https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

https://www.sas.upenn.edu/psych/kable_lab/Joes_Homepage/Publications_files/Bartra_McGuire_Kable_2013.pdf

The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value.

Guided by brain-like ‘spiking’ computational frameworks, neuromorphic computing—brain-inspired computing for machine intelligence—promises to realize artificial intelligence while reducing the energy requirements of computing platforms. This interdisciplinary field began with the implementation of silicon circuits for biological neural routines, but has evolved to encompass the hardware implementation of algorithms with spike-based encoding and event-driven representations. Here we provide an overview of the developments in neuromorphic computing for both algorithms and hardware and highlight the fundamentals of learning and hardware frameworks. We discuss the main challenges and the future prospects of neuromorphic computing, with emphasis on algorithm–hardware codesign. The authors review the advantages and future prospects of neuromorphic computing, a multidisciplinary engineering concept for energy-efficient artificial intelligence with brain-inspired functionality.

Towards spike-based machine intelligence with neuromorphic computing.

Neurogastroenterology is defined as neurology of the gastrointestinal tract, liver, gallbladder and pancreas and encompasses control of digestion through the enteric nervous system (ENS), the central nervous system (CNS) and integrative centers in sympathetic ganglia. This Review provides a broad overview of the field of neurogastroenterology, with a focus on the roles of the ENS in the control of the musculature of the gastrointestinal tract and transmucosal fluid movement. Digestion is controlled through the integration of multiple signals from the ENS and CNS; neural signals also pass between distinct gut regions to coordinate digestive activity. Moreover, neural and endocrine control of digestion is closely coordinated. Interestingly, the extent to which the ENS or CNS controls digestion differs considerably along the digestive tract. The importance of the ENS is emphasized by the life-threatening effects of certain ENS neuropathies, including Hirschsprung disease and Chagas disease. Other ENS disorders, such as esophageal achalasia and gastroparesis, cause varying degrees of dysfunction. The neurons in enteric reflex pathways use a wide range of chemical messengers that signal through an even wider range of receptors. These receptors provide many actual and potential targets for modifying digestive function.

The enteric nervous system and neurogastroenterology

In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological synaptic plasticity and learning are described. The material properties and electrical switching characteristics of a variety of synaptic devices are discussed, with a focus on the use of synaptic devices for neuromorphic or brain-inspired computing. Performance metrics desirable for large-scale implementations of synaptic devices are illustrated. A review of recent work on targeted computing applications with synaptic devices is presented.

https://iopscience.iop.org/article/10.1088/0957-4484/24/38/382001/pdf

Synaptic electronics: materials, devices and applications

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.

/pdf/neuromorphic-silicon-neuron-circuits-3fphnmpvq9.pdf

Neuromorphic Silicon Neuron Circuits

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Neuromorphic Silicon Neuron Circuits