energetic optimality; multicompartment neuron models; synaptic energy consumption; information theory; brain energy
Cullen Carlie L., Pepper Renee E., Clutterbuck Mackenzie T., Pitman Kimberley A., Oorschot Viola, Auderset Loic, Tang Alexander D., Ramm Georg, Emery Ben, Rodger Jennifer, Jolivet Renaud B., Young Kaylene M. (2021), Periaxonal and nodal plasticities modulate action potential conduction in the adult mouse brain, in Cell Reports
, 34(3), 108641-108641.
Conrad Mireille, Jolivet Renaud B. (2020), Modelling Neuromodulated Information Flow and Energetic Consumption at Thalamic Relay Synapses, in Artificial Neural Networks and Machine Learning – ICANN 202029th International Conference on Artific
, Springer International Publishing, Cham.
Kyrargyri Vasiliki, Attwell David, Jolivet Renaud Blaise, Madry Christian (2019), Analysis of Signaling Mechanisms Regulating Microglial Process Movement
, Springer New York, New York, NY.
Harris Julia Jade, Engl Elisabeth, Attwell David, Jolivet Renaud Blaise (2019), Energy-efficient information transfer at thalamocortical synapses, in PLOS Computational Biology
, 15(8), e1007226-e1007226.
Caruso Giuseppe, Caraci Filippo, Jolivet Renaud B. (2019), Pivotal role of carnosine in the modulation of brain cells activity: Multimodal mechanism of action and therapeutic potential in neurodegenerative disorders, in Progress in Neurobiology
, 175, 35-53.
Coggan Jay S., Calì Corrado, Keller Daniel, Agus Marco, Boges Daniya, Abdellah Marwan, Kare Kalpana, Lehväslaiho Heikki, Eilemann Stefan, Jolivet Renaud Blaise, Hadwiger Markus, Markram Henry, Schürmann Felix, Magistretti Pierre J. (2018), A Process for Digitizing and Simulating Biologically Realistic Oligocellular Networks Demonstrated for the Neuro-Glio-Vascular Ensemble, in Frontiers in Neuroscience
, 12, 664.
Madry Christian, Kyrargyri Vasiliki, Arancibia-Cárcamo I. Lorena, Jolivet Renaud, Kohsaka Shinichi, Bryan Robert M., Attwell David (2018), Microglial Ramification, Surveillance, and Interleukin-1β Release Are Regulated by the Two-Pore Domain K+ Channel THIK-1, in Neuron
, 97(2), 299-312.e6.
Conrad Mireille, Engl Elisabeth, Jolivet Renaud B. (2017), Energy use constrains brain information processing, in 2017 IEEE International Electron Devices Meeting (IEDM)
, San Francisco, CA, USAIEEE, San Francisco CA, USA.
|Persistent Identifier (PID)
The code developed to perform simulations within the frame of the project is being progressively deposited on our GitHub lab page (https://github.com/JolivetLab) after curation and test.
Information transmission in the brain is energetically expensive, yet has to satisfy demands of speed and signal-to-noise reliability. We have recently shown that the strong retinogeniculate synapse relaying information from the retina to the thalamus resolves these competing constraints by maximizing energetic efficiency when transferring information. In their physiological state, these synapses are not set to transmit the maximum amount of information possible: information transmission increases when larger excitatory postsynaptic currents (EPSCs) are injected into the postsynaptic thalamic neuron. However, EPSCs that are larger or smaller than physiological EPSCs decrease the information transmitted per energy used. The physiological EPSC size therefore maximizes energy efficiency rather than pure information transfer across the synapse. In other words, the retinogeniculate synapse trades information for energy savings.This finding echoes our earlier finding that, for information being transmitted at a synapse in the presence of noise, a low presynaptic release probability is the optimal solution to maximize the information transmitted per energy expended on the transmission, providing an exciting explanation for the previously not understood fact that synapses are extremely unreliable, often only releasing neurotransmitter a quarter of the times that an action potential is fired.Together, these findings suggest maximization of information transmission per energy used as a design principle in the brain. However, it is unclear how broadly this principle applies. Whether energy efficiency at excitatory synapses is a special property of strong relay synapses, or a more general principle, also governing synaptic inputs that contribute more weakly to determining the output of the postsynaptic cell is an open question. These findings also raise the question of what mechanisms are in effect in order to achieve energetic efficiency of information transfer at synapses.This project will address these questions using information theory and simulations of biologically validated neuron models. In particular, we will investigate under which conditions energetic efficiency of information transfer arises at weak synapses, what biophysical properties of neurons give rise to energetic efficiency of information transfer and how it can be modulated by local network activity. Finally, we will test the hypothesis that energetic efficiency of information transfer is an emergent property of Hebbian learning, more specifically of spike-timing dependent plasticity. In addressing these questions, this project will open a novel avenue to study neural coding by clarifying the natural physical energetic constraints the brain has to do with. This approach eventually holds the potential to identify some of the mechanisms which link energy consumption at synapses to pathologies. Additionally, this approach might open new avenues in the design of artificial computing systems where energy reduction techniques are now being introduced.