Signal transmission and Information transfer in neural circuits

This project has two sides. First is the effect of dynamical properties of the neuronal populations on the transmission of signals and information, and the second explores the effect of structure and in particular, bidirectional connections on the transmission of the signals in neural circuits.

 

Ultimate: Communication in multiple frequency bands

Title speaks for itself!

 

Problem 1:

There are accumulating evidences that the modules with higher frequency take the upper hand in the networks of oscillating modules [1, 2, and see 3]. The simple rationale is that the modules with higher frequencies usually affect the other modules in “right” time, when the receivers are most “sensitive” [3]. We are exploring this in a more realistic framework when the delay in the interaction between the modules is taken into account. The results show that depending on the delay, different preferred directions can be observed for information transmission.

- In another ongoing study we have shown that in the models brain activity operated on the brain connectome, where the delays are assumed to be dependent on distance, the modular structure of the network can be reflected in the synchrony pattern. This effect is frequency dependent, i.e., global and local synchrony can be observed over different frequency, besides the fully incoherent state. This poses the possibility of the flexible pattern of functional integration in the brain networks with fixed connectivity. Also it provides an evidence for the possibility of the control of the functional network for a flexible pattern for communication through coherence [4].

- It could be possible to engage different nodes in the task dependent coherence pattern, reported in brain imaging studies [5], by changing the frequency if the delays are picked from a distribution and the response function of the nodes can be controllable.

- In most of the studies in this context, the information is supposed to be conveyed by the phase of the oscillations. It is necessary to see how the amplitude of the oscillations play the role if they are variable in the model (see P2).

Aref Pariz and Abolfazl Ziaeemehr are engaged in this project, in a collaboration with Mina Zarei from IASBS, and Claudio Mirasso and Ingo Fischer from Mallorca.

 

Problem 2:

Connectivity between the modules, are also known to be important in the quality of the transmission of the signal between the brain modules [6]. We are exploring how the presence of feedback connections can improve the transmission of signals and how the system can self-organize into a “tuned” structure when synapses are plastic.

Hedyeh Rezai is the motor of this research which is ongoing in collaboration with Ad Aertsen and Arvind Kumar from Freiburg.

 

Problem 3:

There are some facts in the connectivity of the cortical circuits, overlooked in models. Log-normal distribution of the synaptic weights and how the weak and strong synapses are distributed [6] can crucially alter the results of the crude models with always considering narrow distributions. Effect of multiple synapses between the pair of neurons (with supposedly different delays and different strengths [7]), effect of over-expression of bi-directional connections. What could be the effect of breaking cortex E/I golden ratio 80-20?

A key-paper: A very important experimental finding indicates the positive correlation between the amplitude and the instantaneous period of gamma oscillations [8]. This correlation confirms the role of inhibition in the generation of the gamma rhythm and exerts a constraint on the time constants of the E and I synapses, never highlighted in the modeling studies. And much more can be deduced from this article!

Just recently a paper has been published which has experimentally explored the abundance of autaptic connections in layer 5 of cortex in mouse and human, showing >50% of principal cell in this layer have one or multiple autaptic synapses which are about four time stronger than between-cell connections [9]. Its effect on collective dynamics is worth to be explored.

We welcome to interested students to join this research.

 

References
[1] Pariz, Aref, Zahra G. Esfahani, Shervin S. Parsi, Alireza Valizadeh, Santiago Canals, and Claudio R. Mirasso. "High frequency neurons determine effective connectivity in neuronal networks." NeuroImage 166 (2018): 349-359.

[2] Palmigiano, Agostina, Theo Geisel, Fred Wolf, and Demian Battaglia. "Flexible information routing by transient synchrony." Nature neuroscience 20, no. 7 (2017): 1014.

[3] Sherfey, Jason S., Salva Ardid, Earl K. Miller, Michael E. Hasselmo, and Nancy J. Kopell. "Prefrontal oscillations modulate the propagation of neuronal activity required for working memory." bioRxiv (2019): 531574.

[4] Fries, Pascal. "A mechanism for cognitive dynamics: neuronal communication through neuronal coherence." Trends in cognitive sciences 9.10 (2005): 474-480.

[5] Sporns, Olaf. "Network attributes for segregation and integration in the human brain." Current opinion in neurobiology 23.2 (2013): 162-171.

[6] Hahn, Gerald, Adrian Ponce-Alvarez, Gustavo Deco, Ad Aertsen, and Arvind Kumar. "Portraits of communication in neuronal networks." Nature Reviews Neuroscience (2018): 1.