Auditory Cortex Neurons Show Task-Related and Learning-Dependent Selectivity toward Sensory Input and Reward during the Learning Process of an Associative Memory Task.

The activity of primary auditory cortex (A1) neurons is modulated not only by sensory inputs but also by other task-related variables in associative learning. However, it is unclear how A1 neural activity changes dynamically in response to these variables during the learning process of associative memory tasks. Therefore, we developed an associative memory task using auditory stimuli in rats. In this task, rats were required to associate tone frequencies (high and low) with a choice of ports (right or left) to obtain a reward. The activity of A1 neurons in the rats during the learning process of the task was recorded. A1 neurons increased their firing rates either when the rats were presented with a high or low tone (frequency-selective cells) before they chose either the left or right port (choice-direction cells), or when they received a reward after choosing either the left or right port (reward-direction cells). Furthermore, the proportion of frequency-selective cells and reward-direction cells increased with task acquisition and reached the maximum level in the last stage of learning. These results suggest that A1 neurons have task- and learning-dependent selectivity toward sensory input and reward when auditory tones and behavioral responses are gradually associated during task training. This selective activity of A1 neurons may facilitate the formation of associations, leading to the consolidation of associative memory.

Hippocampal CA1 Neurons Represent Positive Feedback During the Learning Process of an Associative Memory Task.

The hippocampus is crucial for forming associations between environmental stimuli. However, it is unclear how neural activities of hippocampal neurons dynamically change during the learning process. To address this question, we developed an associative memory task for rats with auditory stimuli. In this task, the rats were required to associate tone pitches (high and low) and ports (right and left) to obtain a reward. We recorded the firing activity of neurons in rats hippocampal CA1 during the learning process of the task. As a result, many hippocampal CA1 neurons increased their firing rates when the rats received a reward after choosing either the left or right port. We referred to these cells as "reward-direction cells." Furthermore, the proportion of the reward-direction cells increased in the middle-stage of learning but decreased after the completion of learning. This result suggests that the activity of reward-direction cells might serve as "positive feedback" signal that facilitates the formation of associations between tone pitches and port choice.

Over-representation of fundamental decision variables in the prefrontal cortex underlies decision bias.

The brain is organized into anatomically distinct structures consisting of a variety of projection neurons. While such evolutionarily conserved neural circuit organization underlies the innate ability of animals to swiftly adapt to environments, they can cause biased cognition and behavior. Although recent studies have begun to address the causal importance of projection-neuron types as distinct computational units, it remains unclear how projection types are functionally organized in encoding variables during cognitive tasks. This review focuses on the neural computation of decision making in the prefrontal cortex and discusses what decision variables are encoded by single neurons, neuronal populations, and projection type, alongside how specific projection types constrain decision making. We focus particularly on "over-representations" of distinct decision variables in the prefrontal cortex that reflect the biological and subjective significance of the variables for the decision makers. We suggest that task-specific over-representation in the prefrontal cortex involves the refinement of the given decision making, while generalized over-representation of fundamental decision variables is associated with suboptimal decision biases, including pathological ones such as those in patients with psychiatric disorders. Such over-representation of the fundamental decision variables in the prefrontal cortex appear to be tightly constrained by afferent and efferent connections that can be optogenetically intervened on. These ideas may provide critical insights into potential therapeutic targets for psychiatric disorders, including addiction and depression.

Contribution of non-sensory neurons in visual cortical areas to visually guided decisions in the rat.

It is widely assumed that trial-by-trial variability in visual detection performance is explained by the fidelity of visual responses in visual cortical areas influenced by fluctuations of internal states, such as vigilance and behavioral history. However, it is not clear which neuronal ensembles represent such different internal states. Here, we utilized a visual detection task, which distinguishes internal states in response to identical stimuli, while recording neurons simultaneously from the primary visual cortex (V1) and the posterior parietal cortex (PPC). We found that rats sometimes withheld their responses to visual stimuli despite the robust presence of visual responses in V1. Our unsupervised analysis revealed distinct population dynamics segregating hit responses from misses, orthogonally embedded to visual response dynamics in both V1 and PPC. Heterogeneous non-sensory neurons in V1 and PPC significantly contributed to population-level encoding accompanied with the modulation of noise correlation only in V1. These results highlight the non-trivial contributions of non-sensory neurons in V1 and PPC for population-level computations that reflect the animals' internal states to drive behavioral responses to visual stimuli.

Dynamic coordination of the perirhinal cortical neurons supports coherent representations between task epochs.

Cortical neurons show distinct firing patterns across multiple task epochs characterized by different computations. Recent studies suggest that such distinct patterns underlie dynamic population code achieving computational flexibility, whereas neurons in some cortical areas often show coherent firing patterns across epochs. To understand how coherent single-neuron code contributes to dynamic population code, we analyzed neural responses in the rat perirhinal cortex (PRC) during cue and reward epochs of a two-alternative forced-choice task. We found that the PRC neurons often encoded the opposite choice directions between those epochs. By using principal component analysis as a population-level analysis, we identified neural subspaces associated with each epoch, which reflected coordination across the neurons. The cue and reward epochs shared neural dimensions where the choice directions were consistently discriminated. Interestingly, those dimensions were supported by dynamically changing contributions of the individual neurons. These results demonstrated heterogeneity of coherent single-neuron representations in their contributions to population code.