In neuroscience, a neural network describes a population of physically interconnected neurons 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 synapses 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.
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 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 implement logical, arithmetic, and 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.
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 stimuli within its receptive field. This property is called tuning. As for 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 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, memories are very likely represented by patterns of activation amongst networks of neurons. However, how these representations are formed, retrieved and reach conscious awareness is not completely understood. Cognitive processes that characterize human intelligence are mainly ascribed to the 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 learn complex patterns.
Today most researchers believe in representations of some kind (representationalism) or, more general, in particular mental states (cognitivism). For instance, perception can be viewed as information processing through transfer information from the world into the brain/mind where it is further processed and related to other information. 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 unified perceptual experience and have qualia.
Neuronal networks are not full reconstructions of any cognitive system found in the human brain, and are therefore unlikely to form a complete representation of human perception. Some researchers argue that human perception must be studied as a whole; hence, the system cannot be taken apart and studied without destroying its original functionality. Furthermore, there is evidence that cognition is gained through a well orchestrated barrage of sub-threshold synaptic activity throughout the network.
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), PET (Positron Emission Tomography) and 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 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.
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 networks, 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 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.
- Learning, Memory and Plasticity
- Comparison of Neural Networks in the Brain and Artificial Neural Networks
- Lecture notes at MIT OpenCourseWare
- Computation in the Brain
- Signaling Properties of the Neuron
- The Problem of Neuronal Coding
- Biological Neural Network Toolbox - A free Matlab toolbox for simulating networks of several different types of neurons
- WormWeb.org: Interactive Visualization of the C. elegans Neural Network - C. elegans, a nematode with 302 neurons, is the only organism for whom the entire neural network has been uncovered. Use this site to browse through the network and to search for paths between any 2 neurons.