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Ketamine is a widely used clinical drug that has several functional and clinical applications, including its use as an anaesthetic, analgesic, anti-depressive, anti-suicidal agent, among others. Among its diverse behavioral effects, it influences short-term memory and induces psychedelic effects. At the neural level across different brain areas, it modulates neural firing rates, neural tuning, brain oscillations, and modularity, while promoting hypersynchrony and random connectivity between neurons. In our recent studies we demonstrated that topical application of ketamine on the visual cortex alters neural tuning and promotes vigorous connectivity between neurons by decreasing their firing variability. Here, we begin with a brief review of the literature, followed by results from our lab, where we synthesize a dendritic model of neural tuning and network changes following ketamine application. This model has potential implications for focused modulation of cortical networks in clinical settings. Finally, we identify current gaps in research and suggest directions for future studies, particularly emphasizing the need for more animal experiments to establish a platform for effective translation and synergistic therapies combining ketamine with other protocols such as training and adaptation. In summary, investigating ketamine's broader systemic effects, not only provides deeper insight into cognitive functions and consciousness but also paves the way to advance therapies for neuropsychiatric disorders.

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The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the nucleus basalis (NB) of Meynert have been recently examined in two male monkeys (Qi et al., 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in the PFC.

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The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the Nucleus Basalis of Meynert (NB) have been recently examined (Qi et al. 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in PFC.

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While the neural mechanisms underpinning the perception of muscularity are poorly understood, recent progress has been made using the psychophysical technique of visual adaptation. Prolonged visual exposure to high (low) muscularity bodies causes subsequently viewed bodies to appear less (more) muscular, revealing a recalibration of the neural populations encoding muscularity. Here, we use visual adaptation to further elucidate the tuning properties of the neural processes underpinning muscle perception for the upper and lower halves of the body. Participants manipulated the apparent muscularity of upper and lower bodies until they appeared 'normal', prior to and following exposure to a series of top/bottom halves of bodies that were either high or low in muscularity. In Experiment 1, participants were adapted to isolated own-gender body halves from one of four conditions; increased (muscularity) upper (body half), increased lower, decreased upper, or decreased lower. Despite the presence of muscle aftereffects when the body halves the participants viewed and manipulated were congruent, there was only weak evidence of muscle aftereffect transfer between the upper and lower halves of the body. Aftereffects were significantly weaker when body halves were incongruent, implying minimal overlap in the neural mechanisms encoding muscularity for body half. Experiment 2 examined the generalisability of Experiment 1's findings in a more ecologically valid context using whole-body stimuli, producing a similar pattern of results as Experiment 1, but with no evidence of cross-adaptation. Taken together, the findings are most consistent with muscle-encoding neural populations that are body-half selective. As visual adaptation has been implicated in cases of body size and shape misperception, the present study furthers our current understanding of how these perceptual inaccuracies, particularly those involving muscularity, are developed, maintained, and may potentially be treated.

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Neural tuning for print refers to differential neural responses (e.g., the N1 component of event-related potentials) to different orthographic forms and other visual stimuli. While impaired neural tuning for print has been well established in dyslexic children who read alphabetic scripts, it remains unclear whether such effects exist in dyslexic children who read Chinese, which dramatically differs in visual and linguistic characteristics from alphabetic words. To fill this gap, we examined two levels of the neural tuning for print: coarse tuning (i.e., false character vs. stroke combination), and fine tuning (i.e., sub-lexical tuning: pseudo character vs. false character; and lexical tuning: real character vs. pseudo character). Using the event-related potential technique, we examined 14 typically developing children and 16 dyslexic children who were screened from 216 nine-year-old children in the third grade. For typically developing children, we observed both coarse and sub-lexical tuning. Critically, for dyslexic children, we found stronger N1 for false character than for stroke combination, suggesting intact coarse tuning, but a reduced N1 difference between false character and pseudo character, suggesting impaired sub-lexical tuning. These results clearly show selective impairments in fine neural tuning at the sub-lexical level in Chinese dyslexic children. Our findings may be associated with unique features of Chinese characters.

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A goal in visual neuroscience is to explain how neurons respond to natural scenes. However, neurons are generally tested using simpler stimuli, often because they can be transformed smoothly, allowing the measurement of tuning functions (i.e., response peaks and slopes). Here, we test the idea that all classic tuning curves can be viewed as slices of a higher-dimensional tuning landscape. We use activation-maximizing stimuli ("prototypes") as landmarks in a generative image space and map tuning functions around these peaks. We find that neurons show smooth bell-shaped tuning consistent with radial basis functions, spanning a vast image transformation range, with systematic differences in landscape geometry from V1 to inferotemporal cortex. By modeling these trends, we infer that neurons in the higher visual cortex have higher intrinsic feature dimensionality. Overall, these results suggest that visual neurons are better viewed as signaling distances to prototypes on an image manifold.

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We have an amazing ability to categorize objects in the world around us. Nevertheless, how cortical regions in human ventral temporal cortex (VTC), which is critical for categorization, support this behavioral ability, is largely unknown. Here, we examined the relationship between neural responses and behavioral performance during the categorization of morphed silhouettes of faces and hands, which are animate categories processed in cortically adjacent regions in VTC. Our results reveal that the combination of neural responses from VTC face- and body-selective regions more accurately explains behavioral categorization than neural responses from either region alone. Furthermore, we built a model that predicts a person's behavioral performance using estimated parameters of brain-behavior relationships from a different group of people. Moreover, we show that this brain-behavior model generalizes to adjacent face- and body-selective regions in lateral occipitotemporal cortex. Thus, while face- and body-selective regions are located within functionally distinct domain-specific networks, cortically adjacent regions from both networks likely integrate neural responses to resolve competing and perceptually ambiguous information from both categories.

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Natural brains perform miraculously well in learning new tasks from a small number of samples, whereas sample efficient learning is still a major open problem in the field of machine learning. Here, we raise the question, how the neural coding scheme affects sample efficiency, and make first progress on this question by proposing and analyzing a learning algorithm that uses a simple reinforce-type plasticity mechanism and does not require any gradients to learn low dimensional mappings. It harnesses three bio-plausible mechanisms, namely, population codes with bell shaped tuning curves, continous attractor mechanisms and probabilistic synapses, to achieve sample efficient learning. We show both theoretically and by simulations that population codes with broadly tuned neurons lead to high sample efficiency, whereas codes with sharply tuned neurons account for high final precision. Moreover, a dynamic adaptation of the tuning width during learning gives rise to both, high sample efficiency and high final precision. We prove a sample efficiency guarantee for our algorithm that lies within a logarithmic factor from the information theoretical optimum. Our simulations show that for low dimensional mappings, our learning algorithm achieves comparable sample efficiency to multi-layer perceptrons trained by gradient descent, although it does not use any gradients. Furthermore, it achieves competitive sample efficiency in low dimensional reinforcement learning tasks. From a machine learning perspective, these findings may inspire novel approaches to improve sample efficiency. From a neuroscience perspective, these findings suggest sample efficiency as a yet unstudied functional role of adaptive tuning curve width.

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Adaptation is a key component of efficient coding in sensory neurons. However, it remains unclear how neurons can provide a stable representation of external stimuli given their history-dependent responses. Here we show that a stable representation is maintained if efficiency is optimized by a population of neurons rather than by neurons individually. We show that spike-frequency adaptation and E/I balanced recurrent connectivity emerge as solutions to a global cost-accuracy tradeoff. The network will redistribute sensory responses from highly excitable neurons to less excitable neurons as the cost of neural activity increases. This does not change the representation at the population level despite causing dynamic changes in individual neurons. By applying this framework to an orientation coding network, we reconcile neural and behavioral findings. Our approach underscores the common mechanisms behind the diversity of neural adaptation and its role in producing a reliable representation of the stimulus while minimizing metabolic cost.

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The auditory system converts the physical properties of a sound waveform to neural activities and processes them for recognition. During the process, the tuning to amplitude modulation (AM) is successively transformed by a cascade of brain regions. To test the functional significance of the AM tuning, we conducted single-unit recording in a deep neural network (DNN) trained for natural sound recognition. We calculated the AM representation in the DNN and quantitatively compared it with those reported in previous neurophysiological studies. We found that an auditory-system-like AM tuning emerges in the optimized DNN. Better-recognizing models showed greater similarity to the auditory system. We isolated the factors forming the AM representation in the different brain regions. Because the model was not designed to reproduce any anatomical or physiological properties of the auditory system other than the cascading architecture, the observed similarity suggests that the AM tuning in the auditory system might also be an emergent property for natural sound recognition during evolution and development.SIGNIFICANCE STATEMENT This study suggests that neural tuning to amplitude modulation may be a consequence of the auditory system evolving for natural sound recognition. We modeled the function of the entire auditory system; that is, recognizing sounds from raw waveforms with as few anatomical or physiological assumptions as possible. We analyzed the model using single-unit recording, which enabled a fair comparison with neurophysiological data with as few methodological biases as possible. Interestingly, our results imply that frequency decomposition in the inner ear might not be necessary for processing amplitude modulation. This implication could not have been obtained if we had used a model that assumes frequency decomposition.

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Functional magnetic resonance imaging (fMRI) adaptation (also known as fMRI repetition suppression) has been widely used to characterize stimulus selectivity in vivo, a fundamental feature of neuronal processing in the brain. We investigated whether SZ patients and BD patients show aberrant fMRI adaptation for object perception. About 52 SZ patients, 55 BD patients, and 53 community controls completed an object discrimination task with three conditions: the same object presented twice, two exemplars from the same category, and two exemplars from different categories. We also administered two functional localizer tasks. A region of interest analysis was employed to evaluate a priori hypotheses about the lateral occipital complex (LOC) and early visual cortex (EVC). An exploratory whole brain analysis was also conducted. In the LOC and EVC, controls showed the expected reduced fMRI responses to repeated presentation of the same objects compared with different objects (i.e., fMRI adaptation for objects, p < .001). SZ patients showed an adaptation effect that was significantly smaller compared with controls. BD patients showed a lack of fMRI adaptation. The whole brain analyses showed enhanced fMRI responses to repeated presentation of the same objects only in BD patients in several brain regions including anterior cingulate cortex. This study was the first to employ fMRI adaptation for objects in SZ and BD. The current findings provide empirical evidence of aberrant fMRI adaptation in the visual cortex in SZ and BD, but in distinctly different ways.

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An important question for neural encoding is what kind of neural systems can convey more information with less energy within a finite time coding window. This paper first proposes a finite-time neural encoding system, where the neurons in the system respond to a stimulus by a sequence of spikes that is assumed to be Poisson process and the external stimuli obey normal distribution. A method for calculating the mutual information of the finite-time neural encoding system is proposed and the definition of information efficiency is introduced. The values of the mutual information and the information efficiency obtained by using Logistic function are compared with those obtained by using other functions and it is found that Logistic function is the best one. It is further found that the parameter representing the steepness of the Logistic function has close relationship with full entropy, and that the parameter representing the translation of the function associates with the energy consumption and noise entropy tightly. The optimum parameter combinations for Logistic function to maximize the information efficiency are calculated when the stimuli and the properties of the encoding system are varied respectively. Some explanations for the results are given. The model and the method we proposed could be useful to study neural encoding system, and the optimum neural tuning curves obtained in this paper might exhibit some characteristics of a real neural system.

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Adult face perception mechanisms are tuned to upright faces, and this orientation selectivity is central to adult expertise with upright faces. Children are less expert than adults, and it has been argued that their face mechanisms are less orientation selective than those of adults. Here we used face aftereffects to test this hypothesis by examining whether children's aftereffects show greater transfer across changes in orientation than do adults' aftereffects. We adapted 7- to 8-year-old children and adults to figural face distortions in both upright and inverted orientations and examined the size of resulting aftereffects in both upright and inverted test faces. If children's face mechanisms are less orientation selective than those of adults, then children's aftereffects should transfer more strongly across changes in orientation. We found no evidence to support this prediction. Children's and adults' aftereffects were similarly reduced by orientation differences between adapt and test. These results indicate that children, like adults, show a high degree of orientation selectivity in face shape coding and suggest that neural tuning to face orientation may be mature by 8 years of age. Our findings are consistent with an emerging view that many of the key attributes of specialized face perception emerge much earlier in development than previously thought.

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During the process of skill learning, synaptic connections in our brains are modified to form motor memories of learned sensorimotor acts. The more plastic the adult brain is, the easier it is to learn new skills or adapt to neurological injury. However, if the brain is too plastic and the pattern of synaptic connectivity is constantly changing, new memories will overwrite old memories, and learning becomes unstable. This trade-off is known as the stability-plasticity dilemma. Here a theory of sensorimotor learning and memory is developed whereby synaptic strengths are perpetually fluctuating without causing instability in motor memory recall, as long as the underlying neural networks are sufficiently noisy and massively redundant. The theory implies two distinct stages of learning--preasymptotic and postasymptotic--because once the error drops to a level comparable to that of the noise-induced error, further error reduction requires altered network dynamics. A key behavioral prediction derived from this analysis is tested in a visuomotor adaptation experiment, and the resultant learning curves are modeled with a nonstationary neural network. Next, the theory is used to model two-photon microscopy data that show, in animals, high rates of dendritic spine turnover, even in the absence of overt behavioral learning. Finally, the theory predicts enhanced task selectivity in the responses of individual motor cortical neurons as the level of task expertise increases. From these considerations, a unique interpretation of sensorimotor memory is proposed--memories are defined not by fixed patterns of synaptic weights but, rather, by nonstationary synaptic patterns that fluctuate coherently.

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