Face familiarity detection with complex synapses

Li, Jian; Benna, Marcus K.; Fusi, Stefano

doi:10.1016/j.isci.2022.105856

Cited by 3 publications

(8 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These neural signatures can possibly be explained by Prh’s role in familiarity and novel object recognition 33 . Familiarity can be detected by comparing, through subtraction, the current sensory input to one that was previously stored in memory 34 . As sensory information is stored in memory, subtraction results in reduced responses to familiar stimuli and increased responses to novel stimuli.…”

Section: Resultsmentioning

confidence: 99%

Perirhinal cortex learns a predictive map of the task environment

Lee,

McLachlan,

Nogueira

et al. 2024

Nat Commun

Self Cite

View full text Add to dashboard Cite

Goal-directed tasks involve acquiring an internal model, known as a predictive map, of relevant stimuli and associated outcomes to guide behavior. Here, we identified neural signatures of a predictive map of task behavior in perirhinal cortex (Prh). Mice learned to perform a tactile working memory task by classifying sequential whisker stimuli over multiple training stages. Chronic two-photon calcium imaging, population analysis, and computational modeling revealed that Prh encodes stimulus features as sensory prediction errors. Prh forms stable stimulus-outcome associations that can progressively be decoded earlier in the trial as training advances and that generalize as animals learn new contingencies. Stimulus-outcome associations are linked to prospective network activity encoding possible expected outcomes. This link is mediated by cholinergic signaling to guide task performance, demonstrated by acetylcholine imaging and systemic pharmacological perturbation. We propose that Prh combines error-driven and map-like properties to acquire a predictive map of learned task behavior.

show abstract

Section: Resultsmentioning

confidence: 99%

Perirhinal cortex learns a predictive map of the task environment

Lee,

McLachlan,

Nogueira

et al. 2024

Nat Commun

Self Cite

View full text Add to dashboard Cite

show abstract

“…Yet, it is worth mentioning that this has been done in other computational models of recognition without changing the general pattern of results obtained (Bogacz & Brown, 2003b;Ji-An et al, 2023;Norman & O'Reilly, 2003).…”

Section: Discussionmentioning

confidence: 99%

“…One way to overcome this problem would be to implement more realistic neural networks taking into account electro‐physiological properties of biological neurons. Yet, it is worth mentioning that this has been done in other computational models of recognition without changing the general pattern of results obtained (Bogacz & Brown, 2003b; Ji‐An et al, 2023; Norman & O'Reilly, 2003).…”

Section: Discussionmentioning

confidence: 99%

“…As our model was inspired by previous works, it therefore functions in a similar way (Ji‐An et al, 2023; Kazanovich & Borisyuk, 2021). That is, it was designed as a two‐step network combining deep learning and simple feedforward neural networks (see Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…Trying to fulfill the gap between modeling and human brain processing, some models used convolutional neural networks (CNNs) to mimic the ventral pathway processing to the PrC (Ji‐An et al, 2023; Kazanovich & Borisyuk, 2021; Tyulmankov et al, 2022). CNNs consist of the succession of several layers of artificial neurons programmed to reproduce the detection of features in a stimulus, mimicking the cells in the visual cortex (Bengio & Lecun, 1997).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computational models can distinguish the contribution from different mechanisms to familiarity recognition

Read,

Delhaye,

Sougné

2023

Hippocampus

View full text Add to dashboard Cite

Familiarity is the strange feeling of knowing that something has already been seen in our past. Over the past decades, several attempts have been made to model familiarity using artificial neural networks. Recently, two learning algorithms successfully reproduced the functioning of the perirhinal cortex, a key structure involved during familiarity: Hebbian and anti‐Hebbian learning. However, performance of these learning rules is very different from one to another thus raising the question of their complementarity. In this work, we designed two distinct computational models that combined Deep Learning and a Hebbian learning rule to reproduce familiarity on natural images, the Hebbian model and the anti‐Hebbian model, respectively. We compared the performance of both models during different simulations to highlight the inner functioning of both learning rules. We showed that the anti‐Hebbian model fits human behavioral data whereas the Hebbian model fails to fit the data under large training set sizes. Besides, we observed that only our Hebbian model is highly sensitive to homogeneity between images. Taken together, we interpreted these results considering the distinction between absolute and relative familiarity. With our framework, we proposed a novel way to distinguish the contribution of these familiarity mechanisms to the overall feeling of familiarity. By viewing them as complementary, our two models allow us to make new testable predictions that could be of interest to shed light on the familiarity phenomenon.

show abstract

Perirhinal Cortex Learns A Predictive Map of The Task Environment

Lee

McLachlan

Nogueira

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Goal-directed tasks involve acquiring an internal model, known as a predictive map, of relevant stimuli and associate outcomes to guide behavior. Here, we identified neural signatures of a predictive map of task behavior in perirhinal cortex (Prh). Mice learned to perform a tactile working memory task by classifying sequential whisker stimuli over multiple training stages. Chemogenetic inactivation demonstrated that Prh is involved in task learning. Chronic two-photon calcium imaging and population analysis revealed that Prh encodes stimulus features as sensory prediction errors. Prh forms stable stimulus-outcome associations that retrogradely expand in a predictive manner and generalize as animals learn new contingencies. Stimulus-outcome associations are linked to ongoing network activity encoding possible expected outcomes. This link is mediated by cholinergic signaling to guide task performance, demonstrated by acetylcholine imaging and perturbation. This demonstrates that Prh combines error-driven and map-like properties to acquire a predictive map of learned task behavior.

show abstract

Face familiarity detection with complex synapses

Cited by 3 publications

References 54 publications

Perirhinal cortex learns a predictive map of the task environment

Perirhinal cortex learns a predictive map of the task environment

Computational models can distinguish the contribution from different mechanisms to familiarity recognition

Perirhinal Cortex Learns A Predictive Map of The Task Environment

Contact Info

Product

Resources

About