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Advanced Neurology

ORIGINAL RESEARCH ARTICLE
Evaluating the cognitive and

electrophysiological plausibility of a thalamic
computational model

Ricardo Tiosso Panassiol * and Francisco Javier Ropero Pelaez 1,2†
1†
1 Department of Neuroscience and Behavior, Institute of Psychology, University of São Paulo, São
Paulo, São Paulo, Brazil
2 Center for Mathematics, Computing, and Cognition, Federal University of ABC, São Paulo, São
Paulo, Brazil

Abstract

The thalamus acts as a gateway to the cortex, relaying information from the
brainstem and spinal cord to cortical regions. Evidence suggests that thalamic
networks play a role in pattern recognition by extracting key elements from sensory
input. We propose that thalamic networks function as a central orthogonalizer
in the brain, enabling the application of the Hebbian learning rule without
contamination or “interference” between the correct output for one input pattern
and the output for other input patterns. This study aims to describe a biologically
plausible artificial neural network that mimics aspects of the physiological
activity of the thalamic circuit. To validate this proposal, the network was tested
† These authors contributed equally
to this work. in four different scenarios. The model successfully replicated electrophysiological
processes, including: (i) inhibitory facilitation; (ii) waveform sculpting in reticular
*Corresponding author:
Ricardo Tiosso Panassiol cells; (iii) pattern completion of thalamic input; and (iv) computational processing
(ricardo.tiosso@usp.br) of stabilized images on the retina. In human experiments, stabilized retinal
images were perceived as sequences of patterns that appeared and disappeared
Citation: Panassiol RT,
Pelaez FJR. Evaluating the suddenly. These patterns closely resembled or were related to the presented image,
cognitive and electrophysiological suggesting that they represent a mix of principal components derived from learned
plausibility of a thalamic images. Such components emerge when a complete image, or only a portion of it,
computational model.
Adv Neuro. 2024;3(3):3188. reaches the thalamus. Our model effectively reconstructs images based on partial
doi: 10.36922/an.3188 eye-to-thalamus information, mirroring human visual responses.
Received: March 17, 2024
Accepted: July 3, 2024 Keywords: Systems neuroscience; Computational modeling; Thalamic circuitry; Principal
components; Hebbian learning
Published Online: September 3, 2024
Copyright: © 2024 Author(s).
This is an Open-Access article
distributed under the terms of the
Creative Commons Attribution 1. Introduction
License, permitting distribution,
and reproduction in any medium, At present, there is broad consensus on interpreting learning or the plastic changes that
provided the original work is occur in the brain in light of the Hebbian paradigm. The idea that synaptic connections
1,2
properly cited. are strengthened by the correlated activity of pre-synaptic and post-synaptic neurons
3
Publisher’s Note: AccScience was first articulated by Hebb in 1948. It gained strength in 1973 when Bliss and
Publishing remains neutral with Lömo experimentally demonstrated that the processes described by Hebb occur in the
4
regard to jurisdictional claims in
published maps and institutional hippocampus. Today, the biological phenomenon most closely associated with Hebbian
affiliations. learning is long-term potentiation (LTP). 5
Volume 3 Issue 3 (2024) 1 doi: 10.36922/an.3188