Memory capacity of a random, recurrently connected network of neurons with multiple, biologically realistic facilitation and adaptation profiles
© DePasquale and Fusi; licensee BioMed Central Ltd. 2011
Published: 18 July 2011
We developed a model of linear, integrate-and-fire neurons endowed with realistic firing rate facilitation and adaptation profiles (Figure 1B, green) based on parameters obtained from rodent cortical slice electrophysiology data . The equations of dynamics of each model neuron contained facilitating and adapting currents, proportional to the intracellular concentration of different ionic species, which were modulated by each neuron’s spiking history. The adapting input network projected in a feed-forward manner through a high dimensional, recurrently connected network of spiking neurons whose activity was then projected to a linear readout, firing-rate neuron. We sought to inspect the recurrently connected network’s capacity for memory by injecting a time-varying “input signal” current into the adapting network (Fig. 1B, red) and training the weights of the linear readout neuron so that its firing rate matched a teaching signal provided to the neuron; the teaching signal was a specified transformation of the input signal current to the adapting input network (Fig. 1B, blue).
This work was supported by the Gatsby Foundation, the Kavli Foundation, DARPA SyNAPSE and the NSF Graduate Research Fellow Program.
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