The response time of physical computational elements is finite, and neurons are no exception. In hierarchical models of cortical networks each layer thus introduces a response lag. This inherent property of physical dynamical systems results in delayed processing of stimuli and causes a timing mismatch between network output and instructive signals, thus afflicting not only inference, but also learning. We introduce Latent Equilibrium, a new framework for inference and learning in networks of slow components which avoids these issues by harnessing the ability of biological neurons to phase-advance their output with respect to their membrane potential. This principle enables quasi-instantaneous inference independent of network depth and avoids the need for phased plasticity or computationally expensive network relaxation phases. We jointly derive disentangled neuron and synapse dynamics from a prospective energy function that depends on a network's generalized position and momentum. The resulting model can be interpreted as a biologically plausible approximation of error backpropagation in deep cortical networks with continuous-time, leaky neuronal dynamics and continuously active, local plasticity. We demonstrate successful learning of standard benchmark datasets, achieving competitive performance using both fully-connected and convolutional architectures, and show how our principle can be applied to detailed models of cortical microcircuitry. Furthermore, we study the robustness of our model to spatio-temporal substrate imperfections to demonstrate its feasibility for physical realization, be it in vivo or in silico.

The response time of physical computational elements is finite, and neurons are no exception. In hierarchical models of cortical networks each layer thus introduces a response lag. This inherent property of physical dynamical systems results in delayed processing of stimuli and causes a timing mismatch between network output and instructive signals, thus afflicting not only inference, but also learning. We introduce Latent Equilibrium, a new framework for inference and learning in networks of slow components which avoids these issues by harnessing the ability of biological neurons to phase-advance their output with respect to their membrane potential. This principle enables quasi-instantaneous inference independent of network depth and avoids the need for phased plasticity or computationally expensive network relaxation phases.

The last decades have seen significant advancements in non-invasive neuroimaging technologies that have been increasingly adopted to examine human brain development. However, these improvements have not necessarily been followed by more sophisticated data analysis measures that are able to explain the mechanisms underlying functional brain development. For example, the shift from univariate (single area in the brain) to multivariate (multiple areas in brain) analysis paradigms is of significance as it allows investigations into the interactions between different brain regions. However, despite the potential of multivariate analysis to shed light on the interactions between developing brain regions, artificial intelligence (AI) techniques applied render the analysis non-explainable. The purpose of this paper is to understand the extent to which current state-of-the-art AI techniques can inform functional brain development. In addition, a review of which AI techniques are more likely to explain their learning based on the processes of brain development as defined by developmental cognitive neuroscience (DCN) frameworks is also undertaken. This work also proposes that eXplainable AI (XAI) may provide viable methods to investigate functional brain development as hypothesised by DCN frameworks.

Summary: Combining neuroimaging data with artificial intelligence technology, researchers have identified a complex network within the brain that comprehends the meaning of spoken sentences. Have you ever wondered why you are able to hear a sentence and understand its meaning – given that the same words in a different order would have an entirely different meaning? New research involving neuroimaging and A.I., describes the complex network within the brain that comprehends the meaning of a spoken sentence. "It has been unclear whether the integration of this meaning is represented in a particular site in the brain, such as the anterior temporal lobes, or reflects a more network level operation that engages multiple brain regions," said Andrew Anderson, Ph.D., research assistant professor in the University of Rochester Del Monte Institute for Neuroscience and lead author on of the study which was published in the Journal of Neuroscience. "The meaning of a sentence is more than the sum of its parts. Take a very simple example – 'the car ran over the cat' and'the cat ran over the car' – each sentence has exactly the same words, but those words have a totally different meaning when reordered."

It may seem like an unnecessary hurdle while you're online shopping, but Captcha tests are a key way that websites ensure online security. The tests, which ask you to identify words or pictures, are designed to tell the difference between man and machine. But a shocking new study has shown that computers can now successfully crack the test. This suggests that Captcha tests no longer offer the protection they used to. Captcha tests, which ask you to identify words or pictures, are designed to tell the difference between man and machine.

On the second floor of a small office building in the middle of Tel Aviv, the bustling heart of Israel's booming tech industry, sits the world headquarters for Cortica, an AI company with the ambitious goal of getting machines to see the world as well as we do. They are one of hundreds of AI startups that have sprouted up all over the world in the last few years. The global AI market has now exceeded a billion dollars and the tech giants are racing to acquire them as they are re-positioning themselves as AI companies first. But it's a convoluted space with a high knowledge barrier of entry. Most of these companies use buzz words like'machine learning', 'deep learning' or'neural networks' knowing that most consumers and investors have no idea what they really mean. For someone without a background in the field it can be hard to distinguish substance from snake oil.

The relation between the computer and the brain has always been of interest to scientists and the public alike. From the notion of'thinking machines' and'artificial intelligence' to applying concepts of neuroscience such as neural networks to solve problems in computer science. Also the earliest computers, using the von Neumann architecture still in use today, used memory and a central processing unit based on concepts of brain architecture (von Neumann, 1958). Also, models of artificial neural networks were inspired by the function of individual neurons as integrators of incoming signals. Detailed models of neural processing, however, are often limited to single tasks (e.g., pattern recognition) and one modality (e.g., only visual information). In addition, artificial neural networks starting with Perceptrons (Rosenblatt, 1959) are designed as a general purpose architecture whereas the architecture of natural neural systems shows a high specialization according to different tasks and functions.

We present a theory of cortico-hippocampal interaction in discrimination learning. The hippocampal region is presumed to form new stimulus representations which facilitate learning by enhancing the discriminability of predictive stimuli and compressing stimulus-stimulus redundancies. The cortical and cerebellar regions, which are the sites of long-term memory.

We present a theory of cortico-hippocampal interaction in discrimination learning. The hippocampal region is presumed to form new stimulus representations which facilitate learning by enhancing the discriminability of predictive stimuli and compressing stimulus-stimulus redundancies. The cortical and cerebellar regions, which are the sites of long-term memory.

We present a theory of cortico-hippocampal interaction in discrimination learning. The hippocampal region is presumed to form new stimulus representations which facilitate learning by enhancing the discriminability of predictive stimuli and compressing stimulus-stimulus redundancies. The cortical and cerebellar regions, which are the sites of long-term memory.