Biological neural network 1726672 212631257 2008-05-15T16:25:21Z Romanski 712492 /* Connections between neurons */ afferent more specific [[Image:Cajal actx inter.jpg|thumb|300px|right|From "Texture of the [[Nervous System]] of Man and the [[Vertebrates]]" by [[Santiago Ramón y Cajal]]. The figure illustrates the diversity of neuronal morphologies in the [[auditory cortex]]. ]] In [[neuroscience]], a '''neural network''' describes a population of physically interconnected [[neuron]]s or a group of disparate neurons whose inputs or signalling targets define a recognizable circuit. Communication between neurons often involves an [[electrochemical]] process. The interface through which they interact with surrounding neurons usually consists of several [[dendrites]] (input connections), which are connected via [[synapse]]s to other neurons, and one [[axon]] (output connection). If the sum of the input signals surpasses a certain [[threshold]], the neuron sends an [[action potential]] (AP) at the [[axon hillock]] and transmits this [[electrical signal]] along the axon. In contrast, a '''neuronal circuit''' is a functional entity of interconnected neurons that influence each other (similar to a [[control loop]] in [[cybernetics]]). ==Early study== {{See also|Connectionism}} Early treatments of neural networks can be found in [[Herbert Spencer]]'s ''Principles of Psychology'', 3rd edition (1872), [[Theodor Meynert]]'s ''[[Psychiatry]]'' (1884), [[William James]]' ''Principles of [[Psychology]]'' (1890), and [[Sigmund Freud]]'s Project for a Scientific Psychology (composed 1895). The first rule of neuronal learning was described by [[Donald Olding Hebb|Hebb]] in [[1949]], [[Hebbian learning]]. Thus, Hebbian pairing of pre-synaptic and post-synaptic activity can substantially alter the dynamic characteristics of the synaptic connection and therefore facilitate or inhibit signal transmission. The neuroscientists [[Warren Sturgis McCulloch]] and [[Walter Pitts]] published the first works on the processing of neural networks called "What the frog's eye tells to the frog's brain." They showed theoretically that networks of artificial neurons could [[implementation|implement]] [[logic]]al, [[arithmetics|arithmetic]], and [[symbolism|symbolic]] functions. Simplified models of biological neurons were set up, now usually called [[perceptrons]] or [[artificial neurons]]. These simple models accounted for [[neural summation]], i.e., potentials at the post-synaptic membrane will summate in the [[cell body]]. Later models also provided for excitatory and inhibitory synaptic transmission. ==Connections between neurons== {{See also|Synapse}} The connections between neurons are much more complex than those implemented in [[neural computing]] architectures. The basic kinds of connections between neurons are [[chemical synapse]]s and [[Electrical synapse|electrical]] [[gap junction]]s. One principle by which neurons work is neural summation, i.e. potentials at the post synaptic membrane will sum up in the cell body. If the [[depolarization]] of the neuron at the axon goes above threshold an action potential will occur that travels down the axon to the terminal endings to transmit a signal to other neurons. Excitatory and inhibitory synaptic transmission is realized mostly by [[inhibitory postsynaptic potential]]s and [[excitatory postsynaptic potential]]s. On the electrophysiological level, there are various phenomena which alter the response characteristics of individual synapses (called [[synaptic plasticity]]) and individual neurons ([[intrinsic plasticity]]). These are often divided into shortterm plasticity and longterm plasticity. Longterm synaptic plasticity is often contended to be the most likely [[memory]] substrate. Usually the term "plasticity" refers to changes in the brain that are caused by activity or experience. Connections display temporal and spatial characteristics. Temporal characteristics refer to the continuously modified activity-dependent efficacy of synaptic transmission, called [[action potential|spike]] dependent synaptic plasticity. It has been observed in several studies that the synaptic efficacy of this transmission can undergo shortterm increase (called [[synaptic facilitation|facilitation]]) or decrease ([[synaptic depression|depression]]) according to the activity of the presynaptic neuron. The induction of long-term changes in synaptic efficacy, by [[long-term potentiation]] (LTP) or [[long-term depression|depression]] (LTD), depends strongly on the relative timing of the onset of the [[excitatory postsynaptic potential|EPSP]] generated by the pre-synaptic AP, and the post-synaptic action potential. LTP is induced by a series of action potentials which cause a variety of biochemical responses. Eventually the reactions cause the insertion of new receptors into the cellular membrane of the dendrites, or serve to increase the efficacy of the receptors through [[phosphorylation]]. Backpropagating APs are impossible because after an action potential travels down a given segment of the axon, the voltage gated sodium channels' ([[sodium channel|Na+ channels]]) [[m gate]] becomes closed, thus blocking any transient opening of the [[h gate]] from causing a change in the intracellular [Na+], and hence preventing the generation of an action potential back towards the cell body. In some cells, however, [[neural backpropagation]] does occur through the [[dendrite|dendritic arbor]] and may have important effects on synaptic plasticity and computation. A neuron in the brain requires a single impulse to a neuromuscular junction to fire for the contraction of the postsynaptic muscle cell. In the spinal cord, however at least 75 [[afferent nerve|afferent]] neurons are required to produce firing. This picture is further complicated by variation in time constant between neurons, as some cells can experience their EPSPs over a wider period of time than others. While in synapses in the [[cognitive development|developing brain]] synaptic depression has been particularly widely observed it has been speculated that it changes to facilitation in adult brains. ==Representations in neural networks== A [[receptive field]] is a small region within the entire visual field. Any given neuron only responds to a subset of [[stimulus (physiology)|stimuli]] within its receptive field. This property is called tuning. As for [[visual system|vision]], in the earlier visual areas, neurons have simpler tuning. For example, a neuron in V1 may fire to any vertical stimulus in its receptive field. In the higher visual areas, neurons have complex tuning. For example, in the fusiform gyrus, a neuron may only fire when a certain face appears in its receptive field. It is also known that many parts of the brain generate patterns of electrical activity that correspond closely to the layout of the retinal image (this is known as [[retinotopy]]). It seems further that imagery that originates from the senses and internally generated imagery may have a shared [[ontology]] at higher levels of [[Cerebral cortex|cortical]] processing (see e.g. [[Language of thought]]). About many parts of the brain some characterization has been made as to what tasks are correlated with its activity. In the brain, [[memory|memories]] are very likely represented by patterns of activation amongst networks of neurons. However, how these [[Knowledge representation|representations]] are formed, retrieved and reach [[awareness|conscious awareness]] is not completely understood. [[Cognitive processes]] that characterize human [[intelligence (trait)|intelligence]] are mainly ascribed to the [[emergence|emergent properties]] of complex dynamic characteristics in the [[complex systems]] that constitute neural networks. Therefore, the study and modeling of these networks have attracted broad interest under different paradigms and many different theories have been formulated to explain various aspects of their behavior. One of these — and the subject of several theories — is considered a special property of a neural network: the ability to [[learning|learn]] complex patterns. ===Philosophical issues=== {{See also|Philosophy of perception}} Today most researchers believe in representations of some kind ([[representationalism]]) or, more general, in particular mental states ([[cognitivism]]). Thus, [[perception]] is [[Information Processing|information processing]] which is used to transfer information from the world into the brain/[[mind]] where it is further processed and related to other information ([[cognitive process]]es). Few others envisage a direct path back into the external world in the form of action (radical [[behaviourism]]). Another issue, called the [[binding problem]], relates to the question of how the activity of more or less distinct populations of neurons dealing with different aspects of [[perception]] are combined to form a [[percept|unified perceptual experience]] and have [[qualia]]. ==Study methods== {{See also|Neuropsychology|Cognitive neuropsychology}} Different [[neuroimaging]] techniques have been developed to investigate the activity of neural networks. The use of 'brain scanners' or functional neuroimaging to investigate the structure or function of the brain is common, either as simply a way of better assessing brain injury with high resolution pictures, or by examining the relative activations of different brain areas. Such technologies may include [[fMRI]] (functional Magnetic Resonance Imaging), [[Positron Emission Tomography|PET]] (Positron Emission Tomography) and [[Computed axial tomography|CAT]] (Computed axial tomography). [[Functional neuroimaging]] uses specific brain imaging technologies to take scans from the brain, usually when a person is doing a particular task, in an attempt to understand how the activation of particular brain areas is related to the task. In functional neuroimaging, especially [[FMRI|Functional Magnetic Resonance Imaging]] (fMRI), which measures hemodynamic activity that is closely linked to neural activity, [[Positron Emission Tomography]] (PET), and [[Electroencephalography]] (EEG) is used. [[Connectionism|Connectionist]] models serve as a test platform for different hypothesis of representation, information processing, and signal transmission. Lesioning studies in such models, e.g. [[artificial neural network]]s, where parts of the nodes are deliberately destroyed to see how the network performs, can also yield important insights in the working of several cell assemblies. Similarly, simulations of dysfunctional neurotransmitters in neurological conditions (e.g., dopamine in the basal ganglia of [[Parkinson's disease|Parkinson's]] patients) can yield insights into the underlying mechanisms for patterns of cognitive deficits observed in the particular patient group. Predictions from these models can be tested in patients and/or via pharmacological manipulations, and these studies can in turn be used to inform the models, making the process recursive. ==See also== *[[Artificial neural network]] *[[Biological cybernetics]] *[[Biological neuron models]] *[[Biologically-inspired computing]] *[[Holistic science]] *''[[How the Mind Works]]'' by [[Steven Pinker]] *[[List of regions in the human brain]] *[[Neural computing]] *[[Neural ensemble]] *[[Neural network]] *[[Neurally controlled animat]] *[[Neuroinformatics]] *[[Parallel distributed processing]] *[[Pulse-coupled networks]] ==External links== *[http://www.benbest.com/science/anatmind/anatmd3.html Learning, Memory and Plasticity] *[http://www.his.sunderland.ac.uk/ps/worksh2/denham.pdf Comparison of Neural Networks in the Brain and Artificial Neural Networks] *[http://ocw.mit.edu/OcwWeb/Brain-and-Cognitive-Sciences/9-95-AResearch-Topics-in-NeuroscienceJanuary--IAP-2003/LectureNotes/ Lecture notes at MIT OpenCourseWare] *[http://www.willamette.edu/~gorr/classes/cs449/brain.html Computation in the Brain] *[http://ifcsun1.ifisiol.unam.mx/Brain/neuron2.htm Signaling Properties of the Neuron] *[http://diwww.epfl.ch/~gerstner/SPNM/node6.html The Problem of Neuronal Coding] *[http://www.ymer.org/amir/software/biological-neural-networks-toolbox/ Biological Neural Network Toolbox] - A free Matlab toolbox for simulating networks of several different types of neurons {{nervous_system}} [[Category:Neural networks]] [[Category:Nervous system]] [[Category:Cognition]] [[bg:Изкуствена невронна мрежа]] [[da:Neuralt netværk]] [[de:Neuronales Netz]] [[fr:Réseau de neurones]] [[it:Rete neurale]] [[nl:Neuraal netwerk]] [[ja:ニューラルネット]] [[pl:Sieć neuronowa]] [[pt:Redes neurais biológicas]] [[ru:Нейронные сети]] [[th:ข่ายงานประสาทเทียม]] [[zh:神经网络]]