Both, #neuroscience and deep learning face a similar challenge: how do neural networks, consisting of interconnected elements, transform representations of stimuli across multiple processing stages to implement a wide range of complex computations and interactive responses?
Artificial neural networks are computing systems slightly inspired by the biological neural networks that constitute a brain.
The connections between neurons in the brain are much more complex than those of the artificial neurons use in the neural computing models of #ArtificialNeuralNetworks. The basic kind of connections between neurons are synapses; both, chemical and electrical. Information moves from neuron to neuron in the form of electrical signals. These signals have to “jump” from one neuron to another across a small space called the synapse.
How information can move across? They do it through chemicals that carry signals (neurotransmitters) across the space between #neurons (synapse). They act as messengers. Some people might have fewer of them. And some may have messengers that have trouble transmitting the information to the next neuron. These are examples of issues that can affect the processing speed.
Neurons routinely work together to transmit information through stablished pathways. These pathways are the neural networks. They are a bit like roads. A neural circuit is a population of neurons interconnected by synapses to carry out specific function when activated. Neural circuits interconnect to one another to form neural networks. Processing speed depends on how efficient or organized these neural networks are. The more we do a certain task, the more efficient (or more densely packed) this part become, enabling a task being quicker to process.