Bijan Pesaran

Neuronal Dynamics and Decision Making

Cerebral cortex contains a mosaic of brain areas that are connected to form distributed networks. In frontal and parietal cortex, these networks are specialized for movements such as saccadic eye movements and reaches. Before each movement we decide to make, these areas contain specific patterns of neural activity which can be used to predict what we will do. My research investigates the dynamics of neuronal activity to understand how brain areas interact during decision-making and tries to "decode" neuronal activity to predict our choices.

For my PhD, I analyzed neural activity from a variety of imaging and electrophysiology experiments. One exciting set of results came from studying single cell activity and local field potential (LFP) activity recorded in parietal area LIP during saccades. LFP activity is believed to reflect local synaptic activity in a population of cells near the recording electrode. We found that oscillations in the LFP predict the direction of a saccade and that these oscillations are coherent with the spiking of single cells. This result has stimulated research into LFP activity and its relation to spiking and behavior. Since LFP activity predicts movements and is easier to record than the activity of single cells, this result may accelerate the development of a brain-machine interface to help paralyzed and locked-in patients.

Recorded single cell activity is thought to predominantly reflect outputs of an area while LFP activity reflects synaptic inputs. This means analyzing spiking and LFP activity simultaneously recorded in multiple areas could be especially useful for investigating interactions between brain areas. For my postdoc, I studied how freely chosen eye movements are coordinated with decisions for reaches, and how activity in reaching areas of parietal and frontal cortex reflects these reach choices. To do this, I developed techniques for simultaneous multiple-area recordings in behaving animals using arrays of electrodes. We found correlations between neural activity in different brain areas reflects whether a subject is making choices or following instructions. We also found eye movement information could be combined with neural activity to improve predictions of reach choices.

Research in my lab continues this work using a combination of experimental and engineering approaches. Multiple area recordings and stimulation will be used to probe the network mechanisms of decision making. Algorithms that predict these choices will then be implemented in real-time systems that will form the basis of a brain-machine interface to translate thought into action. The long-term goal of research in my lab is to understand decision making at the level of brain networks and to apply this knowledge to help paralyzed and locked-in patients.

E-mail: bijan [AT] nyu [DOT] edu

Representative Publications

Mitra, P.P. and Pesaran, B. Analysis of dynamic brain imaging data. Biophys J 76, 691-708 (1999)

Pesaran, B., Pezaris, J.S., Sahani, M., Mitra, P.P. and Andersen, R.A. Temporal structure in neuronal activity during working memory in macaque parietal cortex. Nat Neurosci 5, 805-811 (2002)

Andersen, R.A., Musallam, S. and Pesaran, B. Selecting signals for neural prosthetics. Curr Op Neurobiol 14,720-726 (2004)

Pesaran, B., Musallam, S. and Andersen, R.A. Cognitive neural prosthetics. Curr Biol 16, R77-R80 (2006)

Pesaran, B., Nelson, M.J. and Andersen, R.A. Dorsal premotor neurons encode the relative position of the hand, eye, and goal during reach planning. Neuron 51,125-134 (2006)

Pesaran, B., Nelson M.J. and Andersen, R.A. Free choice activates a decision circuit between frontal and parietal cortex. Nature 453,406-409 (2008)

Additional publications (PubMed)

CV

Center for Neuroeconomics at NYU