A neural network simulation of simultaneous single-unit activity recorded from the dragonfly ganglia

Techniques are described that allow the use of multiple neuron spike data in a computational neural network architecture. The network architecture was devised to match the number of actual neurons from which data were obtained. The network was successfully trained to accurately predict the multiple neuron spike trains. Simultaneous spike histories of 44 neurons were modeled by a network architecture consisting of 44 input units, 88 hidden units with recurrent connections and 44 output units. The activation function of each unit was determined by data unique to a single neuron. These data were coupled with an analog gradient that preserved both the exact spiking times and the relative spiking tendency of each neuron. The input activation values were compared to network output target values calculated to occur 5 msec forward in the composite spiking records of all neurons. Following 2000 training cycles with the gradient data, the average error of each unit in the network was 0.0016. Discrete output values for each network unit were correlated with those of all other units. These correlations were comparable to those done using the actual neuron data. Both correlations reveal a functional connectivity pattern among the units and neurons. These connectivity patterns indicate that the networks may synthesize patterns of activity needed for biological function; in this case, flight patterns carried out in the mesothoracic ganglion of the dragonfly. This model represents, to the best of our knowledge, the first computer based network simulation using actual experimental neural data obtained from a large number of spontaneously active cells in a small intact ganglion.