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Single cell plasticity and population coding stability in auditory thalamus upon associative learning.

ABSTRACT: Cortical and limbic brain areas are regarded as centres for learning. However, how thalamic sensory relays participate in plasticity upon associative learning, yet support stable long-term sensory coding remains unknown. Using a miniature microscope imaging approach, we monitor the activity of populations of auditory thalamus (medial geniculate body) neurons in freely moving mice upon fear conditioning. We find that single cells exhibit mixed selectivity and heterogeneous plasticity patterns to auditory and aversive stimuli upon learning, which is conserved in amygdala-projecting medial geniculate body neurons. Activity in auditory thalamus to amygdala-projecting neurons stabilizes single cell plasticity in the total medial geniculate body population and is necessary for fear memory consolidation. In contrast to individual cells, population level encoding of auditory stimuli remained stable across days. Our data identifies auditory thalamus as a site for complex neuronal plasticity in fear learning upstream of the amygdala that is in an ideal position to drive plasticity in cortical and limbic brain areas. These findings suggest that medial geniculate body's role goes beyond a sole relay function by balancing experience-dependent, diverse single cell plasticity with consistent ensemble level representations of the sensory environment to support stable auditory perception with minimal affective bias.

SUBMITTER: Taylor JA

PROVIDER: S-EPMC8076296 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Single cell plasticity and population coding stability in auditory thalamus upon associative learning.

Taylor James Alexander JA Hasegawa Masashi M Benoit Chloé Maëlle CM Freire Joana Amorim JA Theodore Marine M Ganea Dan Alin DA Innocenti Sabrina Milena SM Lu Tingjia T Gründemann Jan J

Nature communications 20210426 1

Cortical and limbic brain areas are regarded as centres for learning. However, how thalamic sensory relays participate in plasticity upon associative learning, yet support stable long-term sensory coding remains unknown. Using a miniature microscope imaging approach, we monitor the activity of populations of auditory thalamus (medial geniculate body) neurons in freely moving mice upon fear conditioning. We find that single cells exhibit mixed selectivity and heterogeneous plasticity patterns to ...[more]

PMID: 33903596

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Project description:The computational principles adopted by the hippocampus in associative memory (AM) tasks have been one of the most studied topics in computational and theoretical neuroscience. Recent theories suggested that AM and the predictive activities of the hippocampus could be described within a unitary account, and that predictive coding underlies the computations supporting AM in the hippocampus. Following this theory, a computational model based on classical hierarchical predictive networks was proposed and was shown to perform well in various AM tasks. However, this fully hierarchical model did not incorporate recurrent connections, an architectural component of the CA3 region of the hippocampus that is crucial for AM. This makes the structure of the model inconsistent with the known connectivity of CA3 and classical recurrent models such as Hopfield Networks, which learn the covariance of inputs through their recurrent connections to perform AM. Earlier PC models that learn the covariance information of inputs explicitly via recurrent connections seem to be a solution to these issues. Here, we show that although these models can perform AM, they do it in an implausible and numerically unstable way. Instead, we propose alternatives to these earlier covariance-learning predictive coding networks, which learn the covariance information implicitly and plausibly, and can use dendritic structures to encode prediction errors. We show analytically that our proposed models are perfectly equivalent to the earlier predictive coding model learning covariance explicitly, and encounter no numerical issues when performing AM tasks in practice. We further show that our models can be combined with hierarchical predictive coding networks to model the hippocampo-neocortical interactions. Our models provide a biologically plausible approach to modelling the hippocampal network, pointing to a potential computational mechanism during hippocampal memory formation and recall, which employs both predictive coding and covariance learning based on the recurrent network structure of the hippocampus.

Dataset Information

Single cell plasticity and population coding stability in auditory thalamus upon associative learning.

Publications

Single cell plasticity and population coding stability in auditory thalamus upon associative learning.

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