* Input of continuous unlabeled time-based patterns.
* Associative Hebbian Learning (when distinct inputs/patterns come together, they are neuron-wired together). Synapses can be modified via experience. See "Hebbian Theory".
* The brain is a prediction machine: it is always trying to predict the future based on past learned patterns. Learning happens when reality does not match the originally prediction and we rewire the world model based on new input. See "Bayesian approaches to brain function".
* Input signals are processed by many layers, each one creating more abstraction from the previous one, from sensory neurons to the highest cortex layers.
* Each region of the hierarchy forms invariant memories (what a typical region of cortex learns is sequences of invariant representations).
* There is lots of feedback (highest level neurons back to the lowest levels). In some structures (e.g. the thalamus, that is a kind of "hub of information") connections going backward (toward the input) exceed the connections going forward by almost a factor of ten.
* Brain uses Sparse Distributed Memory (SDM). See SDM by Pentti Kanerva (NASA researcher).
* Neuron models have many more variable/parameters (that can be used to transfer or process information) than usual nodes/links from artificial neural networks. E.g.: Long-term potentiation vs Long-term depression, neuronal Habituation vs Sensitization, inhibitory vs excitatory neurons, firing rates, synchronization, neuromodulation, homeostasis and more.
* The backward propagation of errors in artificial neural networks only occurs during the learning phase. But the brain is always learning and updating weights and relationships between patterns, given new inputs.
* During repetitive learning, representations of objects move down the cortical hierarchy (from short-term memory to long-term memory), forming invariant memories.
* The brain needs to replay the memory (memory rehearsal) of a learned stimulus so it can be stored in long-term memory.
* The job of any cortical region is to find out how inputs are related (pattern recognition), to memorize the sequence of correlations between them, and to use this memory to predict how the inputs will behave in the future.
* Predictive coding: the brain is constantly generating and updating hypotheses that predict sensory input at varying levels of abstraction.