A typical neuron receives synaptic signals from thousands of upstream neurons. One of a neurons most important functions is to integrate these signals into a single output. Different neuron types demonstrates remarkable specificity and diversity in integration of synaptic signals, suggesting that the details of synaptic integration are critical to circuit computations and ultimately to behavior. Yet, we know very little about the molecular mechanisms that determine these rules, or about how these rules influence behaviour or disease. Our research addresses these issues by combining in vitro and in vivo electrophysiology, optogenetics, transgenic and knockout mice, viral manipulations and computational modeling. Much of our current work is focused on two areas.

Organization and tuning of synaptic information processing underlying spatial memory
How do responses to synaptic input determine neural computations important for navigation? How are these mechanisms tuned to information encoded? To begin to address these questions we focused on neurons in the medial entorhinal cortex, which are thought to be important for encoding an animal’s location through grid like firing fields. We demonstrated that synaptic integration by stellate neurons in layer II is tuned according to the resolution of grid like firing fields (Garden et al., 2008). Our data suggests that this tuning is accounted for by a corresponding organization of leak potassium and HCN channels. HCN1 channels are likely to be of particular importance because of their dominant role in setting the integrative properties of layer II neurons (Dudman and Nolan, 2009; Nolan et al., 2007). These observations raise many new questions. For example, what are the molecular mechanisms that configure responses to synaptic input? Is tuning static or is it controlled by activity? We are now addressing these questions by combining physiological approaches with computational modeling, molecular and optogenetic tools.

Neuron type specific roles of HCN1 channels in motor behaviors
Neuron types are distinguished by different expression of ion channels, but how does this diversity contribute to neural computations that underlie behavior? To address this question we are dissection the behavioral and physiological roles of the HCN1 channel in particular cell types. Our previous work showed that global deletion of HCN1 impairs motor learning. To distinguish the contributions of HCN1 in different neuron types to this impairment we are generating and characterizing mice with restricted deletion of HCN1. Cell type specific deletion of HCN1 from neuron types important for motor behavior is achieved using the cre-lox system or using viral manipulations. We then use behavioural, in vivo and in vitro electrophysiological analysis to determine the consequences of each manipulation.

Please see also: http://nolanlab.mvm.ed.ac.uk/

5 Selected Publications

Pastoll H., Solanka L., van Rossum MCW., Nolan MF. (2013) Feedback inhibition enables theta-nested gamma oscillations and grid firing fields. Neuron., 77 (1) : 141-54.

Zonta B., Desmazieres A., Rinaldi A., Tait S., Sherman DL., Nolan MF., Brophy PJ.(2011) A critical role for neurofascin in regulating action potential initiation through maintenance of the axon initial segment. Neuron., 69 (5) : 945-56.

Cannon RC., O´Donnell C., Nolan MF. (2010) Stochastic ion channel gating in dendritic neurons: morphology dependence and probabilistic synaptic activation of dendritic spikes. PLoS Comput. Biol., 6( 8) : e1000886.

Dudman JT., Nolan MF. (2009) Stochastically gating ion channels enable patterned spike firing through activity-dependent modulation of spike probability. PLoS Comput. Biol., 5 (2) : e1000290.

Garden, DLF., Dodson PD., O’Donnell C., White MD., Nolan MF. (2008) Tuning of synaptic integration in the medial entorhinal cortex to the organization of grid cell firing fields. Neuron., 60 (5) : 875-89.

• Neural crcuit mechanisms for computation
• Ion channel functions
• In vitro and in vivo electrophysiology
• Computational modeling