Non-spiking neuronNon-spiking neurons are neurons that are located in the central and peripheral nervous systems and function as intermediary relays for sensory-motor neurons. They do not exhibit the characteristic spiking behavior of action potential generating neurons. Non-spiking neural networks are integrated with spiking neural networks to have a synergistic effect in being able to stimulate some sensory or motor response while also being able to modulate the response.
NeuronWithin a nervous system, a neuron, neurone, or nerve cell is an electrically excitable cell that fires electric signals called action potentials across a neural network. Neurons communicate with other cells via synapses - specialized connections that commonly use minute amounts of chemical neurotransmitters to pass the electric signal from the presynaptic neuron to the target cell through the synaptic gap. The neuron is the main component of nervous tissue in all animals except sponges and placozoa.
Artificial neural networkArtificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.
LearningLearning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single event (e.g. being burned by a hot stove), but much skill and knowledge accumulate from repeated experiences. The changes induced by learning often last a lifetime, and it is hard to distinguish learned material that seems to be "lost" from that which cannot be retrieved.
Homeostatic plasticityIn neuroscience, homeostatic plasticity refers to the capacity of neurons to regulate their own excitability relative to network activity. The term homeostatic plasticity derives from two opposing concepts: 'homeostatic' (a product of the Greek words for 'same' and 'state' or 'condition') and plasticity (or 'change'), thus homeostatic plasticity means "staying the same through change". Homeostatic synaptic plasticity is a means of maintaining the synaptic basis for learning, respiration, and locomotion, in contrast to the Hebbian plasticity associated with learning and memory.
Medium spiny neuronMedium spiny neurons (MSNs), also known as spiny projection neurons (SPNs), are a special type of GABAergic inhibitory cell representing 95% of neurons within the human striatum, a basal ganglia structure. Medium spiny neurons have two primary phenotypes (characteristic types): D1-type MSNs of the direct pathway and D2-type MSNs of the indirect pathway. Most striatal MSNs contain only D1-type or D2-type dopamine receptors, but a subpopulation of MSNs exhibit both phenotypes.
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Threshold potentialIn electrophysiology, the threshold potential is the critical level to which a membrane potential must be depolarized to initiate an action potential. In neuroscience, threshold potentials are necessary to regulate and propagate signaling in both the central nervous system (CNS) and the peripheral nervous system (PNS). Most often, the threshold potential is a membrane potential value between –50 and –55 mV, but can vary based upon several factors.
Artificial neuronAn artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network. Artificial neurons are elementary units in an artificial neural network. The artificial neuron receives one or more inputs (representing excitatory postsynaptic potentials and inhibitory postsynaptic potentials at neural dendrites) and sums them to produce an output (or , representing a neuron's action potential which is transmitted along its axon).
Spiking neural networkArtificial neural network Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather transmit information only when a membrane potential—an intrinsic quality of the neuron related to its membrane electrical charge—reaches a specific value, called the threshold.
