Abstract
We provide analytical solutions for mean firing rates and cross-correlations of coincidence detector neurons in recurrent networks with excitatory or inhibitory connectivity, with rate-modulated steady-state spiking inputs. We use discrete-time finite-state Markov chains to represent network state transition probabilities, which are subsequently used to derive exact analytical solutions for mean firing rates and cross-correlations. As illustrated in several examples, the method can be used for modeling cortical microcircuits and clarifying single-neuron and population coding mechanisms. We also demonstrate that increasing firing rates do not necessarily translate into increasing cross-correlations, though our results do support the contention that firing rates and cross-correlations are likely to be coupled. Our analytical solutions underscore the complexity of the relationship between firing rates and cross-correlations.
Original language | English (US) |
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Pages (from-to) | 2637-2661 |
Number of pages | 25 |
Journal | Neural Computation |
Volume | 20 |
Issue number | 11 |
DOIs | |
State | Published - Nov 2008 |
ASJC Scopus subject areas
- Arts and Humanities (miscellaneous)
- Cognitive Neuroscience