Biology subjects

Yang, G. R.

Publications and source records attributed to Yang, G. R..

4 recordsLinked to original sources

bioRxiv2018

Circuit mechanisms for the maintenance and manipulation of information in working memory

Recently it has been proposed that information in short-term memory may not always be stored in persistent neuronal activity, but can be maintained in \"activity-silent\" hidden states such as synaptic efficacies endowed with short-term plasticity (STP). However, working memory involves manipulation as well as maintenance of information in the absence of external stimuli. In this work, we investigated working memory representation using recurrent neural network (RNN) models trained to perform several working memory dependent tasks. We found that STP can support the short-term maintenance of information provided that the memory delay period is sufficiently short. However, in tasks that require actively manipulating information, persistent neuronal activity naturally emerges from learning, and the amount of persistent neuronal activity scales with the degree of manipulation required. These results shed insight into the current debate on working memory encoding, and suggest that persistent neural activity can vary markedly between tasks used in different experiments.

neuroscience↗

bioRxiv2017

Inter-areal balanced amplification enhances signal propagation in a large-scale circuit model of the primate cortex

Reliable signal transmission represents a fundamental challenge for cortical systems, which display a wide range of weights of feedforward and feedback connections among heterogeneous areas. We re-examine the question of signal transmission across the cortex in network models based on recently available mesoscopic, directed- and weighted-inter-areal connectivity data of the macaque cortex. Our findings reveal that, in contrast to feed-forward propagation models, the presence of long-range excitatory feedback projections could compromise stable signal propagation. Using population rate models as well as a spiking network model, we find that effective signal propagation can be accomplished by balanced amplification across cortical areas while ensuring dynamical stability. Moreover, the activation of prefrontal cortex in our model requires the input strength to exceed a threshold, in support of the ignition model of conscious processing, demonstrating our model as an anatomically-realistic platform for investigations of the global primate cortex dynamics.

neuroscience↗

bioRxiv2017

Clustering and compositionality of task representations in a neural network trained to perform many cognitive tasks

A neural system has the ability to flexibly perform many tasks, but the underlying mechanism cannot be elucidated in traditional experimental and modeling studies designed for one task at a time. Here, we trained a single network model to perform 20 cognitive tasks that may involve working memory, decision-making, categorization and inhibitory control. We found that after training, recurrent units developed into clusters that are functionally specialized for various cognitive processes. We introduce a measure to quantify relationships between single-unit neural representations of tasks, and report five distinct types of such relationships that can be tested experimentally. Surprisingly, our network developed compositionality of task representations, a critical feature for cognitive flexibility, whereby one task can be performed by recombining instructions for other tasks. Finally, we demonstrate how the network could learn multiple tasks sequentially. This work provides a computational platform to investigate neural representations of many cognitive tasks.

neuroscience↗

bioRxiv2017

Response Reversal During Top-Down Modulation In Cortical Circuits With Multiple Interneuron Types

Pyramidal cells and interneurons expressing parvalbumin, somatostatin, or vasoactive intestinal peptide show cell type-specific connectivity patterns leading to a canonical microcircuit across cortex. Dissecting the dynamics of this microcircuit is essential to our understanding of the mammalian cortex. However, experiments recording from this circuit often report counterintuitive and seemingly contradictory findings. For example, the response of a V1 neural population to top-down behavioral modulation can reverse from positive to negative when the bottom-up thalamic input changes. We developed a theoretical framework to explain such response reversal, and we showed how this complex dynamics can emerge in circuits that possess two key features: the presence of multiple interneuron populations and a non-linear dependence between the input and output of the populations. Furthermore, we built a cortical circuit model and the comparison of our simulations with real data shows that our model reproduces the complex dynamics observed experimentally in mouse V1. Our explicit calculations allowed us to pinpoint the connections critical to response reversal, and to predict the existence of more types of complex dynamics that could be experimentally tested and the conditions to observe them.

neuroscience↗