Magnetic tunnel junction random number generators applied to dynamically tuned probability trees driven by spin orbit torque.

Perpendicular magnetic tunnel junction (pMTJ)-based true-random number generators (RNGs) can consume orders of magnitude less energy per bit than CMOS pseudo-RNGs. Here, we numerically investigate with a macrospin Landau-Lifshitz-Gilbert equation solver the use of pMTJs driven by spin-orbit torque to directly sample numbers from arbitrary probability distributions with the help of a tunable probability tree. The tree operates by dynamically biasing sequences of pMTJ relaxation events, called 'coinflips', via an additional applied spin-transfer-torque current.

Overcoming the noise in neural computing.

Circuit strategies can enable noisy analog hardware to achieve high precision.

The brain's unique take on algorithms.

Perspectives for understanding the brain vary across disciplines and this has challenged our ability to describe the brain’s functions. In this comment, we discuss how emerging theoretical computing frameworks that bridge top-down algorithm and bottom-up physics approaches may be ideally suited for guiding the development of neural computing technologies such as neuromorphic hardware and artificial intelligence. Furthermore, we discuss how this balanced perspective may be necessary to incorporate the neurobiological details that are critical for describing the neural computational disruptions within mental health and neurological disorders.

Probabilistic Neural Computing with Stochastic Devices.

The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event-driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications.

Intermittent fasting enhances long-term memory consolidation, adult hippocampal neurogenesis, and expression of longevity gene Klotho.

Daily calorie restriction (CR) and intermittent fasting (IF) enhance longevity and cognition but the effects and mechanisms that differentiate these two paradigms are unknown. We examined whether IF in the form of every-other-day feeding enhances cognition and adult hippocampal neurogenesis (AHN) when compared to a matched 10% daily CR intake and ad libitum conditions. After 3 months under IF, female C57BL6 mice exhibited improved long-term memory retention.

Motoneuron expression profiling identifies an association between an axonal splice variant of HDGF-related protein 3 and peripheral myelination.

Disorders that disrupt myelin formation during development or in adulthood, such as multiple sclerosis and peripheral neuropathies, lead to severe pathologies, illustrating myelin's crucial role in normal neural functioning. However, although our understanding of glial biology is increasing, the signals that emanate from axons and regulate myelination remain largely unknown. To identify the core components of the myelination process, here we adopted a microarray analysis approach combined with laser-capture microdissection of spinal motoneurons during the myelinogenic phase of development.

Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence.

Historically, neuroscience principles have heavily influenced artificial intelligence (AI), for example the influence of the perceptron model, essentially a simple model of a biological neuron, on artificial neural networks. More recently, notable recent AI advances, for example the growing popularity of reinforcement learning, often appear more aligned with cognitive neuroscience or psychology, focusing on function at a relatively abstract level. At the same time, neuroscience stands poised to enter a new era of large-scale high-resolution data and appears more focused on underlying neural mechanisms or architectures that can, at times, seem rather removed from functional descriptions.

Computing with Spikes: The Advantage of Fine-Grained Timing.

Neural-inspired spike-based computing machines often claim to achieve considerable advantages in terms of energy and time efficiency by using spikes for computation and communication. However, fundamental questions about spike-based computation remain unanswered. For instance, how much advantage do spike-based approaches have over conventional methods, and under what circumstances does spike-based computing provide a comparative advantage? Simply implementing existing algorithms using spikes as the medium of computation and communication is not guaranteed to yield an advantage.

High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination.

Opportunities offered by new neuro-technologies are threatened by lack of coherent plans to analyze, manage, and understand the data. High-performance computing will allow exploratory analysis of massive datasets stored in standardized formats, hosted in open repositories, and integrated with simulations.

A Combinatorial Model for Dentate Gyrus Sparse Coding.

The dentate gyrus forms a critical link between the entorhinal cortex and CA3 by providing a sparse version of the signal. Concurrent with this increase in sparsity, a widely accepted theory suggests the dentate gyrus performs pattern separation-similar inputs yield decorrelated outputs. Although an active region of study and theory, few logically rigorous arguments detail the dentate gyrus's (DG) coding.

Dopaminergic inputs in the dentate gyrus direct the choice of memory encoding.

Rewarding experiences are often well remembered, and such memory formation is known to be dependent on dopamine modulation of the neural substrates engaged in learning and memory; however, it is unknown how and where in the brain dopamine signals bias episodic memory toward preceding rather than subsequent events. Here we found that photostimulation of channelrhodopsin-2-expressing dopaminergic fibers in the dentate gyrus induced a long-term depression of cortical inputs, diminished theta oscillations, and impaired subsequent contextual learning. Computational modeling based on this dopamine modulation indicated an asymmetric association of events occurring before and after reward in memory tasks.

Low excitatory innervation balances high intrinsic excitability of immature dentate neurons.

Persistent neurogenesis in the dentate gyrus produces immature neurons with high intrinsic excitability and low levels of inhibition that are predicted to be more broadly responsive to afferent activity than mature neurons. Mounting evidence suggests that these immature neurons are necessary for generating distinct neural representations of similar contexts, but it is unclear how broadly responsive neurons help distinguish between similar patterns of afferent activity. Here we show that stimulation of the entorhinal cortex in mouse brain slices paradoxically generates spiking of mature neurons in the absence of immature neuron spiking.

Computational Modeling of Adult Neurogenesis.

The restriction of adult neurogenesis to only a handful of regions of the brain is suggestive of some shared requirement for this dramatic form of structural plasticity. However, a common driver across neurogenic regions has not yet been identified. Computational studies have been invaluable in providing insight into the functional role of new neurons; however, researchers have typically focused on specific scales ranging from abstract neural networks to specific neural systems, most commonly the dentate gyrus area of the hippocampus.

Energy Scaling Advantages of Resistive Memory Crossbar Based Computation and Its Application to Sparse Coding.

The exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency.

Regulation and function of adult neurogenesis: from genes to cognition.

Adult neurogenesis in the hippocampus is a notable process due not only to its uniqueness and potential impact on cognition but also to its localized vertical integration of different scales of neuroscience, ranging from molecular and cellular biology to behavior. This review summarizes the recent research regarding the process of adult neurogenesis from these different perspectives, with particular emphasis on the differentiation and development of new neurons, the regulation of the process by extrinsic and intrinsic factors, and their ultimate function in the hippocampus circuit. Arising from a local neural stem cell population, new neurons progress through several stages of maturation, ultimately integrating into the adult dentate gyrus network.

N2A: a computational tool for modeling from neurons to algorithms.

The exponential increase in available neural data has combined with the exponential growth in computing ("Moore's law") to create new opportunities to understand neural systems at large scale and high detail. The ability to produce large and sophisticated simulations has introduced unique challenges to neuroscientists. Computational models in neuroscience are increasingly broad efforts, often involving the collaboration of experts in different domains.

Perspectives for computational modeling of cell replacement for neurological disorders.

Mathematical modeling of anatomically-constrained neural networks has provided significant insights regarding the response of networks to neurological disorders or injury. A logical extension of these models is to incorporate treatment regimens to investigate network responses to intervention. The addition of nascent neurons from stem cell precursors into damaged or diseased tissue has been used as a successful therapeutic tool in recent decades.

A hypothesis for temporal coding of young and mature granule cells.

While it has been hypothesized that adult neurogenesis (NG) plays a role in the encoding of temporal information at long time-scales, the temporal relationship of immature cells to the highly rhythmic network activity of the hippocampus has been largely unexplored. Here, we present a theory for how the activity of immature adult-born granule cells relates to hippocampal oscillations. Our hypothesis is that theta rhythmic (5-10 Hz) excitatory and inhibitory inputs into the hippocampus could differentially affect young and mature granule cells due to differences in intrinsic physiology and synaptic inhibition between the two cell populations.

Molecular layer perforant path-associated cells contribute to feed-forward inhibition in the adult dentate gyrus.

New neurons, which have been implicated in pattern separation, are continually generated in the dentate gyrus in the adult hippocampus. Using a genetically modified rabies virus, we demonstrated that molecular layer perforant pathway (MOPP) cells innervated newborn granule neurons in adult mouse brain. Stimulating the perforant pathway resulted in the activation of MOPP cells before the activation of dentate granule neurons.

Development of GABAergic inputs controls the contribution of maturing neurons to the adult hippocampal network.

New neurons are continuously generated in the dentate gyrus (DG) in the adult hippocampus, and new granule cells (GCs) have been shown to be necessary for several aspects of learning and memory. Nonetheless, the limited information available regarding the anatomical and physiological development of synaptic inputs onto maturing neurons has restricted our understanding of how new GCs affect cognition. Here, we use photostimulation to demonstrate the time course by which anatomically isolated inhibitory inputs develop onto maturing GCs.

Resolving new memories: a critical look at the dentate gyrus, adult neurogenesis, and pattern separation.

Recently, investigation of new neurons in memory formation has focused on a specific function-pattern separation. However, it has been difficult to reconcile the form of separation tested in behavioral tasks with how it is conceptualized according to computational and electrophysiology perspectives. Here, we propose a memory resolution hypothesis that considers the unique information contributions of broadly tuned young neurons and highly specific mature neurons and describe how the fidelity of memories can relate to spatial and contextual discrimination.