It's fair to say that our world has reached a point where technology is so advanced that robots are almost expected to be lifelike – but what about robots that develop mental illnesses, hallucinations and depression like human beings do? Is this just science fiction, or can we really expect artificial intelligence to grow even more similar to humans in the not-so-distant future? Back in March, New York University hosted a symposium in New York City called Canonical Computations in Brains and Machines, where a group of neuroscientists and experts in the field of artificial intelligence spoke about overlaps in the ways in which human beings and machines think and process information. According to one of these neuroscientists – Zachary Mainen of the Champalimaud Centre for the Unknown – we might expect advanced machines to soon be able to experience some of the same mental problems that people do. "I'm drawing on the field of computational psychiatry, which assumes we can learn about a patient who's depressed or hallucinating from studying AI algorithms like reinforcement learning. If you reverse the arrow, why wouldn't an AI be subject to the sort of things that go wrong with patients?"

Scientists believe the introduction of a hormone-like system, such as the one found in the human brain, could give AI the ability to reason and make decisions like people do. Recent research indicates human emotion, to a certain extent, is the byproduct of learning. And that means machines may have to risk depression or worse if they ever want to think or feel. Zachary Mainen, a neuroscientist at the Champalimaud Centre for the Unknown in Lisbon, speaking at the Canonical Computation in Brains and Machines symposium, discussed the implications of recent experiments to discover the effects serotonin has on decision making. According to Mainen and his team, serotonin may not be related to'mood' or emotional states such as happiness, but instead is a neuro-modulator designed to update and change learning parameters in the brain.

We are exploring the significance of biological complexity for neuronal computation. Here we demonstrate that Hebbian synapses in realistically-modeled hippocampal pyramidal cells may give rise to two novel forms of self -organization in response to structured synaptic input. First, on the basis of the electrotonic relationships between synaptic contacts, a cell may become tuned to a small subset of its input space. Second, the same mechanisms may produce clusters of potentiated synapses across the space of the dendrites. The latter type of self-organization may be functionally significant in the presence of nonlinear dendritic conductances.

We are exploring the significance of biological complexity for neuronal computation. Here we demonstrate that Hebbian synapses in realistically-modeled hippocampal pyramidal cells may give rise to two novel forms of self -organization in response to structured synaptic input. First, on the basis of the electrotonic relationships between synaptic contacts, a cell may become tuned to a small subset of its input space. Second, the same mechanisms may produce clusters of potentiated synapses across the space of the dendrites. The latter type of self-organization may be functionally significant in the presence of nonlinear dendritic conductances.

We are exploring the significance of biological complexity for neuronal computation. Here we demonstrate that Hebbian synapses in realistically-modeled hippocampalpyramidal cells may give rise to two novel forms of self-organization in response to structured synaptic input. First, on the basis of the electrotonic relationships between synaptic contacts, a cell may become tuned to a small subset of its input space. Second, the same mechanisms may produce clusters of potentiated synapses across the space of the dendrites. The latter type of self-organization may be functionally significant in the presence of nonlinear dendritic conductances.