I’m excited to announce that my latest scientific publication – “Multi-task connectivity reveals flexible hubs for adaptive task control” – was just published in Nature Neuroscience. The paper reports on a project I (along with my co-authors) have been working on for over a year. The goal was to use network science to better understand how …
Continue reading “The brain’s network switching stations for adaptive behavior”
Topic 6: Causal Understanding Causal understanding is an important part of human cognition. How do we understand that a particular event or force has caused another event? How do realize that inserting coins into a soda machine results in a cool beverage appearing below? And ultimately, how do we understand people’s reactions to events? The …
Computational models that are implemented, i.e., written out as equations or software, are an increasingly important tool for the cognitive neuroscientist. This is because implemented models are, effectively, hypotheses that have been worked out to the point where they make quantitative predictions about behavior and/or neural activity. In earlier posts, we outlined two computational models …
Continue reading “A Brief Introduction to Reinforcement Learning”
Cognitive neuroscience constantly works to find the appropriate level of description (or, in the case of computational modeling, implementation) for the topic being studied. The goal of this post is to elaborate on this point a bit and then illustrate it with an interesting recent example from neurophysiology. As neuroscientists, we can often choose to …
Continue reading “Levels of Analysis and Emergence: The Neural Basis of Memory”
I recently watched this talk (below) by Joaquin Fuster. His theories provide a good integration of cortical functions and distributed processing in working and long-term memory. He also has some cool videos of likely network interactions across cortex (in real time) in his talk. Here is a diagram of Dr. Fuster’s view of cortical hierarchies: …
Modern technologies allow eye movements to be used as a tool for studying language processing during tasks such as natural reading. Saccadic eye movements during reading turn out to be highly sensitive to a number of linguistic variables. A number of computational models of eye movement control have been developed to explain how these variables …
Several months ago I posted The Will to be Free, Part I. In that post I explained that memory is the key to free will. However, this insight isn’t quite satisfactory. We need three additional things to complete the picture: the ability to choose based on predictions, internal desires, and self-awareness. (A quick disclaimer: These …
Hippocampus is involved in feature binding for novel stimuli (McClelland, McNaughton, & O'Reilly – 1995, Knight – 1996, Hasselmo – 2001, Ranganath & D'Esposito – 2001) It was demonstrated by McClelland et al.that, based on its role in episodic memory encoding, hippocampus can learn fast arbitrary association. This was in contrast to neocortex, which they …
Continue reading “History’s Top Brain Computation Insights: Hippocampus binds features”
Following up on MC's posts about the significant insights in the history of neuroscience, I'll now take Neurevolution for a short jaunt into neuroscience's potential future. In light of recent advances in technologies and methodologies applicable to neuroscience research, the National Science Foundation last summer released a document on the "Grand Challenges of Neuroscience". These …
25) The dopamine system implements a reward prediction error algorithm (Schultz – 1996, Sutton – 1988) It used to be that the main thing anyone "knew" about the dopamine system was that it is important for motor control. Parkinson's disease, which visibly manifests itself as motor tremors, is caused by disruption of the dopamine …
Continue reading “History’s Top Brain Computation Insights: Day 25”
22) Recurrent connectivity in neural networks can elicit learning and reproduction of temporal sequences (Jordan – 1986, Elman – 1990, Schneider – 1991) Powerful learning algorithms such as Hebbian learning, self-organizing maps, and backpropagation of error illustrated how categorization and stimulus-response mapping might be learned in the brain. However, it remained unclear how sequences and …
Continue reading “History’s Top Brain Computation Insights: Day 22”
21) Parallel and distributed processing across many neuron-like units can lead to complex behaviors (Rumelhart & McClelland – 1986, O'Reilly – 1996) Pitts & McCullochprovided amazing insight into how brain computations take place. However, their two-layer perceptrons were limited. For instance, they could not implement the logic gate XOR (i.e., 'one but not both'). An …
Continue reading “History’s Top Brain Computation Insights: Day 21”
20) Spike-timing dependent plasticity: Getting the brain from correlation to causation (Levy – 1983, Sakmann – 1994, Bi & Poo – 1998, Dan – 2002) Hebb's original proposal was worded as such: "When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, …
Continue reading “History’s Top Brain Computation Insights: Day 20”
19) Neural networks can self-organize via competition (Grossberg – 1978, Kohonen – 1981) Hubel and Wiesel's work with the development of cortical columns (see previous post) hinted at it, but it wasn't until Grossberg and Kohonen built computational architectures explicitly exploring competition that its importance was made clear. Grossberg was the first to illustrate the …
Continue reading “History’s Top Brain Computation Insights: Day 19”
18) Behavior exists on a continuum between controlled and automatic processing (Schneider & Shiffrin – 1977) During the 1970s those studying the cognitive computations underlying visual search were at an impasse. One group of researchers claimed that visual search was a flat search function (i.e., adding more distracters doesn't increase search time), while another group …
Continue reading “History’s Top Brain Computation Insights: Day 18”
