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: Multi-Channel Neural Recording Implants: A Review 
Chapter Title:  Neural Signals 
1. Abstract of Cited Paper (BIB001):  A tungsten microelectrode with several small holes burnt in the vinyl insulation enables the action potentials from several adjacent neurons to be observed simultaneously. A digital computer is used to separate the contributions of each neuron by examining and classifying the waveforms of the action potentials. These methods allow studies to be made of interactions between neurons that lie close together. 
2. Abstract of Cited Paper (BIB002):  How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron-electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties. 
3. Abstract of Cited Paper (BIB003):  The intricate coupling between electrical and chemical activity in neural pathways of the central nervous system, and the implication of this coupling in neuropathologies, such as Parkinson's disease, motivates simultaneous monitoring of neurochemical and neuropotential signals. However, to date, neurochemical sensing has been lacking in integrated clinical instrumentation as well as in brain-computer interfaces (BCI). Here, we present an integrated system capable of continuous acquisition of data modalities in awake, behaving subjects. It features one channel each of a configurable neuropotential and a neurochemical acquisition system. The electrophysiological channel is comprised of a 40-dB gain, fully differential amplifier with tunable bandwidth from 140 Hz to 8.2 kHz. The amplifier offers input-referred noise below 2 muV rms for all bandwidth settings. The neurochemical module features a picoampere sensitivity potentiostat with a dynamic range spanning six decades from picoamperes to microamperes. Both systems have independent on-chip, configurable DeltaSigma analog-to-digital converters (ADCs) with programmable digital gain and resolution. The system was also interfaced to a wireless power harvesting and telemetry module capable of powering up the circuits, providing clocks for ADC operation, and telemetering out the data at up to 32 kb/s over 3.5 cm with a bit-error rate of less than 10-5. Characterization and experimental results from the electrophysiological and neurochemical modules as well as the full system are presented. 
4. Abstract of Cited Paper (BIB004):  Recording from neuronal populations is a promising and powerful neuroscience technique; however, interpreting the resulting spike trains presents several challenges. Quian Quiroga and Panzeri discuss how decoding algorithms and information theory can be used to extract information from population recordings. 
5. Abstract of Cited Paper (BIB005):  Acquiring neural signals at high spatial and temporal resolution directly from brain microcircuits and decoding their activity to interpret commands and/or prior planning activity, such as motion of an arm or a leg, is a prime goal of modern neurotechnology. Its practical aims include assistive devices for subjects whose normal neural information pathways are not functioning due to physical damage or disease. On the fundamental side, researchers are striving to decipher the code of multiple neural microcircuits which collectively make up nature's amazing computing machine, the brain. By implanting biocompatible neural sensor probes directly into the brain, in the form of microelectrode arrays, it is now possible to extract information from interacting populations of neural cells with spatial and temporal resolution at the single cell level. With parallel advances in application of statistical and mathematical techniques tools for deciphering the neural code, extracted populations or correlated neurons, significant understanding has been achieved of those brain commands that control, e.g., the motion of an arm in a primate (monkey or a human subject). These developments are accelerating the work on neural prosthetics where brain derived signals may be employed to bypass, e.g., an injured spinal cord. One key element in achieving the goals for practical and versatile neural prostheses is the development of fully implantable wireless microelectronic ?brain-interfaces? within the body, a point of special emphasis of this paper. 
6. Abstract of Cited Paper (BIB006):  BACKGROUND ::: The field of neural prosthetics aims to develop prosthetic limbs with a brain-computer interface (BCI) through which neural activity is decoded into movements. A natural extension of current research is the incorporation of neural activity from multiple modalities to more accurately estimate the user's intent. The challenge remains how to appropriately combine this information in real-time for a neural prosthetic device. ::: ::: ::: METHODOLOGY/PRINCIPAL FINDINGS ::: Here we propose a framework based on decision fusion, i.e., fusing predictions from several single-modality decoders to produce a more accurate device state estimate. We examine two algorithms for continuous variable decision fusion: the Kalman filter and artificial neural networks (ANNs). Using simulated cortical neural spike signals, we implemented several successful individual neural decoding algorithms, and tested the capabilities of each fusion method in the context of decoding 2-dimensional endpoint trajectories of a neural prosthetic arm. Extensively testing these methods on random trajectories, we find that on average both the Kalman filter and ANNs successfully fuse the individual decoder estimates to produce more accurate predictions. ::: ::: ::: CONCLUSIONS ::: Our results reveal that a fusion-based approach has the potential to improve prediction accuracy over individual decoders of varying quality, and we hope that this work will encourage multimodal neural prosthetics experiments in the future. 
7. Abstract of Cited Paper (BIB007):  The development of brain-machine interface technology is a logical next step in the overall direction of neuro-prosthetics. Many of the required technological advances that will be required for clinical translation of brain-machine interfaces are already under development, including a new generation of recording electrodes, the decoding and interpretation of signals underlying intention and planning, actuators for implementation of mental plans in virtual or real contexts, direct somatosensory feedback to the nervous system to refine actions, and training to encourage plasticity in neural circuits. Although pre-clinical studies in nonhuman primates demonstrate high efficacy in a realistic motor task with motor cortical recordings, there are many challenges in the clinical translation of even simple tasks and devices. Foremost among these challenges is the development of biocompatible electrodes capable of long-term, stable recording of brain activity and implantable amplifiers and signal processors that are sufficiently resistant to noise and artifact to faithfully transmit recorded signals to the external environment. Whether there is a suitable market for such new technology depends on its efficacy in restoring and enhancing neural function, its risks of implantation, and its long-term efficacy and usefulness. Now is a critical time in brain-machine interface development because most ongoing studies are science-based and noncommercial, allowing new approaches to be included in commercial schemes under development.

Literature Review:
The electrical activities of the brain can be recorded through three different methods: (1) from the scalp: its corresponding signal is electroencephalogram (EEG); (2) the surface of the brain, which extracts an electrocorticography (ECoG) or intracranial electroencephalography (iEEG) signal; and (3) within the brain, which captures the extracellular activities of neurons. The extracted signals from these methods have a frequency range of a few mHz to 10 kHz and their amplitude is at the range of 20 µV to 10 mV [17] . In the first method, surface electrodes can be used to un-invasively measure the biopotentials of EEG on the scalp. In contrast, in the second method, the electrodes can be placed directly on the brain surface invasively to record electrical activity from the cerebral cortex. The brain signals provided with this method have a dramatically high signal-to-noise ratio (SNR) and are less sensitive to artifacts than EEG. Furthermore, these signals have high spatial and temporal resolution. Capturing the signal inside the body by utilizing implantable electrodes is the most effective approach for direct control of prosthetic devices BIB006 . In the third method, a sharp biocompatible microelectrode is utilized to perform bioelectrical recordings invasively inside the body. By penetrating microelectrodes into the brain, the bioelectrical activity that is transmitted along the axon of a neuron can either be measured intracellularly in a single neuron or extracellularly from the brain BIB001 . The extracellular action potentials (APs) generated by depolarization of the membrane of the neuron are at the range of 100 Hz to 10 kHz and their duration is a few milliseconds. The number of occurrences of the APs are between 10 to 120 times per second. The distance between the active neuron and the recording electrode determines the amplitude of the extracellular APs, which are between 50 µVpp to 500 µVpp BIB004 . In the following, the extraction procedure of the APs (spikes) and the local field potential (LFP) is explained. LFP is the mean field potential generated by neurons in the vicinity of the electrode. The neural activities are first amplified after sensing, then are low-pass filtered to obtain the LFP and are also high-pass filtered to identify the activity of single neurons by using spike detection and performing sorting algorithms BIB005 . The LFP includes lower-frequency neural waveforms in the range of mHz to 200 Hz with an amplitude of 500 µVpp to 5 mVpp. These potentials are used for brain interfacing applications and carry complementary important information BIB008 BIB007 . High-pass filtering extracts the spikes of the nearby neurons on top of background activity. Amplitude threshold methods are used to detect these spikes. In the next step, the features of the spikes are extracted and sorted accordingly. Note that the SNR of the neurons located at a distance of 50-100 µm from the electrode is large enough to easily distinguish the activity of each single unit BIB003 BIB002 . For neurons located between 100 µm to approximately 150 µm far from the electrode, spikes can still be detected but their shapes are masked by the noise. These signals are grouped in a 'multi-unit' cluster. However, neurons farther than 150 µm from the tip of the electrode cannot be detected and are added to the background noise. In addition to the extracellular method, the intracellular procedure is explained in [26] . In this method, a sharp glass micropipette penetrates into a neuron of a slice of a brain in the laboratory for neuroscience research. The neurons of the brain slice die after few hours of recording. A metal electrode located inside the micropipette and in contact with electrolyte is connected to an amplifier. The amplitude of intracellular APs is in the range of 10 to 70 mVpp. The neural recording parameters for different signal modalities are summarized in Table 1 .