January 31, 2017

Guerguiev J, Lillicrap TP, Richards BA, “Towards deep learning with segregated dendrites”

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a […]
October 14, 2015

van Rheede JJ, Richards BA, Akerman CJ, “Sensory-Evoked Spiking Behavior Emerges via an Experience-Dependent Plasticity Mechanism”, Neuron, 87(5), 1050-1062

The ability to generate action potentials (spikes) in response to synaptic input determines whether a neuron participates in information processing. How a developing neuron becomes an active participant in a circuit or whether this process is activity dependent is not known, especially as spike-dependent plasticity mechanisms would not be available to non-spiking neurons. Here we use the optic tectum of awake Xenopus laevis tadpoles to determine how a neuron becomes able to generate sensory-driven spikes in vivo. At the onset of vision, many tectal neurons do not exhibit visual spiking behavior, despite being intrinsically excitable and receiving visuotopically organized synaptic […]
June 24, 2015

Deep learning in the neocortex

Deep learning is an approach to artificial intelligence (AI) that loosely mimics how the brain works. By mimicking the operations of the brain, deep learning can rival, or even outperform, human beings in a number of functions, such as image recognition [1], motor control [2], and speech recognition [3]. Moreover, deep networks can develop representations that are a better match to recordings in the neocortex of humans or non-human primates than existing models in neuroscience [4,5]. This suggests that deep learning captures something important about how our own brains work. The key to “deep” learning (as opposed to “shallow” learning) […]
June 24, 2015

Multiple memory systems for enhanced reinforcement learning

Not all of our memories are stored in the same way. We have multiple memory systems that can provide different types of information [1,2]. Some of our memories are very detailed, giving us the ability to recall specific events and remember what it felt like when we were living it [3]. Our recent memories are stored in this way, as are some of the more emotionally salient events from our life. For example, you might be able to recall exactly what you had for breakfast yesterday or the song that was playing during your first kiss. In contrast, many of […]