February 2023 Newsletter

Featured Research

Learning efficient backprojections across cortical hierarchies
Kevin Max et. al.

Sensory processing and learning needs assignment of credit to synapses in all areas of the cortex. In deep learning, we can use error backpropagation which is biologically implausible. In this article, researchers are therefore introducing Phaseless Alignment Learning as a bio-plausible solution to learning efficient feedback weights in layered cortical hierarchies. This is achieved by exploiting the noise naturally found in biophysical systems as an additional carrier of information.

Mapping a synaptic landscape of a cognitive nucleus of the thalamus
Researchers have identified the origin, intranuclear distribution and size of all types of axon terminals present in a cognitive nucleus of the thalamus. This will be helpful in understanding and developing computational models of the brain since these nuclei integrate signals from different cerebral cortex areas and send the result back to the cortex. The results showed that neuronal subpopulations of the same nucleus can receive different combinations of signals, produce different computations and therefore create multiple functionally different parallel circuits.

Relevance of time perception neural networks in temporal features of concepts
Karim Johari et. al.

Just as concept features like motion and sound function through corresponding neural networks evidence now suggests that temporal features of concepts are based on networks relevant for time conception, i.e. time-relevant meaning of words is subserved by time perception circuits in the human brain. Results showed significantly larger ERPs for event duration vs. object size judgments over right parietal electrodes, a region associated with temporal processing. Additionally, alpha/beta neural oscillation showed a stronger desynchronization for event duration compared to object size in the right parietal electrodes.

Learn More About Whole Brain Emulation

In this section we introduce ideas related to whole brain emulation research. This month we explain the meaning of scale separation.

What is scale seperation?

Scale separation exists when details below a certain level of spatial and/or temporal resolution can be considered in the aggregate without significantly impacting the results with regard to chosen success criteria.


Assuming you want to study the dynamics of an action potential, scale separation would allow us to model at the level of resistance, capacitance, and ion concentrations, while ignoring the underlying molecular details.

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