A systems approach to investigating the roles of cellular mechanisms for tuning of neural computation in the entorhinal cortex

Lead Research Organisation: University of Edinburgh
Department Name: Biomedical Sciences


One of the most challenging problems in science is to understand how the molecules expressed by nerve cells in the brain enable thoughts and actions to take place. This is of fundamental importance, both for academic understanding of how brains work, and for industries aiming to develop therapies that treat neurological and psychiatric disorders. Synthesis of molecules and assembly of cells is similar in the brain and other organs of the body, but the brain is distinguished by its ability to perform computations of considerable complexity. These computations rely upon electrical signals generated by ion channels found in the membrane of nerve cells. Membrane ion channels are the critical molecular link between gene expression and electrical signaling. Computational models that explicitly account for a neuron's membrane ion channels will enable direct links to be established between gene expression, electrical signaling and brain function. We propose to develop quantitative and predictive models that will ultimately account for how gene expression determines the computations carried out in the brain. We will focus on a sub-region of the brain called the entorhinal cortex. This region is organized in a way that makes it a very attractive model. During exploration, nerve cells at the upper end of this region encode an animal's location at a relatively high resolution of approximately 30 cm. Nerve cells located progressively lower down within this region also encode an animals location, but at progressively lower resolution. Importantly, the electrical signals generated when inputs to these nerve cells are activated follow a similar organization. In the upper part of the entorhinal cortex, the electrical signals are very brief. At progressively lower locations, the duration of these signals increases. This organization of electrical signals is probably dues to differences between the ion channels found in the membrane of nerve cells at different locations. We will first develop simple computer models nerve cells in the entorhinal cortex. We will then incorporate into these models data about the organization of ion channels in different nerve cells along the top-to-bottom axis of the medial entorhinal cortex. We will use these models to predict how the neurons will respond to signals that can be used for simple computations, and what happens to these responses if specific ion channel molecules are absent. We will then record from real nerve cells and study their responses to equivalent input signals. These experiments will be repeated on nerve cells in which specific ion channel molecules, or the genes that encode them, have been selectively blocked. By comparison of the experimental results with the model predictions we will be able to refine and improve the predictive power of the models, while also identifying functions that the model may not yet explain and that will therefore require further investigation. Finally, we will use the validated model to predict the roles of specific ion channel molecules in encoding of an animals location at different spatial resolutions. The models and experimental results generated by this study will be of benefit and application in several areas. 1) By establishing basic links between genes, electrical signaling and computation by nerve cells, the study will be important for understanding the healthy brain. It will form a key foundation for further investigations of how specific genes influences brain function. 2) The medial entorhinal cortex and the membrane ion channels that we will focus on are important targets for drug discovery. The computational models that we build will enable dry lab testing of potential therapeutic strategies in development by pharmaceutical or biotechnology companies. 3) The principles uncovered during the proposed work may stimulate future design of biologically based computational devices.

Technical Summary

This study proposes to address how molecules expressed by individual neurons enable abstract computations that underlie cognition and behaviour. We will focus on a sub-region of the brain, the medial entorhinal cortex (MEC), which has a functional topography that makes it an attractive model for investigation of how cellular properties determine neuronal computations and ultimately behaviour. Neurons along the dorsal to ventral axis of layer II of the MEC encode an animal's spatial location, but the spatial scale used becomes progressively larger for progressively more ventral neurons. Differences between layer II neurons in their membrane ion channels cause their responses to synaptic input to follow a dorsal-ventral organization that maps onto the organization of spatial coding. The main objective of the proposed work is to develop and test biophysically constrained computational models of stellate neurons found in layer II of the MEC. These models will be used to evaluate and predict the roles of specific ion channels expressed by stellate neurons in tuning of computations at different locations along the dorsal-ventral axis of the MEC. Patch clamp recordings from neurons in brain slices will be used to test the model predictions. Molecular and genetic approaches will be used to manipulate HCN and leak K+ channels expressed by neurons and the consequences for synaptic computation will be compared with model predictions. Virus based tools for manipulation of these channels will be evaluated. The model will be further refined based on results of these experiments. The models will then be used to predict consequences of molecular manipulations for encoding of location at different spatial scales by neurons in the MEC. The proposed work will direct new understanding of relationships between gene expression and the electrical signaling events that underlie neural computation.

Planned Impact

Users and beneficiaries of the proposed work are in several areas of importance to long-term UK economic growth, heath and well being. 1) The proposed project will contribute to UK capacity building in systems biology. This has been identified by the BBSRC as a strategic priority of long-term benefit to the UK. The proposed work will provide training for the postdoctoral research associate employed to work on the project and for PhD, Masters and undergraduate students who will have the opportunity to work on computational models and experimental systems that develop from the project. The University of Edinburgh is particularly well placed for the project to contribute to postgraduate training, with several successful PhD and Masters programs, both within the host School (Biomedical Sciences) and within the School of Informatics. 2) A major beneficiary outside the academic community will be the commercial private sector, in particular pharmaceutical and biotechnology companies. The predictive tools developed by the proposed project will be of direct use for drug discovery and drug safety evaluation. For example, the study will focus on layer II neurons in the entorhinal cortex, which are a major target for investigation of disorders such as Alzheimer's and epilepsy. The HCN and K2P channels that the project focusses on are also important targets for drug discovery and safety evaluation. 3) As systems biology tools become more powerful it is likely that industrial need for computational analysis will be met by independent service companies as well as by in house contractors. The principal applicant already has close collaborative links with one such start up company (Textensor, Edinburgh, UK) and the tools and approaches developed here are likely to be important for growth of this area. 4) The proposed project will also benefit the wider general public, both through the products of commercial ventures discussed above, and by increased understanding and awareness of mechanisms of cognition. The proposed project includes plans for communication and engagement that take advantage of the strong infrastructure in place at the University of Edinburgh. Engagement in capacity building will primarily be through the framework of the University of Edinburgh undergraduate and postgraduate training programs. Communication and engagement with drug discovery and computational service industries will be both through conventional channels, such as publication and conferences, and by taking advantage of the University of Edinburgh commercial liaison unit, Edinburgh Research and Innovation (ERI). The ERI publicity team will also provide resources for communication with the media and general public. The principal applicant's website will carry details of the project in a form accessible to a wide audience. The proposed work will generate experimental data, experimental tools and computational models that will be suitable for further exploitation and application. Tools and models will be deposited in publicly accessible repositories (see Data Sharing statement). Opportunities for further exploitation come from the potential to apply the models and expertise to areas of commercial interest, such as drug discovery. These will be evaluated by the Principal Applicant during the project and at the end of the grant life-cycle. When significant opportunities arise they will be exploited using resources provided by ERI, which has extensive experience of working with external organisations to develop commercial opportunities arising from new and innovative developments in academic research within the University of Edinburgh. ERI has established a co-ordinated strategy to protect intellectual property and determine the most appropriate commercialisation route for discoveries on all projects.


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Gonzalez-Sulser A (2014) GABAergic projections from the medial septum selectively inhibit interneurons in the medial entorhinal cortex. in The Journal of neuroscience : the official journal of the Society for Neuroscience

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O'Donnell C (2011) Dendritic spine dynamics regulate the long-term stability of synaptic plasticity. in The Journal of neuroscience : the official journal of the Society for Neuroscience

Description The project first addressed the relationship between molecules expressed by nerve cells in the MEC, the electrical properties of these cells, and their ability to compute location. In doing so we took advantage of a previously established anatomical organisation, in which nerve cells found at one end of the MEC represent location at high resolution and respond rapidly to synaptic inputs. Nerve cells found at increasing distances from this end of the MEC represent space at a lower resolution and respond more slowly to synaptic input. We carried out experiments to establish in more detail the organisation of the electrical properties of nerve cells at different locations in the MEC. We used the resulting data to develop computational models that account for electrical properties of these nerve cells in terms of the molecules that they express. Using these models we asked if molecular differences between neurons at different locations account for differences in the resolution of their spatial firing. Our results argued against previous suggestions that location is computed independently by individual neurons and instead suggest the importance of interactions between synaptic connections and ion channels.

We went on to investigate how synaptic interactions in the MEC might play roles in computation of location. Like most other areas of the brain the major class of neuron in the MEC is excitatory, that is when it becomes active it tends to directly increase activity of downstream neurons that it connects too. In most brain areas nearby excitatory neurons talk directly to one another. In contrast, we found that the excitatory neurons in the MEC that are thought to be particularly important for spatial computation are only able to interact indirectly via distinct inhibitory cells. To examine functional implications of this unusual principle for organisation of connections between nerve cells we developed computational models. These models showed that this organising principle could explain how cells compute location and at the same time could also account for oscillations in the electrical activity of groups of nerve cells that are associated with spatial behaviours.
Exploitation Route Examples so far include:

1. Other research groups have already performed and published experimental tests of predictions made by models developed as part of the study.

2. Other research groups have adopted optogenetic stimulation protocols developed as part of the project.

3. We have developed new molecular tools which we have made freely available via AddGene. These tools have since been obtained from the site by many other research groups (> 100 requests to date).
Sectors Digital/Communication/Information Technologies (including Software),Pharmaceuticals and Medical Biotechnology

URL http://nolanlab.mvm.ed.ac.uk
Description BBSRC funding committee A
Geographic Reach National 
Policy Influence Type Participation in a advisory committee
Description BBSRC funding committee A
Geographic Reach National 
Policy Influence Type Participation in a advisory committee
Description New York Academy of Sciences - Takeda Innovator Award jury member
Geographic Reach Multiple continents/international 
Policy Influence Type Participation in a advisory committee
Description Wellcome Trust funding panel
Geographic Reach National 
Policy Influence Type Participation in a advisory committee
Description Project grant (A systems approach to the cellular and molecular organization of neural circuits for representation of space)
Amount £719,445 (GBP)
Funding ID BB/L010496/1 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 01/2014 
End 12/2016
Description Wellcome Trust Investigator Award
Amount £1,589,107 (GBP)
Funding ID 200855/Z/16/Z 
Organisation Wellcome Trust 
Sector Charity/Non Profit
Country United Kingdom
Start 09/2016 
End 08/2021
Title Computational models and tools for analysis of E-I networks 
Description The model enables simulation of generation of spatial firing patterns and oscillatory activity by networks of excitatory and inhibitory neurons. Tools enable parallel simulation and analysis of many networks. 
Type Of Material Computer model/algorithm 
Year Produced 2015 
Provided To Others? Yes  
Impact Provided the first demonstration of the roles of noise and variation in network properties on spatial computations. Has contributed to an ongoing debate about the neural mechanisms for spatial computation. 
URL https://senselab.med.yale.edu/modeldb/showmodel.cshtml?model=183017
Title RNA sequencing data 
Description Datasets to compare expression of genes between parts of the brain that encode location at different spatial resolution. 
Type Of Material Database/Collection of data 
Year Produced 2014 
Provided To Others? Yes  
Impact This data enabled identification of molecular markers for neuronal cell types important for spatial navigation. 
URL https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63300