The chapter introduces key codesign principles across multiple layers of the design stack highlighting the need for cross-layer optimizations. Mitigation of various non-idealities stemming from emerging devices such as device-to-device variations, cycle-to-cycle variations, conductance drift, and stuck-at-faults through algorithm–hardware codesign are discussed. Further, inspiration from the brain’s self-repair mechanism is utilized to design neuromorphic systems capable of autonomous self-repair. Finally, an end-to-end codesign approach is outlined by exploring synergies of event-driven hardware and algorithms with event-driven sensors, thereby leveraging maximal benefits of brain-inspired computing.

This chapter provides a selection of problems relevant to the field of neuromorphic computing that intersects materials science, electrical engineering, computer science, neural networks, and device design for realizing AI in hardware and algorithms. The emphasis on interdisciplinary nature of neuromorphic computing is apparent.

This chapter starts with a discussion on models informing probability versus the case where probability is inherent in the model. The chapter also goes into detail to argue why a particular interpretation of quantum mechanics, Bohmian economics, can be useful in finance. We provide for an example of how such mechanics can be applied to daily returns on commodity prices. We also briefly look into the potential connection between Bohmian mechanics and a macroscopic fluid system.

The chapter discusses concepts in plasticity that go beyond memory. Several examples are discussed starting with the complexity of dendritic structure in biological neurons, nonlinear summation of signals from synapses by neurons and the vast range of plasticity that has been discovered in biological brain circuits. Learning and memory are commonly assigned to synapses; however, non-synaptic changes are important to consider for neuromorphic hardware and algorithms. The distinction between bioinspired and bio-realistic designs of hardware for AI is discussed. While synaptic connections can undergo both functional and structural plasticity, emulating such concepts in neuromorphic computing will require adaptive algorithms and semiconductors that can be dynamically reprogrammed. The necessity for close collaboration between neuroscience and neuromorphic engineering community is highlighted. Methods to implement lifelong learning in algorithms and hardware are discussed. Gaps in the field and directions for future research and development are discussed. The prospects for energy-efficient neuromorphic computing with disruptive brain-inspired algorithms and emerging semiconductors are discussed.

The chapter begins with physics and mathematical description of the nonlinear dynamics seen in biological neurons and their adaptation into neuromorphic hardware. Various abstractions of the Hodgkin–Huxley model of squid neuron have been studied in neuromorphic computing. Filamentary threshold switches that can act as neurons are discussed. The combination of ionic and electronic relaxation pathways offers unique abilities to design low-power artificial neurons. Ferroelectric, insulator–metal transition, 2D materials, and organic semiconductor-based neurons are discussed wherein modulation of long-range transport and/or bound charge displacement are utilized for neuron function. Besides electron transport, light and spin state can also be effectively utilized to create photonic and spintronic neurons respectively. The chapter should provide the reader a comprehensive insight into design of artificial neurons that can generate action potentials, spanning various classes of inorganic and organic semiconductors, and different stimuli for input and readout of signals such as voltage, light, spin current, and ionic currents.

The chapter begins with a discussion on standard mechanisms for training spiking neural networks ranging from – (a) unsupervised spike-timing-dependent plasticity, (b) backpropagation through time (BPTT) using surrogate gradient techniques, and (c) conversion techniques from conventional analog non-spiking networks. Subsequently, various local learning algorithms with different degrees of locality are discussed that have the potential to replace computationally expensive global learning algorithms such as BPTT. The chapter concludes with pointers to several emerging research directions in the neuromorphic algorithms domain ranging from stochastic computing, lifelong learning, and dynamical system-based approaches, among others. Finally, we also underscore the need for looking at hybrid neuromorphic algorithm design combining principles of conventional deep learning along with forging stronger connections with computational neuroscience.

The chapter begins by answering the question “how did physics, which originally represented the philosophy of nature, evolve into its modern phase of the philosophical as well as the scientific knowledge of nature” in terms of a brief history of physics. This is presented in the form of four chronological phases – ancient times, the scientific revolution, the birth of modern physics and the modern version of the quest for the nature of reality. This is followed by the authors’ interpretation of the generic structure of the physical theories of motion and by the application of the interpretation to the problem of “the motion of a particle under gravity”. We then introduce the reader to three key features which characterize financial and economic systems: markets, decision making, and the economic actor. The chapter goes into some detail on each of these three ingredients. The chapter ends by providing the abstracts of the remaining chapters.

The chapter introduces fundamental principles of deep learning. We discuss supervised learning of feedforward neural networks by considering a binary classification problem. Gradient descent techniques and backpropagation learning algorithms are introduced as means of training neural networks. The impact of neuron activations and convolutional and residual network architectures on the learning performance are discussed. Finally, regularization techniques such as batch normalization and dropout are introduced for improving the accuracy of trained models. The chapter is essential to connect advances in conventional deep learning algorithms to neuromorphic concepts.