2nd German-Korean Workshop 2010, Berlin, Germany
The output of single cortical neurons as recorded in the living brain shows high response variability across experimental repetitions (e.g. Shadlen & Newsome, 1998). Yet, the cortex is able to process sensory information with an intriguing temporal fidelity and behavioral responses are timed with high accuracy in each instance. To solve this apparent contradiction we chose combined experimental and theoretical approaches to investigate different neuron-intrinsic and neuron-extrinsic sources of cortical variability. (1) Somatic noise current injection in vitro (Nawrot et al., 2008) allowed us to quantify single neuron output variability (Fano factor) for balanced excitatory and inhibitory input under stationary input conditions. (2) Analysis of single unit recordings from the motor cortex of behaving monkeys allowed us to quantitatively estimate the excess variability in vivo (Nawrot et al., 2008; Rickert et al., 2009). (3) Observation of large scale brain signals such as human epicortical field potentials (Mehring et al., 2004) were used to monitor spontaneous network dynamics on global spatial and temporal scales. (4) Specific neuron-intrinsic mechanisms that are responsible for the phenomenon of spike frequency adaptation (SFA) contribute to a reliable (i.e. less variable) encoding of sensory information in response to a new stimulus (Farkhooi et al., 2009).
I will argue that neuron-intrinsic sources of variability are mostly negligible (Nawrot et al., 2009). About one half of the observed single neuron variability in vivo can be explained by the stochastic nature of the balanced input and of synaptic transmission, while the second half may be attributed to global ongoing activity dynamics in the cortical network. Our results imply that the Poisson point process is a deficient model for the description of spike train statistics of real cortical neurons. Under stationary conditions, real neurons are more regular and less variable. Non-renewal properties due to intrinsic mechanisms of adaptation further increase their response reliability under transient input conditions.
Farkhooi F, Strube M, Nawrot MP (2009) Serial correlation in neural spike trains: experimental evidence, stochastic modelling, and single neuron variability. Physical Review E 79: 021905
Mehring C, Nawrot MP, Cardoso de Oliveira S, Vaadia E, Schulze-Bonhage A, Aertsen A, Ball T (2004) Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex. J Physiol Paris 98: 498
Nawrot MP, Boucsein C, Rodriguez-Molina V, Riehle A, Aertsen A, Rotter S (2008) Measurement of variability dynamics in cortical spike trains. J Neurosci Meth 169: 374-390
Nawrot MP, Schnepel P, Aertsen A and Boucsein C (2009) Precisely timed signal transmission in neocortical networks with reliable intermediate-range projections. Frontiers in Neural Circuits 3:1
Rickert J, Riehle A, Aertsen A, Rotter S, Nawrot MP (2009) Dynamic encoding of movement direction in motor cortical neurons. Journal of Neuroscience 29: 13870-13882
Shadlen & Newsome (1998) The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding. J Neurosci 18:3870