to brain-machine interfaces. Here we demonstrate real-time classification of complex patterns of mean firing rates, using a VLSI network of spiking neurons and dynamic synapses which implement a robust spike-driven plasticity mechanism. The learning rule implemented is a supervised one: a teacher signal provides the output neuron with an extra input spike-train during training, in parallel to the spike-trains that represent the input pattern. The teacher signal simply indicates if the neuron should respond to the input pattern with a high rate or with a low one. The learning mechanism modifies the synaptic weights only as long as the current generated by all the stimulated plastic synapses does not match the output desired by the teacher, as in the perceptron learning rule. We describe the implementation of this learning mechanism and present experimental data that demonstrate how the VLSI neural network can learn to classify patterns of neural activities, also in the case in which they are highly correlated. 
3. Abstract of Cited Paper (BIB003):  Neurobiological processing systems are remarkable computational devices. They use slow, stochastic, and inhomogeneous computing elements and yet they outperform today's most powerful computers at tasks such as vision, audition, and motor control, tasks that we perform nearly every moment that we are awake without much conscious thought or concern. Despite the vast amount of resources dedicated to the research and development of computing, information, and communication technologies, today's fastest and largest computers are still not able to match biological systems at robustly accomplishing real-world tasks. While the specific algorithms and representations that biological brains use are still largely unknown, it is clear that instead of Boolean logic, precise digital representations, and synchronous operations, nervous systems use hybrid analog/digital components, distributed representations, massively parallel mechanisms, combine communications with memory and computation, and make extensive use of adaptation, self-organization, and learning. On the other hand, as with many successful man-made systems, it is clear that biological brains have been co-designed with the body to operate under a specific range of conditions and assumptions about the world. ::: ::: Understanding the computational principles used by the brain and how they are physically embodied is crucial for developing novel computing paradigms and guiding a new generation of technologies that can combine the strengths of industrial-scale electronics with the computational performance of brains. 
4. Abstract of Cited Paper (BIB004):  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:
As previously mentioned, the two main plasticity mechanisms are STP and LTP. STP is effective for representing temporal signals and dynamics and comes in two different forms, depression and potentiation. The former occurs when postsynaptic potential falls rapidly, i.e., within 1 s or less, during repetitive stimulation, whereas the latter corresponds to a rapid growth in the postsynaptic potential, after repeated occurrences of a stimulus, also resulting in an increase of synaptic efficacy. Circuit implementations for these types of dynamics can be found in BIB004 . On the other hand, LTP occurs at excitatory synapses lasting minutes or more and plays a crucial role in learning. Particularly, in SNNs, spike-timing-dependent plasticity (STDP) constitutes the most intensively studied neural learning mechanism and can be considered as a spike-based formulation of the Hebbian learning rule  . In 1949, Hebb postulated the existence of synaptic strengthening, when a presynaptic neuron repeatedly takes part in firing a postsynaptic one. This process is commonly referred to as long-term potentiation in STDP, whereas long-term depression corresponds to the process in which anti-causal spike sequences with a postsynaptic spike preceding a presynaptic one lead to a decay in the synaptic weight. The change of synaptic connections ðIw=wÞ , where w is the synaptic weight, versus the relative timing of presynaptic and postsynaptic spikes is shown in Figure 5 . STDP mechanisms, effective in learning how to classify spatio-temporal spike patterns, have been implemented in both analog and mixedsignal VLSI technologies, as described in BIB002 . To mitigate the constraints of bounded weight and limited precision of synapses, which was described in the previous section, Brader et al. BIB001 proposed a spike-based plasticity rule with bistable synaptics, which was implemented in silicon BIB002 , BIB003 . This rule uses two stable states for every synapse, i.e., the depressing and potentiated states, using a stochastic mechanism for transitioning between them, based on spiking history. The protocol modifies only a random portion of all stimulated synapses with a small probability, significantly increasing storage capacity and lifetime memory of SNNs, which has been shown to increase inversely proportional with the probability of synaptic modification BIB005 . Unlike the STDP protocol, synaptic weight updates depend on: 1) the timing of the presynaptic spike; 2) the state of the postsynaptic neuron potential; and 3) a slow variable, related to the calcium concentration in biological neurons, proportional to the postsynaptic neuron mean firing rate. Brader's model also implements a "stoplearning" mechanism, preventing overfitting when the input pattern is highly correlated to the pattern stored in the synaptic weights. Overall, this bistable synaptic model is able to reproduce the STDP protocol and supports both unsupervised and supervised learning.