Gabrielle J. Gutierrez 1, Larry F. Abbott 2, Eve Marder 1 1 Volen Center for Complex Systems, Brandeis University 2 Department of Neuroscience, Department of Physiology and Cellular Biophysics, Columbia University Recent theoretical work on networks has shown that, due to the nonlinear dynamics that govern its behavior, a network does not have to be built for a specific task in order to perform that task. With the addition of some form of “read-out” circuitry, such as a downstream neuron or a model cell, a given task can be achieved with the right combination of synaptic weights for the connections from the cells in the network to the read-out cell. However, it is unclear whether a complex biological network can actually compute as broad a range of functions as these models are predicting. My research is focused on investigating whether a biological network can act as such a dynamical computing reservoir. It is unclear whether a complex biological network can actually compute as broad a range of functions as these models are predicting. My research is focused on finding out whether a biological network can act as such a dynamical computing reservoir. The stomach is dissected from the crab and the Stomatogastric Nervous System (STNS) is dissected from the stomach tissue and pinned onto a Sylgard®-coated petri dish. The STG is desheathed to allow an intracellular electrode access to the cell bodies. Vaseline wells are made around the nerves that will be recorded extracellularly. To obtain a recording of the activity of each cell in the network, nerves are recorded extracellularly and 1-2 cells are recorded intracellularly. The descending inputs from the modulatory ganglia are blocked with local application of M Tetrodotoxin (TTX) and Sucrose. The Pyloric Dilator cell (PD) is injected with a sine wave of current using a current clamp protocol and a wave function generator. The frequency of the injected current ranges from 0.01 Hz to 3 Hz and the amplitude ranges from ± 0.1 nA to ± 5 nA. The dynamical complexity of the STG is quantified by performing Principal Components Analysis (PCA). This method picks out and ranks the key players in transforming an input into a target function. It also produces basis functions that can be used to generate a set of possible output functions. The stomatogastric ganglion (STG) of the Jonah crab is a wonderful system for these studies.  small, highly connected, neural network  contains approximately 30 neurons, most of which are motor neurons that control the rhythmic contractions of the stomach muscles which allow the animal to chew and filter its food  the STG can easily be isolated from the rest of the crustacean nervous system  the activity of most of its neurons can be recorded simultaneously Target Function Actual Output These data are put through Principal Components Analysis (PCA) and a set of basis functions and a set of coefficients are calculated. A linear sum of the weighted network outputs is calculated in order to produce the best match to a Target Function. The dynamic range of a network’s outputs is enhanced by the neurons that display phase shifted activity and by the neurons that have higher frequency activity. However, there is a tendency for the higher frequency outputs to be noisier. A balance between complexity and reliability needs to be struck. This is likely to be what differentiates a modeled neural network from a biological neural network. This work can be extended to examine the role of neuromodulators in expanding or contracting the set of possible outputs. In the context of the biological functionality of the STG, quantifying the dynamic range of this network will provide insights into how neurons are able to switch between two ongoing rhythms in the network. 1.5 Hz current injection 0.1 Hz current injection Upon injecting a PD cell with sinusoidal current, many of the other cells in the network respond to the stimulus. Due to the nonlinear interactions in the network, the output activity each cell produces is more complex than the input. The voltage traces display two interesting features of complex network activity. Some of the firing patterns are phase shifted relative to the input. Some of the firing patterns contain higher frequency components.