Past events

The octopus is an active hunter, a remarkable invertebrate whose complex behaviors rely on exceptionally good visual and tactile senses coupled with highly advanced learning and memory (LM) abilities. Studying the octopus LM system may therefore reveal characteristics universally important for mediation of complex behaviors. We developed slice and isolated brain preparations of the LM area in the octopus brain to characterize the short- and long-term neural plasticity. The importance of these processes for LM are been tested in behavioral experiments. The results support the importance of LTP in behavioral LM and suggest new ideas regarding the organization of short- and long-term memory systems.

I am interested in how neural dynamics, changes in neural sensitivity on the time scale of milliseconds to seconds, support rapid changes in perceptual capability. Our key focus is in testing the hypothesis that dynamics in early sensory cortices, such as the primary somatosensory cortex, play a key role in perception. To examine these issues in a model where detailed invasive studies are possible, we study the vibrissa sensory system of rodents. To examine the broader relevance of these findings, we have a smaller parallel effort I human perception and imaging. A variety of our studies suggest that. In this seminar, I will describe work we have done to understand the 'natural scene' of vibrissa perception, the signals that are generated during active surface contact. I will then describe how these signals are encoded in 2 recently discovered cortical maps, a frequency map, shaped by the resonance properties of the vibrissae, and a direction map, encoding the angle of vibrissa motion. I will then discuss why having these cortical feature columns, with adjacent neurons sharing similar computational processing, may enhance information processing. Specifically, I will argue that this arrangement facilitates dynamic regulation of neural sensitivity by neuromodulators that have a more course spatial precision, on the order of 300-1000 microns. I will briefly mention the hemo-neural hypothesis, the proposal that changes in blood flow and volume may be one source of neural regulation on this spatial scale. I will then describe an example of the dynamic regulation of spatial activation across a cortical map, resulting from adaptation during robust thalamocortical input at 5-25 Hz. I will argue that this adaptation leads to a transition into a state that is enhanced for discrimination of alternative stimuli, but impoverished for detection of novel stimuli (decreased sensitivity). To test the hypothesis that these kinds of cortical dynamics in the primary somatosensory cortex regulate perception, I will describe human MEG experiments showing that changes in the amplitude of SI activation regulate detection probability, and showing that these changes in perception and cortical amplitude are predicted by ongoing rhythmic activity in human SI at 5-25 Hz (the 'mu' rhythm).

Large, strongly coupled neural networks tend to produce chaotic spontaneous activity. This might appear to make them unsuitable for generating reliable sensory responses or repeatable motor patterns. However, this is not the case. Inputs can induce a phase transition, leading to responses uncontaminated by chaotic "noise". Likewise, appropriately trained feedback units can control the chaos, resulting in a wide variety of repeatable output patterns. These issues will be discussed accompanied by examples and demonstrations.

It is usually assumed that the brain learns by changing neural circuits that are otherwise stable. However, recent experiments in monkeys show that the neural representation of movement in motor cortex is continually changing even without learning, when practicing a familiar task. We set to investigate the reason for these changes. We analyzed the empirical data and found that the changes are slow and random. We constructed a theoretical model of a cortical network that learns a motor skill by changing synaptic strengths. Our model explains how the network can change its synaptic strengths, and neural activity, without changing the motor behavior. Additionally, our model replicates the observed changes when synaptic learning is assumed highly noisy. We speculate that this noise serves to explore different synaptic configurations during learning.