Farzan Nadim, Ph.D. Professor

Federated Dept. of Biological Sciences and

Dept. of Mathematical Sciences,

RESEARCH

I combine computational, analytical and experimental techniques towards understanding how properties of neurons and their synaptic dynamics shape the output of oscillatory neuronal networks. In particular, our laboratory studies the generation of rhythmic motor patterns in the crustacean stomatogastric nervous system (STNS). These rhythmic patterns are responsible for chewing and digestion of food in the intact animal, but persist in an acutely isolated nervous system in vitro.

The main focus of my research is to understand how synaptic dynamics, such as short-term depression and facilitation contribute to the generation and control of oscillatory neuronal activity. Such synaptic dynamics are found ubiquitously in all parts of the nervous systems. My modeling approach is to use geometric dynamical systems to follow the global behavior of models of neurons and small networks and, at the same time, to build biophysically realistic computer models of the system under study. Experiments in our lab involve characterization of the synaptic dynamics in the STNS and studying the contribution of these dynamics to network output in the biological network.

Using these techniques we have recently discovered several important consequences of synaptic dynamics in oscillatory networks. First, in several related modeling and experimental studies, we showed that the presence of synaptic depression in oscillatory networks involving feedback inhibition could lead to bistability in the network output. In a separate set of studies involving both experiments and modeling, we showed that an important contribution of synaptic depression in oscillatory networks in which neurons are active at different phases of the oscillation is to promote a constant activity phase for each neuron when the oscillation frequency is altered.

The STNS is affected by multiple neuromodulators and neurohormones that produce a large degree of plasticity in the rhythmic patterns produced by these networks. The amplitude and dynamics of synaptic currents is greatly affected by neuromodulation. Moreover, the effect of these modulators is dependent on the previous history of activity, or the state of the system. Such state-dependence produces an extra degree of plasticity that is another research focus in our laboratory.

Another project that we work on involves interactions between a fast and slow rhythmic network. I am specifically interested in the interaction between the slow gastric mill rhythm (period ~10sec) and the much faster pyloric rhythm (period ~1sec). These two oscillatory patterns interact through identified synaptic pathways. A modeling study, based on anatomical and physiological data, of the gastric mill rhythm elicited by the modulatory neuron MCN1 led to a surprising result. The model predicted that the activity of the fast pyloric oscillator completely controls the period of the slow gastric mill oscillator. In particular, the fast rhythm can alter the period of the slow rhythm over a range much larger than its own period, and the suppression of the fast rhythm significantly slows down or even disrupts the slow oscillations. The model also predicted that the MCN1-elicited gastric mill rhythm is time-locked to the pyloric rhythm. My collaborators and I have started to confirm the model predictions by electrophysiological experiments. My current goal is to determine the network mechanisms underlying coordination of distinct network activities, with a special focus on how such coordination breaks apart.

My research has helped identify new mechanisms through which a fast and a slow oscillatory network coordinate their activities. Elucidating mechanisms through which non-identical networks interact will help us understand, at a cellular and network level, how widespread synchronous patterns arise in large non-homogeneous networks, such as the brain. Such widespread synchronization of rhythmic activity among networks of neurons that normally function to produce distinct behavior can lead to disorders such as generalized epilepsy and Parkinson's disease. 

Our laboratory develops and maintains several software Virtual Instruments in LabWindows/CVI (National Instruments, TX). These software instruments include a Windows version of dynamic clamp, an arbitrary waveform generator and even a software chart-recorder, oscilloscope and digitizer. These software applications are available at http://stg.rutgers.edu/software/software.htm.

Copyright: STG Lab 2006
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