Biology subjects

Aquino, T. G.

Publications and source records attributed to Aquino, T. G..

5 recordsLinked to original sources

bioRxiv2026

Task goals reorganize visual codes in human ventral temporal cortex via medial frontal modulation

How sensory representations in the sensory cortex are dynamically and rapidly modulated to support flexible, goal-directed behavior remains a fundamental open question. Using rare simultaneous single-neuron recordings across multiple human brain regions, including the ventral temporal cortex (VTC), a high-level visual area not traditionally associated with context-dependent coding, we show that visual representations in VTC are rapidly reconfigured by the presence or absence of preceding verbal task instructions. Identical visual stimuli evoked distinct responses in VTC depending on whether task instructions were provided or not, with categorical representations sharpened when goals were specified in advance. In contrast, dorsal anterior cingulate cortex (dACC) and the hippocampus carried strong signals related to instruction timing, consistent with known regional specialization in top-down control. Coupling between dACC and VTC increased during periods of instructional uncertainty and high cognitive demand on a rapid timescale during stimulus presentation, and its strength predicted correct performance. Together, these findings uncover a rapid, online feedback mechanism through which the medial frontal cortex dynamically reorganizes sensory population codes in VTC, linking flexible cortical coordination to successful goal-directed computation.

neuroscience↗

bioRxiv2023

Top-down signaling dynamically mediates information processing in biologically inspired RNNs

Recent studies have proposed employing biologically plausible recurrent neural networks (RNNs) to investigate flexible decision-making in the brain. However, the mechanisms underlying the integration of bottom-up sensory inputs and temporally varying top-down factors (such as task instructions and selective attention) remain poorly understood, both within the context of these models and the brain. To address this knowledge gap, we trained biologically inspired RNNs on complex cognitive tasks that require adaptive integration of these factors. Through comprehensive analyses of RNNs and neural activity from mouse primary visual cortex, we show that sensory neurons in low-level areas possess the remarkable ability to multiplex and dynamically combine both bottom-up and top-down information via local inhibitory-to-inhibitory connections. Our results shed light on the role of disinhibitory circuits in the intricate interplay between bottom-up and top-down factors to enable flexible decision processes.

neuroscience↗

bioRxiv2023

Encoding of predictive associations in human prefrontal and medial temporal neurons during Pavlovian conditioning

Pavlovian conditioning is thought to involve the formation of learned associations between stimuli and values, and between stimuli and specific features of outcomes. Here we leveraged human single neuron recordings in ventromedial prefrontal, dorsomedial frontal, hippocampus and amygdala neurons while patients performed a sequential Pavlovian conditioning task containing both stimulus-value and stimulus-stimulus associations. Neurons in the ventromedial prefrontal cortex encoded predictive value along with the amygdala, but also encoded predictions about the identity of stimuli that would subsequently be presented, suggesting a role for neurons in this region in encoding predictive information beyond value. Unsigned error signals were found in dorsomedial prefrontal areas and hippocampus, potentially supporting learning of non-value related outcome features. Our findings implicate distinct human prefrontal and medial temporal neuronal populations in mediating predictive associations which could partially support model-based mechanisms during Pavlovian conditioning. Significance statementPavlovian conditioning is a fundamental form of learning, allowing organisms to associate stimuli and outcomes. Recent Pavlovian work suggests that phenomena such as devaluation sensitivity and sensory preconditioning can be explained by a model-based learning framework. How human neurons perform model-based learning during Pavlovian conditioning is still an open question. We recorded single neurons from epilepsy patients during a two-step Pavlovian conditioning task and found that ventromedial prefrontal neurons encoded expected rewards along with amygdala neurons, but also predicted the identity of upcoming stimuli as required for model-based cognition. Additionally, medial frontal neurons were found to encode error signals that could be used for stimulus-outcome learning. This is the first study mapping model-based computations during Pavlovian conditioning in human neurons.

neuroscience↗

bioRxiv2021

Neurons in human pre-supplementary motor area encode key computations for value-based choice

Adaptive behavior in real-world environments demands that choices integrate over several variables, including the novelty of the options under consideration, their expected value, and uncertainty in value estimation. We recorded neurons from the human pre-supplementary motor area (preSMA), ventromedial prefrontal cortex (vmPFC) and dorsal anterior cingulate to probe how integration over decision variables occurs during decision-making. In contrast to the other areas, preSMA neurons not only represented separate pre-decision variables for each choice option, but also encoded an integrated utility signal and, subsequently, the decision itself. Conversely, post-decision related encoding of variables for the chosen option was more widely distributed and especially prominent in vmPFC. Our findings position the human preSMA as central to the implementation of value-based decisions.

neuroscience↗

bioRxiv2019

Value-related neuronal responses in the human amygdala during observational learning

AbstractThe amygdala plays an important role in many aspects of social-cognition and reward-learning. Here we aimed to determine whether human amygdala neurons are involved in the computations necessary to implement learning through observation. We performed single-neuron recordings from the amygdalae of human neurosurgical patients (male and female) while they learned about the value of stimuli through observing the outcomes experienced by another agent interacting with those stimuli. We used a detailed computational modeling approach to describe patients behavior in the task. Then, using both population-level decoding and single neuron analyses we found evidence to implicate amygdala neurons in two key computations relevant for observational-learning: tracking the expected future reward associated with a given stimulus, and in tracking outcome values received by oneself or other agents. Encoding and decoding analyses suggested observational value coding in amygdala neurons occurred in a different subset of neurons than experiential value coding. Collectively, these findings support a key role for the human amygdala in the computations underlying the capacity for learning through observation. Significance statementSingle neuron studies of the human brain provide a unique window into the computational mechanisms of cognition. In this study, epilepsy patients implanted intracranially with depth microelectrodes performed an observational learning task. We measured activity bilaterally in the amygdala and found a representation for observational rewards as well as observational expected reward values. Additionally, the representation of self-experienced and observational values was performed by distinct subsets of amygdala neurons. This study thus provides a rare glimpse into the role of human amygdala neurons in social cognition.

neuroscience↗