Plasticity of inhibitory synaptic transmission in the hippocampus
Lead Research Organisation:
University of Bristol
Department Name: Physiology and Pharmacology
Abstract
The building blocks of the brain are nerves cells, also called neurons, connected to each other by synapses. Neurons form two categories: excitatory neurons promote activity in their connected neurons, whereas inhibitory neurons depress activity. Even though inhibitory neurons are less numerous than excitatory neurons (20%), they play a central role in brain function and computation. Disruption of inhibitory neurons leads to an imbalance in excitation and inhibition that can, in turn, lead to diseases such as epilepsy, schizophrenia and autism spectrum disorders. Thus, understanding what regulates the strength of excitatory and inhibitory synapses is an important research goal. Interestingly, although there has been much focus on plasticity of excitatory synapses, plasticity of inhibitory synapses has been largely neglected. In this BBSRC project, we aim to investigate the phenomenology and function of inhibitory synaptic plasticity.
The hippocampus is a brain area that plays an important role in episodic memory and spatial navigation. Neuronal network activity in the hippocampus exhibits multiple discrete states that are identified by rhythmic oscillations and perform distinct computational functions. Memories are initially encoded during exploration when theta (5-12Hz) and gamma (30-120Hz) frequency oscillations are prevalent whereas consolidation of memories occurs offline in periods of transient sharp wave ripple (~200Hz) activity during rest or sleep. Specific subtypes of inhibitory neurons have been found to be active in these distinct behavioural states and, intriguingly, inhibitory plasticity has been shown to depend on the precise co-ordination of inhibitory and excitatory neuron firing and on the state of the neural network. This indicates that different behavioural states are likely to trigger distinct forms of inhibitory plasticity at specific inhibitory synapses. However, there is very limited evidence for the role of inhibitory plasticity in the hippocampus.
Our hypotheses are that plasticity of inhibitory synapses: a) can regulate network computations, b) is specific to the inhibitory neurons subtypes and c) determines input selectivity for excitatory neurons in a behavioural state dependent manner. We propose a joint effort from an experimental team, which has expertise in synaptic plasticity in the hippocampus, with a computational team that recently proposed a theoretical model of inhibitory plasticity. By a tight interaction allowing the computational model to be informed by and to guide experiments and experiments to refine the model, we aim to investigate how neural activity engages inhibitory plasticity depending on the inhibitory neuron subtype, and how inhibitory plasticity shapes network computations in the hippocampus. This work will provide important information on the mechanisms and function of inhibitory plasticity that will ultimately improve our understanding of how inhibition may be modified in disease states potentially leading to new therapies.
The hippocampus is a brain area that plays an important role in episodic memory and spatial navigation. Neuronal network activity in the hippocampus exhibits multiple discrete states that are identified by rhythmic oscillations and perform distinct computational functions. Memories are initially encoded during exploration when theta (5-12Hz) and gamma (30-120Hz) frequency oscillations are prevalent whereas consolidation of memories occurs offline in periods of transient sharp wave ripple (~200Hz) activity during rest or sleep. Specific subtypes of inhibitory neurons have been found to be active in these distinct behavioural states and, intriguingly, inhibitory plasticity has been shown to depend on the precise co-ordination of inhibitory and excitatory neuron firing and on the state of the neural network. This indicates that different behavioural states are likely to trigger distinct forms of inhibitory plasticity at specific inhibitory synapses. However, there is very limited evidence for the role of inhibitory plasticity in the hippocampus.
Our hypotheses are that plasticity of inhibitory synapses: a) can regulate network computations, b) is specific to the inhibitory neurons subtypes and c) determines input selectivity for excitatory neurons in a behavioural state dependent manner. We propose a joint effort from an experimental team, which has expertise in synaptic plasticity in the hippocampus, with a computational team that recently proposed a theoretical model of inhibitory plasticity. By a tight interaction allowing the computational model to be informed by and to guide experiments and experiments to refine the model, we aim to investigate how neural activity engages inhibitory plasticity depending on the inhibitory neuron subtype, and how inhibitory plasticity shapes network computations in the hippocampus. This work will provide important information on the mechanisms and function of inhibitory plasticity that will ultimately improve our understanding of how inhibition may be modified in disease states potentially leading to new therapies.
Technical Summary
Inhibitory interneurons play a central role in brain function. They maintain excitatory-inhibitory balance, and generate a range of oscillatory network dynamics. Disruption to these processes by inappropriate inhibition can lead to diseases such as epilepsy and autism spectrum disorders. In the hippocampus there are multiple different interneuron subtypes that target different regions of the network. We have chosen to focus on two distinct interneuron subtypes in the CA1 region: basket cells (BCs) and oriens lacunosum moleculare (OLM) cells that inhibit the soma/proximal and distal dendrites of CA1 pyramidal cells respectively and are active within different phases of theta, gamma and sharp wave ripple oscillations.
Network computations can be learned and dynamically regulated through the process of synaptic plasticity. It is increasingly clear that the diverse array of interneuron subtypes within the hippocampus perform specific functions on network processing. Furthermore, recent evidence shows that inhibitory synaptic transmission is not static but can be dynamically regulated in an activity-dependent manner with far-reaching but currently unexplored implications for the hippocampus. Thus, we hypothesise that inhibitory plasticity will be engaged by BCs and OLM cells during different behaviours and that this will in turn dynamically regulate the preferred inputs for CA1 pyramidal cells.
Here, we will address these hypotheses using a combination of computational modelling and electrophysiological recording in hippocampal slices coupled with optogenetic techniques to stimulate and record from select subtypes of interneurons. The experimental and theoretical aspects will be interdigitated to enable theoretical predictions to be informed by experimental work and vice versa. The goal is to understand when inhibitory plasticity is induced at specific interneuron-pyramidal cell synapses and how it regulates hippocampal function.
Network computations can be learned and dynamically regulated through the process of synaptic plasticity. It is increasingly clear that the diverse array of interneuron subtypes within the hippocampus perform specific functions on network processing. Furthermore, recent evidence shows that inhibitory synaptic transmission is not static but can be dynamically regulated in an activity-dependent manner with far-reaching but currently unexplored implications for the hippocampus. Thus, we hypothesise that inhibitory plasticity will be engaged by BCs and OLM cells during different behaviours and that this will in turn dynamically regulate the preferred inputs for CA1 pyramidal cells.
Here, we will address these hypotheses using a combination of computational modelling and electrophysiological recording in hippocampal slices coupled with optogenetic techniques to stimulate and record from select subtypes of interneurons. The experimental and theoretical aspects will be interdigitated to enable theoretical predictions to be informed by experimental work and vice versa. The goal is to understand when inhibitory plasticity is induced at specific interneuron-pyramidal cell synapses and how it regulates hippocampal function.
Planned Impact
Who will benefit from the research?
As well as specific academic beneficiaries, the public (particularly school pupils and teachers) and wider academic community will benefit from the increase in knowledge about the role of inhibitory synaptic plasticity. In addition, sectors of the pharmaceutical industry working to develop effective drug therapies for neurological diseases will also benefit from the proposed work. Indirectly, and in the long term, people suffering from such diseases may also benefit. Therefore, there is the potential for beneficial impact on both the health and wealth of the UK.
How will they benefit from this research?
Enabling us to adapt to our environment is one of the most fundamental functions of the brain, a process that is believed to be underpinned by synaptic plasticity. Since memory is so integral to all our lives, gaining knowledge about the mechanisms of synaptic plasticity is of interest not only to the academic community but also to the wider public.
Pharmaceutical industry: Research into numerous neurological diseases such as schizophrenia, autism and Alzheimer's disease has found deficits in synaptic plasticity that could contribute to disease symptoms. Our close working relationship with specific pharmaceutical companies means our work is likely to enhance their understanding of the fundamental science of learning and memory, pharmacological approaches to manipulating it and putative novel drugs and targets. JRM has ongoing collaborations with Eli Lilly & co (through the Centre for Cognitive Neuroscience) to study the effects of receptor agonists on synaptic transmission in the hippocampus. JRM co-supervises CASE award studentships at Bristol University since October 2011. Through the research described in this proposal we can offer these companies academic expertise to further this goal. This is particularly important since Eli Lilly & co are developing receptor selective agonists for use in the treatment of cognitive disorders.
The social impact and economic costs of the diseases mentioned above are enormous. Therefore our work will benefit society from the advances we make in investigating mechanisms that may underlie such diseases, and will benefit the economy both in terms of costs saved in care for patients suffering from these conditions, and also in profits from pharmaceuticals developed and sold by UK-based companies. We acknowledge that these indirect benefits may take several years before they are realised.
High-tech industry: This work will benefit current and future developers of smart technologies, since the inhibitory learning rule developed for this project is likely to inspire new machine learning algorithms, new implementations in neuromorphic engineering, and new learning rules for intelligent robotics. We will make use of CC's contact, Tom Schaul, at Google Deepmind to encourage the use of our model of inhibitory plasticity in novel machine learning techniques.
Educational impact: We will train a new generation of scientists by training the staff in our laboratory and by teaching at summer schools. By teaching at Bristol and Imperial, CC and JRM will train a new generation of non-academic workers in the UK, teaching a solid skillset for working in pharmaceutical, biotechnological, or engineering companies such as high-tech companies using machine learning or robotics, but also in banks and insurances that use artificial neural network techniques.
As well as specific academic beneficiaries, the public (particularly school pupils and teachers) and wider academic community will benefit from the increase in knowledge about the role of inhibitory synaptic plasticity. In addition, sectors of the pharmaceutical industry working to develop effective drug therapies for neurological diseases will also benefit from the proposed work. Indirectly, and in the long term, people suffering from such diseases may also benefit. Therefore, there is the potential for beneficial impact on both the health and wealth of the UK.
How will they benefit from this research?
Enabling us to adapt to our environment is one of the most fundamental functions of the brain, a process that is believed to be underpinned by synaptic plasticity. Since memory is so integral to all our lives, gaining knowledge about the mechanisms of synaptic plasticity is of interest not only to the academic community but also to the wider public.
Pharmaceutical industry: Research into numerous neurological diseases such as schizophrenia, autism and Alzheimer's disease has found deficits in synaptic plasticity that could contribute to disease symptoms. Our close working relationship with specific pharmaceutical companies means our work is likely to enhance their understanding of the fundamental science of learning and memory, pharmacological approaches to manipulating it and putative novel drugs and targets. JRM has ongoing collaborations with Eli Lilly & co (through the Centre for Cognitive Neuroscience) to study the effects of receptor agonists on synaptic transmission in the hippocampus. JRM co-supervises CASE award studentships at Bristol University since October 2011. Through the research described in this proposal we can offer these companies academic expertise to further this goal. This is particularly important since Eli Lilly & co are developing receptor selective agonists for use in the treatment of cognitive disorders.
The social impact and economic costs of the diseases mentioned above are enormous. Therefore our work will benefit society from the advances we make in investigating mechanisms that may underlie such diseases, and will benefit the economy both in terms of costs saved in care for patients suffering from these conditions, and also in profits from pharmaceuticals developed and sold by UK-based companies. We acknowledge that these indirect benefits may take several years before they are realised.
High-tech industry: This work will benefit current and future developers of smart technologies, since the inhibitory learning rule developed for this project is likely to inspire new machine learning algorithms, new implementations in neuromorphic engineering, and new learning rules for intelligent robotics. We will make use of CC's contact, Tom Schaul, at Google Deepmind to encourage the use of our model of inhibitory plasticity in novel machine learning techniques.
Educational impact: We will train a new generation of scientists by training the staff in our laboratory and by teaching at summer schools. By teaching at Bristol and Imperial, CC and JRM will train a new generation of non-academic workers in the UK, teaching a solid skillset for working in pharmaceutical, biotechnological, or engineering companies such as high-tech companies using machine learning or robotics, but also in banks and insurances that use artificial neural network techniques.
Publications
Albesa-González A
(2024)
Learning with filopodia and spines: Complementary strong and weak competition lead to specialized, graded, and protected receptive fields.
in PLoS computational biology
Ang GWY
(2021)
The functional role of sequentially neuromodulated synaptic plasticity in behavioural learning.
in PLoS computational biology
Atherton LA
(2016)
Assessment of Methods for the Intracellular Blockade of GABAA Receptors.
in PloS one
Betterton RT
(2017)
Acetylcholine modulates gamma frequency oscillations in the hippocampus by activation of muscarinic M1 receptors.
in The European journal of neuroscience
Boboeva V
(2021)
Free recall scaling laws and short-term memory effects in a latching attractor network.
in Proceedings of the National Academy of Sciences of the United States of America
Boboeva V
(2024)
Unifying network model links recency and central tendency biases in working memory.
in eLife
Bono J
(2017)
Modelling plasticity in dendrites: from single cells to networks.
in Current opinion in neurobiology
Bono J
(2019)
Synaptic plasticity onto inhibitory neurons as a mechanism for ocular dominance plasticity.
in PLoS computational biology
Bono J
(2017)
Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level.
in Nature communications
Brzosko Z
(2017)
Sequential neuromodulation of Hebbian plasticity offers mechanism for effective reward-based navigation.
in eLife
Chang JC
(2024)
De novo motor learning creates structure in neural activity that shapes adaptation.
in Nature communications
Cone I
(2024)
Latent representations in hippocampal network model co-evolve with behavioral exploration of task structure
in Nature Communications
Delamare G
(2024)
Intrinsic Neural Excitability Biases Allocation and Overlap of Memory Engrams.
in The Journal of neuroscience : the official journal of the Society for Neuroscience
Delamare G
(2024)
Drift of neural ensembles driven by slow fluctuations of intrinsic excitability.
in eLife
Ebner C
(2019)
Unifying Long-Term Plasticity Rules for Excitatory Synapses by Modeling Dendrites of Cortical Pyramidal Neurons.
in Cell reports
Feulner B
(2022)
Small, correlated changes in synaptic connectivity may facilitate rapid motor learning.
in Nature communications
Feulner B
(2021)
Neural manifold under plasticity in a goal driven learning behaviour.
in PLoS computational biology
Feulner B
(2020)
Neural manifold under plasticity in a goal driven learning behaviour
Gallinaro JV
(2021)
Memories in a network with excitatory and inhibitory plasticity are encoded in the spiking irregularity.
in PLoS computational biology
Gallinaro JV
(2023)
Synaptic weights that correlate with presynaptic selectivity increase decoding performance.
in PLoS computational biology
Geiller T
(2022)
Local circuit amplification of spatial selectivity in the hippocampus.
in Nature
Griffith T
(2016)
Control of Ca2+ Influx and Calmodulin Activation by SK-Channels in Dendritic Spines.
in PLoS computational biology
Hersey M
(2022)
A tale of two transmitters: serotonin and histamine as in vivo biomarkers of chronic stress in mice.
in Journal of neuroinflammation
Hertäg L
(2022)
Prediction-error neurons in circuits with multiple neuron types: Formation, refinement, and functional implications.
in Proceedings of the National Academy of Sciences of the United States of America
Kaleb K
(2021)
Network-centered homeostasis through inhibition maintains hippocampal spatial map and cortical circuit function.
in Cell reports
Kaleb K
(2020)
Network-Centered Homeostasis Through Inhibition Maintains Hippocampal Spatial Map and Cortical Circuit Function
in SSRN Electronic Journal
Knöpfel T
(2019)
Audio-visual experience strengthens multisensory assemblies in adult mouse visual cortex.
in Nature communications
Lazic SE
(2020)
A Bayesian predictive approach for dealing with pseudoreplication.
in Scientific reports
Maes A
(2020)
Learning spatiotemporal signals using a recurrent spiking network that discretizes time.
in PLoS computational biology
Maes A
(2023)
Long- and short-term history effects in a spiking network model of statistical learning.
in Scientific reports
Maes A
(2021)
Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons.
in PLoS computational biology
| Description | We have discovered a novel form of synaptic plasticity that occurs at inhibitory synapses in the hippocampus. This plasticity occurs during learning and has important consequences. We have found that inhibitory plasticity enables memory representations to stabilise and avoid interference from newly acquired memories. This is fundamentally important to our memory systems. |
| Exploitation Route | The role of inhibitory plasticity in memory stability has important implications for how we understand memory mechanisms. This could be used by education institutions to understand how memories interact and to avoid interference between information systems. More significantly these findings provide a major new insight into the potential mechanisms by which machine learning might be implemented to more efficiently and stably learn. This will have impact in any sector where machine learning is widely used. |
| Sectors | Aerospace Defence and Marine Chemicals Construction Digital/Communication/Information Technologies (including Software) Education Electronics Energy Environment Financial Services and Management Consultancy Healthcare Manufacturing including Industrial Biotechology Pharmaceuticals and Medical Biotechnology Retail Transport |
| URL | https://www.biorxiv.org/content/10.1101/774562v1 |
| Description | our findings have the potential to be used in machine learning algorithms to optimise compter learning. This could impact on any sector that involves machine learning. |
| First Year Of Impact | 2019 |
| Sector | Digital/Communication/Information Technologies (including Software) |
| Description | BrainSight: Imaging of neural codes over the lifecourse |
| Amount | £203,000 (GBP) |
| Funding ID | BB/S019227/1 |
| Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 06/2019 |
| End | 06/2020 |
| Description | Neural adaptation to sensory stimuli by regulation of dendritic spikes and synaptic plasticity. |
| Amount | £844,820 (GBP) |
| Funding ID | BB/R002177/1 |
| Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2018 |
| End | 09/2022 |
| Description | Regulation of plateau potentials by dendritically targeted inhibitory synaptic transmission. |
| Amount | £550,000 (GBP) |
| Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 03/2021 |
| End | 03/2024 |
| Description | Hippocampal network modelling |
| Organisation | University of Exeter |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Experimental data and intellectual input |
| Collaborator Contribution | Mathemetical modelling |
| Impact | Rackham, OJL, Tsaneva-Atanasova, K, Ganesh, A, and Mellor, JR (2010). A Ca2+-based Computational Model for NMDA Receptor-Dependent Synaptic Plasticity at Individual Postsynaptic Spines in the Hippocampus. Frontiers in Synaptic Neuroscience 2, 31. Petrovic, MM, Nowacki, J, Olivo, V, Tsaneva-Atanasova, K, Randall, AD & Mellor, JR (2012). Inhibition of post-synaptic Kv7/KCNQ/M channels facilitates Long-Term Potentiation in the Hippocampus. PLoS One 7(2): e30402. Tigaret, CM, Tsaneva-Atanasova, K, Collingridge, GL & Mellor, JR (2013). Wavelet Transform-Based De-Noising for Two-Photon Imaging of Synaptic Ca2+ Transients. Biophysical Journal 104, 1006-17. |
| Start Year | 2010 |
| Description | Hippocampal network modelling with Clopath |
| Organisation | Imperial College London |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | experimental data for models. |
| Collaborator Contribution | Modelling of hippocampal network |
| Impact | Funding of BBSRC grant |
| Start Year | 2015 |
| Description | Hippocampal place cell recording |
| Organisation | University of Bristol |
| Department | School of Physiology, Pharmacology and Neuroscience |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Discovering the plasticity potential of naturally occurring spike patterns |
| Collaborator Contribution | Expertise to record place cell activity in awake behaving animals |
| Impact | Sadowski et al., Cell Reports 2016 |
| Start Year | 2010 |
| Description | Imaging calcium dynamics in vivo |
| Organisation | University of Bristol |
| Department | School of Physiology, Pharmacology and Neuroscience |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Measurement of calcium and synaptic dynamics in brain slices |
| Collaborator Contribution | Measurement of calcium and synaptic dynamics in awake animals |
| Impact | Tigaret et al., 2016 Nat Comms Tigaret et al., 2018 J Neurosci |
| Start Year | 2012 |
| Description | development of cholinergic drugs for cognitive enhancement |
| Organisation | Eli Lilly & Company Ltd |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Determination of the effects of cholinergic compounds in hippocampal function. Measurement of acetylcholine release in hippocampus and prefrontal cortex |
| Collaborator Contribution | Funding of CASE award studentships. In kind contributions of novel drugs. |
| Impact | Atherton et al., 2015 Trends in Neurosci Teles Grilo-Riovo et al,. 2017 Cell Reports Teles Grillo-Ruivo and Mellor 2013 Front in Neurosci Chamberlain et al., 2013 J Neurosci Atherton et al., 2017 PLoS ONE |
| Start Year | 2012 |
| Description | Contribution to press articles |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Contributions to media articles on the subject of memory |
| Year(s) Of Engagement Activity | 2014,2015,2016,2017,2018,2019,2020,2021,2022,2023 |
| Description | Press release |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | Press releases about our published research (Neuron 2010 and J Neurosci 2011, Nature Neurosci 2012, Cerebral Cortex 2016 and Nature Communications 2016) led to interest from a number of media outlets. Article on our reserach published in New Scientist. |
| Year(s) Of Engagement Activity | 2010,2011,2013,2015,2016,2017,2018,2019,2020,2021 |
| Description | Public lecture |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Public/other audiences |
| Results and Impact | Organised public lecture on brain imaging. |
| Year(s) Of Engagement Activity | 2015 |