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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.

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.

13 14 Cortical neurons show distinct firing patterns across multiple task-epochs 15 characterized by distinct computational aspects. Recent studies suggest that 16 such distinct patterns underly dynamic population code achieving 17 computational flexibility, whereas neurons in some cortical areas often show 18 coherent firing patterns across epochs. To understand how such coherent 19 single-neuron code contribute to dynamic population code, we analyzed 20 neural responses in the perirhinal cortex (PRC) during cue and reward 21 epochs of a two-alternative forced-choice task. We found that the PRC 22 neurons often encoded the opposite choice-directions between those epochs. 23 By using principal component analysis as population-level analysis, we 24 identified neural subspaces associated with each epoch, which reflected 25 coordinated patterns across the neurons. The cue and reward epochs shared 26 neural dimensions where the choice directions were consistently 27 discriminated. Interestingly, those dimensions were supported by 28 dynamically changing contributions of individual neurons. These results 29 indicated heterogeneity of coherent single-neuron responses in their 30 3 contribution to population code. 31 32 33 65 two-alternative forced-choice task and analyzed neural responses in two 66 5 epochs, where different computations are demanded: making predictions 67 about the outcome of choices (cue epoch) and reinforcing the choices (reward 68 epoch). By taking advantage of the interleaved visual and olfactory cue 69 stimuli, which allowed us to evaluate modality-independent encodings, we 70 analyzed dynamic population encodings related to different choices during 71 those epochs in relation to single-neuron level selectivity. 72 73 74 Results 75 76Neurons in the PRC encode choice directions during a two-alternative 77 forced-choice task. We trained rats to perform a two-alternative forced-78 choice task where they chose a target port (left/right) associated with a 79 presented cue to obtain reward ( Fig. 1a-b). The task performance was of a 80 similar level regardless of the cue modality (mean correct rate in visual 81 trials = 95.6 ± 5.5%; olfactory trials = 92.3 ± 4.3%). We recorded spiking 82 activities from the left PRC (n = 207 neurons) during the task performance 83 (37 sessions in five rats). 84 6 As shown in Fig. 1c, the PRC neurons typically showed distinct temporal 85 firing patterns in left and right trials. To characterize how the PRC was 86 activated by different trial conditions, we compared firing pattens among 87 different cue-modalities and choices across all the recorded neurons. The 88 neurons were sorted by their peak firing rates in visually-cued left choice 89 trials (top left in Fig. 1d). As consistent with previous studies in other brain 90 regions 28-33 , the peak responses of the PRC neurons tiled the duration of a 91 trial. The response patterns across the neurons were well preserved 92 between the cue modalities but much less so between the choice directions 93 (comparison between th...

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.

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During visual detection tasks, subjects sometimes fail to respond to identical visual stimuli even when the stimuli are registered on their retinas. It is widely assumed that variability in detection performance is attributed to the fidelity of the visual responses in visual cortical areas, which could be modulated by fluctuations of subjective internal states such as vigilance, attention, and reward experiences. However, it is not clear what neural ensembles represent such different internal states. Here, we utilized a behavioral task that differentiated distinct perceptual states to identical stimuli, and analyzed neuronal responses simultaneously recorded from both primary visual cortex (V1) and posterior parietal cortex (PPC) during the task. We found that population activity differed across choice types with the major contribution of non-sensory neurons, rather than visually-responsive neurons, in V1 as well as PPC. The distinct population-level activity in V1, but not PPC, was restricted within the stimulus presentation epoch, which was distinguished from pre-stimulus background activity and was supported by near-zero noise correlation. These results indicate a major contribution of non-sensory neurons in V1 for population-level computation that enables behavioral responses from visual information.2 instance, in sensory detection task, human or animal subjects are instructed or well-trained to 3 reliably report the presence and absence of sensory stimuli to obtain rewards. When the sensory 4 evidence is near threshold for the decision criterion, the subjects' reports vary across trials 5 despite the best efforts of subjects to get rewards. Interestingly, even if they report the absence 6 of stimuli, it is sometimes possible that they could correctly guess the contents of the stimuli 7 above chance level if they are forced to answer 1-8 . Revealing the neural mechanisms underlying 8 such trial-by-trial variability of perceptual reports is crucial to understand how the brain exploits 9 sensory information for optimal decision making. 11The trial-by-trial variance of the responses to identical stimuli is believed to reflect noises in 12 conversion of sensory information into motor outputs 9 . It has been demonstrated that variability 13 of firing rates of sensory neurons is responsible for the trial-by-trial variability of choices 10-14 .14 However, the accumulating evidence suggests that perceptual decision is also significantly 15 affected by latent subjective states reflecting task engagement 15,16 . For instance, it is known that 16 behavioral response variability is correlated with mind wondering in humans 17 and fluctuations 17 of physiological and behavioral states in animals [18][19][20][21][22] . These drifts of subjective states could be 18 partially attributed to fluctuation of cortical states 22-28 , in which synchronization and 19 desynchronization of many neurons in particular areas of cortex could affect efficiency of the 20 population coding 29,30 . In addition, the task engagement is known t...