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Computer Bus carry data, address, control signals to various components of the computer. Functions Of Data Bus, Address Bus, Control Bus.
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Recent development of Large language models (LLMs) empowers them to become more versatile and agnostic to specific tasks. Considering the impressive performance of LLM for capturing rich conceptual knowledge in their lexical embedding, an intriguing question arises: are frozen LLMs capable of solving multimodal tasks? The abovementioned question however is under-explored and doesn’t gain much
Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment. However, because of their probabilistic nature, they are computationally intensive. An innovative solution utilizes memristors’ inherent probabilistic nature to implement Bayesian neural networks. However, when using memristors, statistical effects follow the laws of device physics, whereas in Bayesian neural networks, those effects can take arbitrary shapes. This work overcome this difficulty by adopting a variational inference training augmented by a “technological loss”, incorporating memristor physics. This technique enabled programming a Bayesian neural network on 75 crossbar arrays of 1,024 memristors, incorporating CMOS periphery for in-memory computing. The experimental neural network classified heartbeats with high accuracy, and estimated the certainty of its predictions. The results reveal orders-of-magnitude improvement in inference energy efficiency compared to a microcontroller or an embedded graphics processing unit performing the same task. Bayesian networks gain importance in safety-critical applications. Authors conducted experiments with a memristor-based Bayesian network trained with variational inference with technological loss, achieving accurate heartbeats classification and prediction certainty.
Spiking neural networks (SNNs) capture the most important aspects of brain information processing. They are considered a promising approach for next-generation artificial intelligence. However, the biggest problem restricting ...
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VC dimension of Graph Neural Networks with Pfaffian activation functions
Transformers from Scratch
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Microsoft AI Researchers Introduce PPE: A Mathematically Guaranteed Reinforcement learning (RL) Algorithm For Exogenous Noise
Astrocytes are heterogeneous glial cells of the central nervous system1–3. However, the physiological relevance of astrocyte diversity for neural circuits and behaviour remains unclear. Here we show that a specific population of astrocytes in the central striatum expresses μ-crystallin (encoded by Crym in mice and CRYM in humans) that is associated with several human diseases, including neuropsychiatric disorders4–7. In adult mice, reducing the levels of μ-crystallin in striatal astrocytes through CRISPR–Cas9-mediated knockout of Crym resulted in perseverative behaviours, increased fast synaptic excitation in medium spiny neurons and dysfunctional excitatory–inhibitory synaptic balance. Increased perseveration stemmed from the loss of astrocyte-gated control of neurotransmitter release from presynaptic terminals of orbitofrontal cortex–striatum projections. We found that perseveration could be remedied using presynaptic inhibitory chemogenetics8, and that this treatment also corrected the synaptic deficits. Together, our findings reveal converging molecular, synaptic, circuit and behavioural mechanisms by which a molecularly defined and allocated population of striatal astrocytes gates perseveration phenotypes that accompany neuropsychiatric disorders9–12. Our data show that Crym-positive striatal astrocytes have key biological functions within the central nervous system, and uncover astrocyte–neuron interaction mechanisms that could be targeted in treatments for perseveration. In mice, a population of astrocytes in the central striatum, characterized by expression of μ-crystallin, has a role in perseveration phenotypes that are often associated with human neuropsychiatric disorders.
Artificial intelligence basics: Bayesian Deep Reinforcement Learning explained! Learn about types, benefits, and factors to consider when choosing an Bayesian Deep Reinforcement Learning.
