Network dynamics and neuronal homeostasis
We investigate how neural circuits achieve and maintain robust function. Collectively known as neuronal homeostastic processes, a whole collection of cellular mechanisms exist that control neural excitability in an activity-dependent manner. Together they are hypothesised to keep neurons, and networks of neurons, within a regime where normal function is best supported. Importantly, evidence from studies investigating the genetic factors of a range of neuropsychiatric conditions such as epilepsy and autism spectrum disorders indicates that faulty or disrupted homeostatic signalling is a main cause of brain dysfunction. Currently, in particular the links between the cellular and systems levels are poorly understood, a gap we attempt to address using computational modelling and data analysis.
Sloppiness in neuronal networks
Long term recordings from intact brains suggest that neurons are not stable, but subject to ongoing plasticity. This can be seen on different levels, for instance changes in spine sizes, or fluctuations in average activity levels. We observed the same phenomenon in high-density recordings from cell cultures, where individual neurons could change their activity and correlation with other neurons over the course of days.
This seems at odds with the basic postulate of neural homeostasis, which states that neurons are kept close to set-point activity levels. It also raises the question for stable function is maintained in presence of such changes.
We found that the effects of single neurons on the activity generated by a whole network is highly anisotropic, with some ‘stiff’ neurons providing a stable backbone, around which fluctuations can occur in ‘sloppy’ neurons that have only a minor effect on the overall population activity. In terms of a parametric statistical model of population activity, this corresponds to a separation of parameter directions with high likelihood curvature, the stiff dimensions, and sloppy directions where the likelihood is essentially flat. Moreover, fluctuations over time occurred preferentially along sloppy directions, hence essential functional network properties were preserved over time.
This analysis was inspired by work in James Sethna’s group suggesting that sloppiness is a generic feature of complex, coupled non-linear systems.
D. Panas, H. Amin, A. Maccione, O. Muthmann, M. van Rossum, L. Berdondini, M.H. Hennig (2015). Sloppiness in spontaneously active neuronal networks. J Neurosci, 35(22): 8480-8492.
Hoemostasis mediated by a diffusive messenger
The investigation of neuronal plasticity and homeostasis is typically focused on cell-autonomous processes. It is however well known that diffusive messengers are released in an activity-dependent manner, and affect neural excitability. Here we investigated the differences between cell-intrinsic and diffusive messengers in network models, focusing on data obtained on the effects of nitric oxide.
We found that a diffusive messenger that signals average activity levels across a network is capable of maintaining stability in a balanced network, similar to a purely local process. In addition, the diffusive signal allows individual neurons to deviate from a homeostatic set-point, which is not possible with a local mechanism unless homeostatic set-points are heterogeneous. We show that the resulting networks have properties that qualitatively differ from networks with imposed heterogeneity, which may yield computational advantages.
Y. Sweeney, J. Hellgren Kotaleski, M.H. Hennig (2015). A diffusive homeostatic signal maintains neural heterogeneity and responsiveness in cortical networks. PLoS Comput Biol 11(7): e1004389. doi:10.1371/journal.pcbi.1004389.
Hoemostasis in the developing retina
Retinal waves are spontaneously occurring activity patterns that help to prepare and pattern the visual system. Specific features of the waves are thought to play an instructive role in this process, and therefore have to be maintained as the retinal network matures. We analysed retinal waves recorded in vitro in presence of drugs known to systematically change the appearance of waves. Consistent with the presence of a homeostatic process, we found the network compensates for the intervention. Interestingly, the compensation we found was not a simple return to original activity levels, but a network-wide network-wide restoration of the variability of the waves. This demonstrate that homeostatic processes are capable of maintaining relevant network states.
M.H. Hennig, J. Grady, J. van Coppenhagen and E. Sernagor (2011). Age-dependent homeostatic plasticity of GABAergic signaling in developing retinal networks. Journal of Neuroscience, 31(34): 12159-12164.
High density microelectrde array recordings
In collaboration with the labs of Evelyne Sernagor and Luca Berdondini, we analyse data obtained with the 4096 channel APS multielectrode array. These arrays allow the characterisation of neural network activity in unprecedented detail. We develop tools for data analysis, and conduct experiments to study homeostasis in cultured networks and the developing retina, and light responses in the developing and mature retina.
Spike counts recorded with the APS MEA during 20 minutes in cultured neurons under normal conditions (left), and in presence of the glutamate receptor antagonist CNQX (middle: acute; right: next day). Each pixel represents activity recorded on one MEA channel, which typically originates from a single neuron.
For more information, see the Herding Spikes Project site..
G. Hilgen, M. Sorbaro, S. Pirmoradian, J.-O. Muthmann, I. Kepiro, S. Ullo, C. Juarez Ramirez, A. Maccione, L. Berdondini, V. Murino, D. Sona, F. Cella Zanacchi, U. Bhalla, E. Sernagor, M.H. Hennig (2016). Unsupervised spike sorting for large scale, high density multielectrode arrays. bioRxiv doi: http://dx.doi.org/10.1101/048645.
J.-O. Muthmann, H. Amin, E. Sernagor, A. Maccione, D. Panas, L. Berdondini, U.S. Bhalla, M.H. Hennig MH (2015). Spike detection for large neural populations using high density multielectrode arrays. Front. Neuroinform. 9:28. doi: 10.3389/fninf.2015.00028.
Retinal waves are a fascinating example of spontaneous network activity, which is found in developing neural circuits all over the brain. They consist of intrinsically generated propagating activity patterns, which disappear once the retinal circuits are ready for normal vision. Retinal waves have been found in many vertebrate species, and are thought to play an important role in guiding the neural development of the retina and the higher visual system. In collaboration with Evelyne Sernagor and Stephen Eglen, we study the regulation and developmental maturation of retinal waves, as well as the effects of their manipulation on retinal development. Using a combination of modelling and experiment, we found that the network producing early-stage cholinergic waves is exhibits dynamics close to a transition between local and global functional connectedness, and that this network state is under homeostatic control.
Twelve consecutive retinal waves recorded with the APS MEA in a P4 mouse retina, in presence of 6mM potassium in the solution. Waves travel from dark to light colours.
A. Maccione, M.H. Hennig, M. Gandolfo, O. Muthmann, J. van Copenhagen, S.J. Eglen, L. Berdondini and E. Sernagor (2014). Following the Ontogeny of Retinal Waves: Pan-Retinal Recordings of Population Dynamics in the Neonatal Mouse. Journal of Physiology, 592, 1545-1563.
M.H. Hennig, C.Adams, D. Willshaw and E. Sernagor (2009). Early-stage waves in the retinal network emerge close to a critical state transition between local and global functional connectivity. Journal of Neuroscience, 29:1077-1086.
Auditory brainstem physiology and function
The auditory pathway of the brainstem consists of several nuclei each specialised in extracting sound location or other relevant features from the acoustic environment. In collaboration with Ian Forsythe (Leicester), Bruce Graham (Stirling) and Conny Kopp-Scheinpflug (Munich), we investigate the function and physiology of neurons in this system. The calyx of Held, a giant glutamatergic nerve terminal in te MNTB, serves as a model for excitatory synaptic transmission (see also a summary on Bruce’s website). Neural excitability, synaptic integration and homeostasis are investigated in the MNTB, LSO and SPN. This work involes the development of detailed biophysical models, which are directly constrained by data from slice recordings.
C. Kopp-Scheinpflug, A.J.B. Tozer, S.W. Robinson, B.L. Tempel, M.H. Hennig and I.D. Forsythe (2011). The Sound of Silence: ionic mechanisms encoding sound termination. Neuron, 71, 911-925.
M.H. Hennig, M. Postlethwaite, I.D. Forsythe and B.P. Graham (2008). Interactions between multiple sources of short term plasticity during evoked and spontaneous activity at the rat calyx of Held. Journal of Physiology, 13: 3129-3146.