Properties Of Biological Neurons Research Articles

Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments.

A key challenge for AI is to build embodied systems that operate in dynamically changing environments. Such systems must adapt to changing task contexts and learn continuously. Although standard deep learning systems achieve state of the art results on static benchmarks, they often struggle in dynamic scenarios. In these settings, error signals from multiple contexts can interfere with one another, ultimately leading to a phenomenon known as catastrophic forgetting. In this article we investigate biologically inspired architectures as solutions to these problems. Specifically, we show that the biophysical properties of dendrites and local inhibitory systems enable networks to dynamically restrict and route information in a context-specific manner. Our key contributions are as follows: first, we propose a novel artificial neural network architecture that incorporates active dendrites and sparse representations into the standard deep learning framework. Next, we study the performance of this architecture on two separate benchmarks requiring task-based adaptation: Meta-World, a multi-task reinforcement learning environment where a robotic agent must learn to solve a variety of manipulation tasks simultaneously; and a continual learning benchmark in which the model's prediction task changes throughout training. Analysis on both benchmarks demonstrates the emergence of overlapping but distinct and sparse subnetworks, allowing the system to fluidly learn multiple tasks with minimal forgetting. Our neural implementation marks the first time a single architecture has achieved competitive results in both multi-task and continual learning settings. Our research sheds light on how biological properties of neurons can inform deep learning systems to address dynamic scenarios that are typically impossible for traditional ANNs to solve.

Based on Artificial Neural Network Simulation of Alloy Finite Element

Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. First modal analyses of microstructure defects are performed in ANSYS. Second the genetic algorithm is implemented in MATLAB to Calculate the Value of b and p. The last, The FEM analysis results are imported in ANSYS about the Stress distribution. The result presented in this paper is obtained using the Genetic Algorithm Optimization Toolbox.

ARTIFICAL NEURAL NETWORKS: The New Era Of I.T

Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex: artificial neural network algorithms attempt to abstract this complexity and focus on what may hypothetically matter most from an information processing point of view. Good performance (e.g. as measured by good predictive ability, low generalization error), or performance mimicking animal or human error patterns, can then be used as one source of evidence towards supporting the hypothesis that the abstraction really captured something important from the point of view of information processing in the brain. Another incentive for these abstractions is to reduce the amount of computation required to simulate artificial neural networks, so as to allow one to experiment with larger networks and train them on larger data sets. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well

Time-of-Day-Dependent Enhancement of Adult Neurogenesis in the Hippocampus

BackgroundAdult neurogenesis occurs in specific regions of the mammalian brain such as the dentate gyrus of the hippocampus. In the neurogenic region, neural progenitor cells continuously divide and give birth to new neurons. Although biological properties of neurons and glia in the hippocampus have been demonstrated to fluctuate depending on specific times of the day, it is unclear if neural progenitors and neurogenesis in the adult brain are temporally controlled within the day.Methodology/Principal FindingsHere we demonstrate that in the dentate gyrus of the adult mouse hippocampus, the number of M-phase cells shows a day/night variation throughout the day, with a significant increase during the nighttime. The M-phase cell number is constant throughout the day in the subventricular zone of the forebrain, another site of adult neurogenesis, indicating the daily rhythm of progenitor mitosis is region-specific. Importantly, the nighttime enhancement of hippocampal progenitor mitosis is accompanied by a nighttime increase of newborn neurons.Conclusions/SignificanceThese results indicate that neurogenesis in the adult hippocampus occurs in a time-of-day-dependent fashion, which may dictate daily modifications of dentate gyrus physiology.

Iterative principles of recognition in probabilistic neural networks

When considering the probabilistic approach to neural networks in the framework of statistical pattern recognition we assume approximation of class-conditional probability distributions by finite mixtures of product components. The mixture components can be interpreted as probabilistic neurons in neurophysiological terms and, in this respect, the fixed probabilistic description contradicts the well known short-term dynamic properties of biological neurons. By introducing iterative schemes of recognition we show that some parameters of probabilistic neural networks can be "released" for the sake of dynamic processes without disturbing the statistically correct decision making. In particular, we can iteratively adapt the mixture component weights or modify the input pattern in order to facilitate correct recognition. Both procedures are shown to converge monotonically as a special case of the well known EM algorithm for estimating mixtures.

Pulse-type hardware chaotic neuron model and its bifurcation phenomena

A number of studies has recently been made on hardware for a biological neuron for application to information processing functions of neural networks. A model of a single neuron with chaotic dynamics (called “the chaotic neuron model” proposed by Aihara) has the following properties of biological neurons: graded responses, relative refractoriness, and spatio-temporal summation of inputs. The model exhibits not only periodic responses but also chaotic responses for periodic stimulation. It is suggested that, with the chaotic neuron model, new information processing structures can be realized in neural networks. Accordingly, a single-neuron model should be designed so as to have the properties of biological neurons. But a pulse-type hardware chaotic neuron model has not been found. We previously proposed a pulse-type hardware neuron model composed of a negative resistance circuit, resistors, and capacitors. We reported also that the pulse-type hardware neuron model exhibited continuous firing for a constant stimulus current. But chaotic features of the neuron model have not been investigated. In this paper, we show, firstly, that the pulse-type hardware neuron model has the features of the chaotic neuron model, and we show that it is useful for the pulse-type hardware chaotic neuron model. Next, we show that the pulse-type hardware chaotic neuron model has three chaotic regions in the bifurcation diagram to periodic pulse train stimulation, and we clarify the bifurcation route and the return map in each chaotic region.

Untying the Gordian knot: contemporary studies of neuronal organization.

The nervous systems of invertebrates and vertebrates consist of neuronal networks of varying complexity, and the elucidation of the organization of these networks is essential if we are to understand neural function. Up until the mid-19th Century gross dissection was the primary tool available to scientists to study the nervous system. The development of neurohistological techniques, electrical stimulation, and observation of neural function in humans and animals following injury added rapidly to our understanding of the nervous system during the following century. Over the last 3 decades investigators seeking to unravel the complexities of neural circuits have made use of analytical methods based upon the biological properties of neurons, including orthograde and retrograde axonal transport of tracer substances, the expression of particular genes and gene products that can be assessed with immunocytochemical or in situ methods, and the imaging of the utilization of oxygen or glucose by active populations of neurons. Advances in neuroscience have led to an enormous expansion in our knowledge of normal neural functioning and how that function is altered by injury or disease. Modern studies of neuronal organization have been at the center of our increased understanding of how the brain works.

Pulse-type hardware chaotic neuron model and its bifurcation phenomena

Abstract A number of studies has recently been made on hardware for a biological neuron for application to information processing functions of neural networks. A model of a single neuron with chaotic dynamics (called “the chaotic neuron model” proposed by Aihara) has the following properties of biological neurons: graded responses, relative refractoriness, and spatio-temporal summation of inputs. The model exhibits not only periodic responses but also chaotic responses for periodic stimulation. It is suggested that, with the chaotic neuron model, new information processing structures can be realized in neural networks. Accordingly, a single-neuron model should be designed so as to have the properties of biological neurons. But a pulse-type hardware chaotic neuron model has not been found. We previously proposed a pulse-type hardware neuron model composed of a negative resistance circuit, resistors, and capacitors. We reported also that the pulse-type hardware neuron model exhibited continuous firing for a constant stimulus current. But chaotic features of the neuron model have not been investigated. In this paper, we show, firstly, that the pulse-type hardware neuron model has the features of the chaotic neuron model, and we show that it is useful for the pulse-type hardware chaotic neuron model. Next, we show that the pulse-type hardware chaotic neuron model has three chaotic regions in the bifurcation diagram to periodic pulse train stimulation, and we clarify the bifurcation route and the return map in each chaotic region.

A COMPUTATIONAL HYPOTHESIS OF PROBABILITY INFERENCE IN NEURAL NETWORKS AND SOME RELATIONS TO PSYCHOLOGICAL MODELS

In an attempt to understand the nature of information processing at the neuronal level and its relation to normal cognition at the behavioral level, this paper presents a neurally plausible computational theory of probability inference inspired by certain biological properties of neurons. The probability inference that the neural networks of this theory solve is the production of either probabilities or likelihoods associated with one set of events based on another set of events. The theory describes how a single neuron might create a probability in an analog fashion. It describes how certain optimal computational principles required for probability inference (e.g., Bayes rule) might be implemented at the neuronal level despite apparent limitations of the computational capacity of neurons. In fact, it is the fundamental properties of neurons that lead to the particular neuronal computation. Finally, this report attempts to connect this neural network theory to existing psychological models that assume Bayes inference schemes.

Chaotic neural networks

A model of a single neuron with chaotic dynamics is proposed by considering the following properties of biological neurons: (1) graded responses, (2) relative refractoriness and (3) spatio-temporal summation of inputs. The model includes some conventional models of a neuron as its special cases; namely, chaotic dynamics is introduced as a natural extension of the former models. Chaotic solutions of both the single chaotic neuron and the chaotic neural network composed of such neurons are numerically demonstrated.