Neuronal processes underlying higher cognitive functions
We pursue the hypothesis that computations are performed in the high dimensional dynamic state space provided by the non-linear dynamics that evolve in the recurrent network of delay coupled oscillatory circuits. Core assumptions are:
i) information about statistical contingencies in natural scenes is contained in the weight distributions of recurrent connections and expressed in temporal correlations among the discharges of feature specific neurons;
ii) information conveyed by top down projections as a function of attention and expectancy is also manifest in the correlation structure of distributed neuron populations and
iii) if sensory evidence matches the predictions stored in the functional architecture of the recurrent networks and/or the expectancies conveyed by top down projections, network dynamics converge to states characterized by increased coherence (synchrony) and specific correlation patterns.
To test the predictions of this conceptual framework we investigate population dynamics with cellular resolution by performing massive parallel recordings from the visual cortex of behaviourally trained monkeys with chronically implanted microelectrode arrays.
Bányai M, Lazar A, Klein L, Klon-Lipok J, Stippinger M, Singer W, Orbán G (2019). Stimulus complexity shapes response correlations in primary visual cortex. Proc Natl Acad Sci USA 116(7), 2723-2732. https://doi.org/10.1073/pnas.1816766116
Singer W (2018). Neuronal oscillations: Unavoidable and useful? European Journal of Neuroscience 48(7), 2389-2398. https://doi.org/https://doi.org/10.1111/ejn.13796
Singer W, Lazar A (2016). Does the cerebral cortex exploit high-dimensional, non-linear dynamics for information processing? Front Comput Neurosci 10, 99. https://doi.org/10.3389/fncom.2016.00099
Singer W (2013). Cortical dynamics revisited. Trends Cogn Sci 17(12), 616-626. https://doi.org/10.1016/j.tics.2013.09.006
Uhlhaas PJ, Singer W (2006). Neural synchrony in brain disorders: Relevance for cognitive dysfunctions and pathophysiology. Neuron 52(1), 155-168. https://doi.org/10.1016/j.neuron.2006.09.020