Also, please download RTHybrid modules for RTXI from https://github.com/GNB-UAM/rthybrid-for-rtxi and install them following the instructions.
We previously created a conductance-based computational model of mouse thoracic postganglionic neurons. In the present study, we have expanded the single-cell model into a network model with synaptic inputs based on whole-cell recordings. We systematically varied the average firing rate of a network of stochastically firing preganglionic neurons and measured the resultant firing rate in simulated postganglionic neurons. Synaptic gain was defined as the ratio of postganglionic to preganglionic firing rate.
We found that for a network configuration that mimics the typical arrangement in mouse, low presynaptic firing rates (<0.1Hz) resulted a synaptic gain close to 1, while firing rates closer to 1Hz resulted in a synaptic gain of 2.5. Synaptic gain diminished for firing rates higher than ~3Hz. We also determined that synaptic gain linearly increases with the number of secondary synaptic inputs (n) within the range of physiologically realistic presynaptic firing rate. Amplitude of secondary inputs also determines frequency-dependent synaptic gain, with a bifurcation where secondary synaptic amplitude equals recruitment threshold. We further demonstrate that the synaptic gain phenomenon depends on the preservation of passive membrane properties as determined by whole-cell recordings.
One major biological role of the sympathetic nervous system is the regulation of vascular tone in both skeletal muscle and cutaneous structures. The firing rate of muscle vasoconstrictor preganglionic neurons is modulated by the cardiac cycle, while cutaneous vasoconstrictor neurons fire independently of the cardiac cycle. We modulated preganglionic firing rate according to the typical mouse heart rate to determine if cardiac rhythmicity changes the overall firing rate of postganglionic neurons. Cardiac rhythmicity does not appear to have a significant impact on synaptic gain within the physiological range of preganglionic input.
Under normal physiological conditions, the unity gain of sympathetic neurons would lead to faithful transmission of central signals to peripheral targets. However, during episodes of high sympathetic activation, the postganglionic network can amplify central signals in a frequency-dependent manner. These results suggest that postganglionic neurons play a more active role in shaping sympathetic activity than previously thought.
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The brain is a network system in which excitatory and inhibitory neurons keep the activity balanced in the highly non-uniform connectivity pattern of the microconnectome. It is well known that the relative percentage of inhibitory neurons is much smaller than excitatory neurons. So, in general, how the inhibitory neurons can keep the balance with the surrounding excitatory neurons is an important question.
This study simultaneously recorded electric signals from ~1000 neurons from seven acute brain slices of mice with a MEA (multi-electrode array) to analyze the network architectures of cortical neurons. Subsequently, we analyzed the spike data to reconstruct the causal interaction networks between the neurons from their spiking activities. The utilized analysis mainly consists of the following four steps: first, transfer entropy was adopted from previous research to reconstruct the neural network. Briefly, transfer entropy quantifies the amount of information transferred between neurons and is suitable for the effective connectivity analysis of neural networks. This allowed to elucidate the Microconnectome and the comprehensive and quantitative characteristics of interaction networks among neurons. Second, our study distinguishes between excitatory synapses and inhibitory synapses using a newly developed method called sorted local transfer entropy. Third, we also applied methods from graph theory to evaluate the network architecture. Especially, we observed that the precedence in centrality and controlling ability of inhibitory neurons. The centrality was quantified with K-core centrality, and the controlling ability was quantified with the ratio of nodes included in FVSs (Feedback Vertex Sets). Fourth, we stained acute brain slices and gave layer labels to individual neurons. Further detail will be shown in [1].
As the result, we found that inhibitory neurons, locating highly central and having strong controlling ability of other neurons, mainly locate in deep cortical layers by comparing with distribution of neurons coloured by NeuN immunostaining data. Preceding the observation, we also found that inhibitory neurons show higher firing rate than excitatory neurons, and that their firing rate also closely obey a log-normal distribution as previously known about excitatory neurons. Additionally, their connectivity strengths also obeyed a log-normal distribution.
Acknowledgements: This study was supported by several MEXT fundings (19H05215, 17K19456) and Leading Initiative for Excellent Young Researchers (LEADER) program, and grants from the Uehara Memorial Foundation.
References
1. Kajiwara M, Nomura R, Goetze F, Akutsu T, Shimono M. Inhibitory neurons are a Central Controlling regulator in the effective cortical microconnectome. bioRxiv. 2020.
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Poster link: https://prezi.com/view/TcBGr7hdgut8rgw7blAQ
It is widely accepted that humans have limited cognitive resources and that these finite resources impose restrictions on what the brain can compute. Although endowed with limited computational power, humans are still presented daily with decisions that require solving complex problems. This raises a tension between computational capacity and the computational requirements of solving a problem. In order to understand how hardness of problems affect problem-solving ability we propose a measure to quantify the difficulty of problems for humans. For this we make use of computational complexity theory, a widely studied theory used to quantify the hardness of problems for electronic computers. It has been proposed that computational complexity theory can be applied to humans, but it remains an open empirical question whether this is the case.
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Mikko Lehtimäki, Marja-Leena Linne, Lassi Paunonen
We studied a highly visual marsupial, the Tammar wallaby (Macropus Eugenii), which represents a phylogenetically distinct branch of mammals for which the orientation map structure is unknown. The topography of RCC’s in wallabies is very similar to cats and primates. They have a high density of RGC in the retinal specialization, indicated by a high CP ratio of 20. If orientation columns are the mammalian norm and if species with high CP ratios have OS maps, we would predict the existence of orientation columns in wallaby cortex. We used intrinsic optical imaging and multi-channel electrophysiology methods to examine the functional organization of the wallaby cortex. We found robust OS in a high proportion of cells in the primary visual cortex and clear orientation columns similar to those found in primates and cats but with bias towards vertical and horizontal preferences, suggesting lifestyle-driven variations. The findings suggest that orientation columns are the norm and it might be that the rodents and rabbits are unusual in terms of mammalian cortical architecture.
Adam JH Newton, Craig Kelley, Michael L Hines, William W Lytton, Robert A McDougal
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Rodrigo Pena will be leading the discussion of this poster.
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Abstract:
Finely-timed spike relationships provide knowledge of putative monosynaptic connections in populations of neurons. Recent experiments involving hippocampal in vivo recordings were able to demonstrate such a relationship by means of the cross-correlation function (CCF) [1,2]. A sharp peak within a few milliseconds in the CCF indicates the presence of a connection. Yet, neurons that are not monosynaptically connected can emit spikes within some short temporal distance as a result of network co-modulation [3], usually in the form of background noise. In general, there is an agreement that CCFs are shaped by either the connectivity, synaptic properties, or background activity [4]. However, it remains unclear whether and how the postsynaptic intrinsic neuronal properties such as the ionic currents’ nonlinearities and time constants shape the CCFs between pre- and postsynaptic neurons. The presence of presynaptic-dependent postsynaptic signatures may serve to differentiate between correlation and causation.
We address these issues by combining biophysical modeling, numerical simulations and dynamical systems tools. We extend the framework developed in [5] to describe an ultra-precise monosynaptic connection by including ionic currents with representative dynamics. The model consists of two neurons receiving uncorrelated noise where the presynaptic neuron sends a fixed number of synaptic events to the postsynaptic neuron. CCF is computed as an average over a number of trials. We consider a number of scenarios corresponding to different levels of the ionic currents, their nonlinearities and effective time constants.
Our results show the emergence of an additional slower and wider temporal relationship, after the sharp peak in the CCF. This relationship depends on the dynamic properties present in the postsynaptic neuron model (ionic curretns) in the subthreshold regime. Upon a synaptic event, if the neuron is not on the verge of a spike, it will increase its voltage following some dynamics, which depends particularly on the effective time constant, and which will be reflected in the CCF. This temporal relationship may not be clearly observed in experiments due to a high signal-to-noise ratio and is not capturing external modulation effects. We explain this effect using a phase-plane description where we capture the spike-initiation nonlinearity in terms of nullclines and connect it to the CCF.
We expect that these results will help the identification of monosynaptic connections between different neuron types, in particular, those connections among neurons from different classes.
Funding Acknowledgment
This work was supported by the National Science Foundation grant DMS-1608077 (HGR).
References
[1] English, D. F., McKenzie, S., Evans, T., Kim, K., Yoon, E., and Buzsáki, G. (2017). Pyramidal cell-interneuron circuit architecture and dynamics in hippocampal networks. Neuron, 96, 505-520.
[2] Constantinidis, C., and Goldman-Rakic, P.S. (2002). Correlated discharges among putative pyramidal neurons and interneurons in the primate prefrontal cortex. J. Neurophysiol. 88, 3487–3497.
[3] Yu, J., and Ferster, D. (2013). Functional coupling from simple to complex cells in the visually driven cortical circuit. J. Neurosci., 33, 18855-18866.
[4] Ostojic, S., Brunel, N., & Hakim, V. (2009). How connectivity, background activity, and synaptic properties shape the cross-correlation between spike trains. J. Neurosci, 29, 10234-10253.
[5] Platkiewicz, J., Saccomano, Z., McKenzie, S., English, D., and Amarasingham, A. (2019). Monosynaptic inference via finely-timed spikes. arXiv preprint arXiv:1909.08553.
Topic: P165: Using dynamical mean field theory to study synchronization in the brain.
Time: Jul 20, 2020 07:00 PM Amsterdam, Berlin, Rome, Stockholm, Vienna
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Using Dynamical Mean Field Theory (DMFT) to study state transitions in the brain
This work focuses on the dynamics of large networks of neurons, and particularly aims to study the effects of brain structures and functions on state transitions, such as those found in epilepsy.
