314 Boyden Hall
One of the most distinguishing properties of the nervous system is its plasticity. Plasticity brings along as a consequence the potential for instability of the system. However, neurons and neural networks maintain remarkably stable properties. This can be observed at many levels, from single neurons to the behaving animal (we maintain a relatively stable personality for decades after all!). Such stability enables the nervous system to maintain an identity, while plasticity simultaneously allows the system to change and adapt to external and internal stimuli. Many of the mechanisms that allow the nervous system to retain its plasticity and be stable simultaneously are not understood. Plasticity has been studied mostly at the level of synapses, and it is believed to underlie learning and memory. However, long-term plasticity also takes place at the level of voltage-gated ionic currents that determine cellular excitability and electric activity of both neurons and neural networks. This form of plasticity can in principle also underlie some forms of learning and memory (Marder et al, 1996), as well as recovery from injury.
In my laboratory we are thus interested in understanding mechanisms of neuronal plasticity and homeostasis of the ionic currents that determine the electric activity of neurons. We are also very interested in understanding what (factors?) regulate the growth and survival of neurons and glial cells in the crustacean nervous system. Small neuropeptides expressed in the crustacean nervous system are potential, yet unstudied, growth factors.
To try to answer some of the issues mentioned above we use as a model system simple neural networks (i.e. few neurons) found in the crustacean stomatogastric nervous system. Using both cell biological, electrophysiological and computational tools, in my laboratory we study mechanisms of neuronal plasticity and homeostasis of the ionic currents that determine the electric activity of neurons and simple neural networks in the crustacean stomatogastric nervous system. We also use computer models to understand how neuronal ionic currents interact to produce activity and to interface in real time with electrophysiological recordings of neuronal activity (for which we use a new electrophysiological recording technique called 'dynamic clamp'). At the single cell level we find that the complexity provided by the combination of several non-linear processes, i.e. voltage-dependent ionic currents, leads to the generation of multiple states that can be characterized electrophysiologically and that depend on the history (initial conditions) of the system. Each one of these states is stable, in the sense that it can be generated by many possible combinations of the parameters that characterize the system. Thus, such a system is both robust and flexible (Goldman et al, 2001). In this context we study the mechanisms of recovery of normal single-cell neuronal and network activity in response to injury and removal of central modulatory inputs (decentralization). We hypothesize that these mechanisms involve the activation and expression of voltage-dependent ionic channels de novo, and inactivation or removal of existing ones. These changes lead to dynamical modifications in activity patterns during the course of their recovery to a functional state that is very similar to the non-decentralized (normal) system (Luther et al, 2003).
We are also studying trophic effects of substances (i.e. oligopeptides and amines) that in our preparation as well as in other preparations are better known to behave as neuromodulators instead of trophic factors. These effects are likely to modify the dynamic range of states that the system can express or develop into both during development and in the adult.
Additionally, we study other forms of plasticity and growth regulation in cultured dissociated neurons and long term organotypical culture.
B.S. in Biology, Universidad de Chile, 1984.
Ph.D. in Biophysics, Brandeis University, 1990.
Zhao S, and Golowasch J. Ionic current correlations underlie the global tuning of large numbers of neuronal activity attributes.. J Neurosci. 32:13380-8, 2012.
Unal CT, Golowasch JP, and Zaborsky L. Adult mouse basal forebrain harbors two distinct cholinergic populations defined by their electrophysiology. Front Behav Neursci. 6:21, 2012.
Temporal S, Desai M, Khorkova O, Varghese G, Dai A, Schulz DJ, and Golowasch J. Neuromodulation independently determines correlated channel expression and conductance levels in motor neurons of the stomatogastric ganglion. J Neurophysiol. 107:718-727, 2012.
Zhang, Y., and Golowasch, J. Recovery of rhythmic activity in a central pattern generator: analysis of the role of neuromodulator and activity-dependent mechanisms. J Comput Neurosci. 31:685-699, 2012.
Zhao, S., Golowasch, J., and Nadim, F. Pacemaker neuron and network oscillations depend on a neuromodulator-regulated linear current. Front. Behav. Neurosci. 4:21, 2010.
Golowasch, J., Thomas, G., Taylor, A.L., Patel, A., Pineda, A., Khalil, C., and Nadim, F. Membrane capacitance measurements revisited: dependence of capacitance value on measurement method in nonisopotential neurons. J. Neurophysiol. 102 :2161-75, 2009.
Zhang, Y., Khorkova, O., Rodriguez, R., and Golowasch, J. Activity and neuromodulatory input contribute to the recovery of rhythmic output after decentralization in a central pattern generator. J. Neurophysiol. 101: 372-86, 2009.
Zhang, Y., and Golowasch, J. Modeling Recovery of Rhythmic Activity: Hypothesis for the role of a calcium pump. Neurocomputing. 70: 1657-1662, 2007.
Gansert, J., Golowasch, J and Nadim, F. Sustained rhythmic activity in gap- neurons depends on the diameter of coupled dendrites. J. Neurophysiology. 98: 3450-3460, 2007.
Khorkova, O. and Golowasch, J. Neuromodulators, not activity, control coordinated expression of ionic currents. J. Neuroscience, 27: 8709-8718, 2007.
Zhang, Y and Golowasch, J. Modeling Recovery of Rhythmic Activity: Hypothesis for the role of a calcium pump. Neurocomputing, 70: 1657-1662, 2007
Haedo, R. and Golowasch, J. Ionic Mechanism Underlying Recovery of Rhythmic Activity in Adult Isolated Neurons. J. Neurophysiology, 96: 1860-1876, 2006.
Nadim, F. and Golowasch, J. Signal transmission between gap-junctionally coupled passive cables is most effective at an optimal diameter. J. Neurophysiology, 95 (6): 3831-3843, 2006.
Rabbah, P., Golowasch, J. and Nadim, F. Effect of electrical coupling on ionic current and synaptic potential measurements. J Neurophysiol., 2005. [Link]
Luther, J. Robie, A.A., Yarotsky, J., Reina, Ch., Marder, E. and Golowasch, J. Episodic Bouts of Activity Accompany Recovery of Rhythmic Output By a Neuromodulator- and Activity-Deprived Adult Neural Network. J. Neurophysiology, 90: 2720-2730, 2003.
Golowasch, J., Goldman, M.S., Abbott, L.F. and Marder, E. Failure of averaging in the construction of conductance-based neuron models. J. Neurophysiology, 87: 1129-1131, 2002.
Goldman, M.S., Golowasch, J., Marder, E. and Abbott, L.F. Global structure, robustness, and modulation of neuronal models. J. Neuroscience., 21(14): 5229-5238, 2001.
Goldman, M.S., Golowasch, J., Abbott, L.F. and Marder, E. Dependence of firing pattern on intrinsic ionic conductances: sensitive and insensitive combinations. Neurocomputing, 32-33:141-146, 2000.
Swensen, A.M., Golowasch, J., Christie, A.E., Coleman, M.J., Nusbaum, M.P. and Marder, E. GABA and responses to GABA in the stomatogastric ganglion of the crab Cancer borealis. J. Exp Biol., 203(14): 2075-2092, 2000.
Golowasch, J., Abbott, L.F. and Marder, E. Activity dependent regulation of potassium currents in the stomatogastric ganglion of the crab, Cancer borealis. J. Neuroscience, 19: RC33 (1-5), 1999.
Golowasch, J., Manor, Y. and Nadim, F. Recognition of Slow Processes in Rhythmic Networks. Trends in Neuroscience, 22(9): 375-377, 1999.
Marder, E., Abbott, L.F., Turrigiano, G.G., Liu, Z. and Golowasch, J. Memory from the dynamics of intrinsic membrane currents. Proc. Natl. Acad. Sci., USA. 93: 13481-13486, 1996.