Human metabolic adaptations and prolonged expensive neurodevelopment: A review

1.	After weaning, human hunter-gatherer juveniles receive substantial (≈3.5-7 MJ day^-1^), extended (≈15 years) and reliable (kin and nonkin food pooling) energy provision.
2.	The childhood (pediatric) and the adult human brain takes a very high share of both basal metabolic rate (BMR) (child: 50-70%; adult: ≈20%) and total energy expenditure (TEE) (child: 30-50%; adult: ≈10%).
3.	The pediatric brain for an extended period (≈4-9 years-of-age) consumes roughly 50% more energy than the adult one, and after this, continues during adolescence, at a high but declining rate. Within the brain, childhood cerebral gray matter has an even higher 1.9 to 2.2-fold increased energy consumption. 
4.	This metabolic expensiveness is due to (i) the high cost of synapse activation (74% of brain energy expenditure in humans), combined with (ii), a prolonged period of exuberance in synapse numbers (up to double the number present in adults). Cognitive development during this period associates with volumetric changes in gray matter (expansion and contraction due to metabolic related size alterations in glial cells and capillary vascularization), and in white matter (expansion due to myelination). 
5.	Amongst mammals, anatomically modern humans show an unique pattern in which very slow musculoskeletal body growth is followed by a marked adolescent size/stature spurt. This pattern of growth contrasts with nonhuman primates that have a sustained fast juvenile growth with only a minor period of puberty acceleration. The existence of slow childhood growth in humans has been shown to date back to 160,000 BP. 
6.	Human children physiologically have a limited capacity to protect the brain from plasma glucose fluctuations and other metabolic disruptions. These can arise in adults, during prolonged strenuous exercise when skeletal muscle depletes plasma glucose, and produces other metabolic disruptions upon the brain (hypoxia, hyperthermia, dehydration and hyperammonemia). These are proportional to muscle mass.
7.	Children show specific adaptations to minimize such metabolic disturbances. (i) Due to slow body growth and resulting small body size, they have limited skeletal muscle mass. (ii) They show other adaptations such as an exercise specific preference for free fatty acid metabolism. (iii) While children are generally more active than adolescents and adults, they avoid physically prolonged intense exertion. 
8.	Childhood has a close relationship to high levels of energy provision and metabolic adaptations that support prolonged synaptic neurodevelopment. 
&#xa

Human metabolic adaptations and prolonged expensive neurodevelopment: A review

1.	After weaning, human hunter-gatherer juveniles receive substantial (≈3.5-7 MJ day^-1^), extended (≈15 years) and reliable (kin and nonkin food pooling) energy provision.
2.	The childhood (pediatric) and the adult human brain takes a very high share of both basal metabolic rate (BMR) (child: 50-70%; adult: ≈20%) and total energy expenditure (TEE) (child: 30-50%; adult: ≈10%).
3.	The pediatric brain for an extended period (≈4-9 years-of-age) consumes roughly 50% more energy than the adult one, and after this, continues during adolescence, at a high but declining rate. Within the brain, childhood cerebral gray matter has an even higher 1.9 to 2.2-fold increased energy consumption. 
4.	This metabolic expensiveness is due to (i) the high cost of synapse activation (74% of brain energy expenditure in humans), combined with (ii), a prolonged period of exuberance in synapse numbers (up to double the number present in adults). Cognitive development during this period associates with volumetric changes in gray matter (expansion and contraction due to metabolic related size alterations in glial cells and capillary vascularization), and in white matter (expansion due to myelination). 
5.	Amongst mammals, anatomically modern humans show an unique pattern in which very slow musculoskeletal body growth is followed by a marked adolescent size/stature spurt. This pattern of growth contrasts with nonhuman primates that have a sustained fast juvenile growth with only a minor period of puberty acceleration. The existence of slow childhood growth in humans has been shown to date back to 160,000 BP. 
6.	Human children physiologically have a limited capacity to protect the brain from plasma glucose fluctuations and other metabolic disruptions. These can arise in adults, during prolonged strenuous exercise when skeletal muscle depletes plasma glucose, and produces other metabolic disruptions upon the brain (hypoxia, hyperthermia, dehydration and hyperammonemia). These are proportional to muscle mass.
7.	Children show specific adaptations to minimize such metabolic disturbances. (i) Due to slow body growth and resulting small body size, they have limited skeletal muscle mass. (ii) They show other adaptations such as an exercise specific preference for free fatty acid metabolism. (iii) While children are generally more active than adolescents and adults, they avoid physically prolonged intense exertion. 
8.	Childhood has a close relationship to high levels of energy provision and metabolic adaptations that support prolonged synaptic neurodevelopment. 
&#xa

Analog Spiking Neuromorphic Circuits and Systems for Brain- and Nanotechnology-Inspired Cognitive Computing

Human society is now facing grand challenges to satisfy the growing demand for computing power, at the same time, sustain energy consumption. By the end of CMOS technology scaling, innovations are required to tackle the challenges in a radically different way. Inspired by the emerging understanding of the computing occurring in a brain and nanotechnology-enabled biological plausible synaptic plasticity, neuromorphic computing architectures are being investigated. Such a neuromorphic chip that combines CMOS analog spiking neurons and nanoscale resistive random-access memory (RRAM) using as electronics synapses can provide massive neural network parallelism, high density and online learning capability, and hence, paves the path towards a promising solution to future energy-efficient real-time computing systems. However, existing silicon neuron approaches are designed to faithfully reproduce biological neuron dynamics, and hence they are incompatible with the RRAM synapses, or require extensive peripheral circuitry to modulate a synapse, and are thus deficient in learning capability. As a result, they eliminate most of the density advantages gained by the adoption of nanoscale devices, and fail to realize a functional computing system. This dissertation describes novel hardware architectures and neuron circuit designs that synergistically assemble the fundamental and significant elements for brain-inspired computing. Versatile CMOS spiking neurons that combine integrate-and-fire, passive dense RRAM synapses drive capability, dynamic biasing for adaptive power consumption, in situ spike-timing dependent plasticity (STDP) and competitive learning in compact integrated circuit modules are presented. Real-world pattern learning and recognition tasks using the proposed architecture were demonstrated with circuit-level simulations. A test chip was implemented and fabricated to verify the proposed CMOS neuron and hardware architecture, and the subsequent chip measurement results successfully proved the idea. The work described in this dissertation realizes a key building block for large-scale integration of spiking neural network hardware, and then, serves as a step-stone for the building of next-generation energy-efficient brain-inspired cognitive computing systems

AI of Brain and Cognitive Sciences: From the Perspective of First Principles

Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation. Despite these powerful applications, there are still many tasks in our daily life that are rather simple to humans but pose great challenges to AI. These include image and language understanding, few-shot learning, abstract concepts, and low-energy cost computing. Thus, learning from the brain is still a promising way that can shed light on the development of next-generation AI. The brain is arguably the only known intelligent machine in the universe, which is the product of evolution for animals surviving in the natural environment. At the behavior level, psychology and cognitive sciences have demonstrated that human and animal brains can execute very intelligent high-level cognitive functions. At the structure level, cognitive and computational neurosciences have unveiled that the brain has extremely complicated but elegant network forms to support its functions. Over years, people are gathering knowledge about the structure and functions of the brain, and this process is accelerating recently along with the initiation of giant brain projects worldwide. Here, we argue that the general principles of brain functions are the most valuable things to inspire the development of AI. These general principles are the standard rules of the brain extracting, representing, manipulating, and retrieving information, and here we call them the first principles of the brain. This paper collects six such first principles. They are attractor network, criticality, random network, sparse coding, relational memory, and perceptual learning. On each topic, we review its biological background, fundamental property, potential application to AI, and future development.Comment: 59 pages, 5 figures, review articl

Semiconductor Memory Devices for Hardware-Driven Neuromorphic Systems

This book aims to convey the most recent progress in hardware-driven neuromorphic systems based on semiconductor memory technologies. Machine learning systems and various types of artificial neural networks to realize the learning process have mainly focused on software technologies. Tremendous advances have been made, particularly in the area of data inference and recognition, in which humans have great superiority compared to conventional computers. In order to more effectively mimic our way of thinking in a further hardware sense, more synapse-like components in terms of integration density, completeness in realizing biological synaptic behaviors, and most importantly, energy-efficient operation capability, should be prepared. For higher resemblance with the biological nervous system, future developments ought to take power consumption into account and foster revolutions at the device level, which can be realized by memory technologies. This book consists of seven articles in which most recent research findings on neuromorphic systems are reported in the highlights of various memory devices and architectures. Synaptic devices and their behaviors, many-core neuromorphic platforms in close relation with memory, novel materials enabling the low-power synaptic operations based on memory devices are studied, along with evaluations and applications. Some of them can be practically realized due to high Si processing and structure compatibility with contemporary semiconductor memory technologies in production, which provides perspectives of neuromorphic chips for mass production

Interaction dynamics and autonomy in cognitive systems

The concept of autonomy is of crucial importance for understanding life and cognition. Whereas cellular and organismic autonomy is based in the self-production of the material infrastructure sustaining the existence of living beings as such, we are interested in how biological autonomy can be expanded into forms of autonomous agency, where autonomy as a form of organization is extended into the behaviour of an agent in interaction with its environment (and not its material self-production). In this thesis, we focus on the development of operational models of sensorimotor agency, exploring the construction of a domain of interactions creating a dynamical interface between agent and environment. We present two main contributions to the study of autonomous agency: First, we contribute to the development of a modelling route for testing, comparing and validating hypotheses about neurocognitive autonomy. Through the design and analysis of specific neurodynamical models embedded in robotic agents, we explore how an agent is constituted in a sensorimotor space as an autonomous entity able to adaptively sustain its own organization. Using two simulation models and different dynamical analysis and measurement of complex patterns in their behaviour, we are able to tackle some theoretical obstacles preventing the understanding of sensorimotor autonomy, and to generate new predictions about the nature of autonomous agency in the neurocognitive domain. Second, we explore the extension of sensorimotor forms of autonomy into the social realm. We analyse two cases from an experimental perspective: the constitution of a collective subject in a sensorimotor social interactive task, and the emergence of an autonomous social identity in a large-scale technologically-mediated social system. Through the analysis of coordination mechanisms and emergent complex patterns, we are able to gather experimental evidence indicating that in some cases social autonomy might emerge based on mechanisms of coordinated sensorimotor activity and interaction, constituting forms of collective autonomous agency

Long-term memory encoding of facial information in humans: an EEG and tACS study

In recent years, the investigation of memory formation and retrieval has attracted increasing interest. As oscillatory activity plays a crucial role in neuroplastic processes, episodic memory is to a considerable extent attributable to synaptic changes, synchronization, and neurophysiological alterations through oscillating electric fields. Perception processes are part of episodic memory encoding. Human face perception and encoding arouse particular interest due to their fundamental relevance in social behavior. This study aimed to determine the causal role of brain dynamics in the encoding of facial episodic memory in humans. As recent studies revealed an enhancement in cognitive processes by the entrainment of internal brain oscillations, tACS stepped up as a new method of non-invasive brain stimulation to induce neuroplasticity (Antal et al. 2008; Antal and Herrmann 2016). It is a promising tool to test the role of brain oscillations on episodic memory encoding in humans and the potential for memory improvement. For the entire study, we developed a memory task that includes encoding, a Short-Term Memory Retrieval Part, maintenance, and a Long-Term Memory Retrieval Part. In the longterm face recognition, we assessed both the performance and the choice confidence on the 3-point scale. Two consecutive experiments were performed. For the first experiment (20 participants), we used 128-channel EEG to identify the region of the brain that is exclusively responsible for the long-term face encoding and the frequency of the brain rhythm that is associated with the successful subsequent recognition. Then, we conducted the tACS experiment (25 participants) based on the frequency and spatial data from the EEG experiment. We implemented a double-blinded, randomized, counterbalanced, crossover, and placebo-controlled study design. 20 minutes of 4 Hz-tACS at 3 mA peak-to-peak were applied during the encoding task to the identified right or to the left TPO area for active control. One more session included sham stimulation to one or the other area. The EEG study revealed a significant synchronization of brain oscillations during successful long-term facial memory encoding in the right TPO area at the low theta range (4 Hz). In complete agreement, the placebo-controlled tACS study showed a significant enhancement of long-term memory recognition performance after the low theta-stimulation of the right but not the left TPO area. The results indicate that low theta oscillations in the right TPO area are vital for successful episodic long-term memory encoding of facial stimuli. Secondly, we confirm that active low theta-tACS applied on this area during encoding improves the subsequent memory recognition performance. This supports the concept of lateralization for face processing in the right posterior brain region; moreover it puts forward this area as a crucial neocortical node in communication with the hippocampus for the long-term memory encoding (Pitcher et al. 2011; Geib et al. 2017). The results are in agreement with other studies that revealed a positive correlation between theta power and memory performance (Pahor and Jaušovec 2014; Clouter et al. 2017). However, the present work reveals a causal link between the empowered low theta in the right TPO area and enhanced subsequent long-term memory recognition. In summary, tACS is a highly suitable non-invasive tool to entrain local neocortical low theta activity and enhance long-term memory encoding, which is important in the clinical context for revealing novel therapeutic strategies in prosopagnosia and prosopamnesia.2021-09-1