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Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains.
Blindsight is the ability of people who are cortically blind due to lesions in their striate cortex, also known as the primary visual cortex or V1, to respond to visual stimuli that they do not consciously see. The term was coined by Lawrence Weiskrantz and his colleagues in a paper published in Brain in 1974. A similar paper in which the discriminatory capacity of a cortically blind patient had been studied was published in Nature in 1973.
Connectionism refers to both an approach in the field of cognitive science that hopes to explain mental phenomena using artificial neural networks (ANN) and to a wide range of techniques and algorithms using ANNs in the context of artificial intelligence to build more intelligent machines. Connectionism presents a cognitive theory based on simultaneously occurring, distributed signal activity via connections that can be represented numerically, where learning occurs by modifying connection strengths based on experience.
The visual system comprises the sensory organ and parts of the central nervous system which gives organisms the sense of sight as well as enabling the formation of several non-image photo response functions. It detects and interprets information from the optical spectrum perceptible to that species to "build a representation" of the surrounding environment. The visual system carries out a number of complex tasks, including the reception of light and the formation of monocular neural representations, colour vision, the neural mechanisms underlying stereopsis and assessment of distances to and between objects, the identification of a particular object of interest, motion perception, the analysis and integration of visual information, pattern recognition, accurate motor coordination under visual guidance, and more. The neuropsychological side of visual information processing is known as visual perception, an abnormality of which is called visual impairment, and a complete absence of which is called blindness. Non-image forming visual functions, independent of visual perception, include the pupillary light reflex and circadian photoentrainment.
In neuroanatomy, the lateral geniculate nucleus is a structure in the thalamus and a key component of the mammalian visual pathway. It is a small, ovoid, ventral projection of the thalamus where the thalamus connects with the optic nerve. There are two LGNs, one on the left and another on the right side of the thalamus. In humans, both LGNs have six layers of neurons alternating with optic fibers.
The receptive field, or sensory space, is a delimited medium where some physiological stimuli can evoke a sensory neuronal response in specific organisms.
A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes can create a cycle, allowing output from some nodes to affect subsequent input to the same nodes. This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition. Recurrent neural networks are theoretically Turing complete and can run arbitrary programs to process arbitrary sequences of inputs.
A feedforward neural network (FNN) is an artificial neural network wherein connections between the nodes do not form a cycle. As such, it is different from its descendant: recurrent neural networks.
A neural circuit is a population of neurons interconnected by synapses to carry out a specific function when activated. Neural circuits interconnect to one another to form large scale brain networks. Biological neural networks have inspired the design of artificial neural networks, but artificial neural networks are usually not strict copies of their biological counterparts.
A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological neurons, or an artificial neural network, used for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled in artificial neural networks as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1.
Neurophilosophy or philosophy of neuroscience is the interdisciplinary study of neuroscience and philosophy that explores the relevance of neuroscientific studies to the arguments traditionally categorized as philosophy of mind. The philosophy of neuroscience attempts to clarify neuroscientific methods and results using the conceptual rigor and methods of philosophy of science.
Neuroinformatics is the field that combines informatics and neuroscience. Neuroinformatics is related with neuroscience data and information processing by artificial neural networks. There are three main directions where neuroinformatics has to be applied:
Computational neurogenetic modeling (CNGM) is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This area brings together knowledge from various scientific disciplines, such as computer and information science, neuroscience and cognitive science, genetics and molecular biology, as well as engineering.
An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer. The connectivity and weights of hidden neurons are fixed and randomly assigned. The weights of output neurons can be learned so that the network can produce or reproduce specific temporal patterns. The main interest of this network is that although its behaviour is non-linear, the only weights that are modified during training are for the synapses that connect the hidden neurons to output neurons. Thus, the error function is quadratic with respect to the parameter vector and can be differentiated easily to a linear system.
There are many types of artificial neural networks (ANN).
Network of human nervous system comprises nodes that are connected by links. The connectivity may be viewed anatomically, functionally, or electrophysiologically. These are presented in several Wikipedia articles that include Connectionism, Biological neural network, Artificial neural network, Computational neuroscience, as well as in several books by Ascoli, G. A. (2002), Sterratt, D., Graham, B., Gillies, A., & Willshaw, D. (2011), Gerstner, W., & Kistler, W. (2002), and Rumelhart, J. L., McClelland, J. L., and PDP Research Group (1986) among others. The focus of this article is a comprehensive view of modeling a neural network. Once an approach based on the perspective and connectivity is chosen, the models are developed at microscopic, mesoscopic, or macroscopic (system) levels. Computational modeling refers to models that are developed using computing tools.
Orientation columns are organized regions of neurons that are excited by visual line stimuli of varying angles. These columns are located in the primary visual cortex (V1) and span multiple cortical layers. The geometry of the orientation columns are arranged in slabs that are perpendicular to the surface of the primary visual cortex.
Catastrophic interference, also known as catastrophic forgetting, is the tendency of an artificial neural network to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. With these networks, human capabilities such as memory and learning can be modeled using computer simulations. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ratcliff (1990). It is a radical manifestation of the 'sensitivity-stability' dilemma or the 'stability-plasticity' dilemma. Specifically, these problems refer to the challenge of making an artificial neural network that is sensitive to, but not disrupted by, new information. Lookup tables and connectionist networks lie on the opposite sides of the stability plasticity spectrum. The former remains completely stable in the presence of new information but lacks the ability to generalize, i.e. infer general principles, from new inputs. On the other hand, connectionist networks like the standard backpropagation network can generalize to unseen inputs, but they are very sensitive to new information. Backpropagation models can be considered good models of human memory insofar as they mirror the human ability to generalize but these networks often exhibit less stability than human memory. Notably, these backpropagation networks are susceptible to catastrophic interference. This is an issue when modelling human memory, because unlike these networks, humans typically do not show catastrophic forgetting.
In deep learning, a convolutional neural network is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input. They have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series.
Laura Busse is a German neuroscientist and professor of Systemic Neuroscience within the Division of Neurobiology at the Ludwig Maximilian University of Munich. Busse's lab studies context-dependent visual processing in mouse models by performing large scale in vivo electrophysiological recordings in the thalamic and cortical circuits of awake and behaving mice.
References
- Azam, Farooq (2000). "Biologically Inspired Modular Neural Networks. PhD Dissertation". Virginia Tech. hdl:10919/27998.
- Happel, Bart; Murre, Jacob (1994). "The Design and Evolution of Modular Neural Network Architectures" (PDF). Neural Networks. 7 (6–7): 985–1004. doi:10.1016/s0893-6080(05)80155-8.[ permanent dead link ]
- Hubel, DH; Livingstone, MS (1990). "Color and contrast sensitivity in the lateral geniculate body and primary visual cortex of the macaque monkey". Journal of Neuroscience. 10 (7): 2223–2237. doi: 10.1523/JNEUROSCI.10-07-02223.1990 . PMC 6570379 . PMID 2198331.
- Tahmasebi, P.; Hezarkhani, A. (2011). "Application of a Modular Feedforward for Grade Estimation". Natural Resources Research. 20 (1): 25–32. doi:10.1007/s11053-011-9135-3. S2CID 45997840.
- Clune, Jeff; Mouret, Jean-Baptiste; Lipson, Hod (2013-01-30). "The evolutionary origins of modularity". Proceedings of the Royal Society B: Biological Sciences. 280 (1755): 20122863. arXiv: 1207.2743 . doi:10.1098/rspb.2012.2863. ISSN 0962-8452. PMC 3574393 . PMID 23363632.
- Tahmasebi, Pejman; Hezarkhani, Ardeshir (2012). "A fast and independent architecture of artificial neural network for permeability prediction". Journal of Petroleum Science and Engineering. 86: 118–126. doi:10.1016/j.petrol.2012.03.019.