Stable dynamics in recurrent neural networks depend crucially on inhibition. Biological neural networks often exhibit a striking “balance” of excitation an inhibition. Moreover, inhibitory synapses are plastic and there seems to exists a plethora of different inhibitory cell types. From a modeling point of view such inhibitory complexity remains largely unexplored and its purpose elusive.
We are particularly interested in two aspects of this inhibitory complexity. First, we strive to understand the computational role of excitation-inhibition balance in recurrent and feed-forward neural networks. Second, we seek to understand which role certain interneuron types could play in computing local prediction error signals (e.g. mismatch neurons) and in solving the spatial credit assignment problem (see also deep credit assignment).
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- D’Amour, J.A., and Froemke, R.C. (2015). Inhibitory and Excitatory Spike-Timing-Dependent Plasticity in the Auditory Cortex. Neuron 86, 514–528. full-text
- Vogels, T.P., Froemke, R.C., Doyon, N., Gilson, M., Haas, J.S., Liu, R., Maffei, A., Miller, P., Wierenga, C., Woodin, M.A., Zenke, F., Sprekeler, H., 2013.
Inhibitory Synaptic Plasticity – Spike timing dependence and putative network function. Front Neural Circuits 7. doi:10.3389/fncir.2013.00119
- Vogels, T.P., Sprekeler, H., Zenke, F., Clopath, C., Gerstner, W., 2011.
Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks. Science 334, 1569–1573. doi:10.1126/science.1211095