Alcohol-induced damage to the fimbria/fornix reduces hippocampal-prefrontal cortex connection during early abstinence.

INTRODUCTION: Alcohol dependence is characterized by a gradual reduction in cognitive control and inflexibility to contingency changes. The neuroadaptations underlying this aberrant behavior are poorly understood. Using an animal model of alcohol use disorders (AUD) and complementing diffusion-weighted (dw)-MRI with quantitative immunohistochemistry and electrophysiological recordings, we provide causal evidence that chronic intermittent alcohol exposure affects the microstructural integrity of the fimbria/fornix, decreasing myelin basic protein content, and reducing the effective communication from the hippocampus (HC) to the prefrontal cortex (PFC). Using a simple quantitative neural network model, we show how disturbed HC-PFC communication may impede the extinction of maladaptive memories, decreasing flexibility. Finally, combining dw-MRI and psychometric data in AUD patients, we discovered an association between the magnitude of microstructural alteration in the fimbria/fornix and the reduction in cognitive flexibility. Overall, these findings highlight the vulnerability of the fimbria/fornix microstructure in AUD and its potential contribution to alcohol pathophysiology. Fimbria vulnerability to alcohol underlies hippocampal-prefrontal cortex dysfunction and correlates with cognitive impairment.

Nootropic effects of LSD: Behavioral, molecular and computational evidence.

The therapeutic use of classical psychedelic substances such as d-lysergic acid diethylamide (LSD) surged in recent years. Studies in rodents suggest that these effects are produced by increased neural plasticity, including stimulation of the mTOR pathway, a key regulator of metabolism, plasticity, and aging. Could psychedelic-induced neural plasticity be harnessed to enhance cognition? Here we show that LSD treatment enhanced performance in a novel object recognition task in rats, and in a visuo-spatial memory task in humans. A proteomic analysis of human brain organoids showed that LSD affected metabolic pathways associated with neural plasticity, including mTOR. To gain insight into the relation of neural plasticity, aging and LSD-induced cognitive gains, we emulated the experiments in rats and humans with a neural network model of a cortico-hippocampal circuit. Using the baseline strength of plasticity as a proxy for age and assuming an increase in plasticity strength related to LSD dose, the simulations provided a good fit for the experimental data. Altogether, the results suggest that LSD has nootropic effects.

The importance of urgency in decision making based on dynamic information.

A standard view in the literature is that decisions are the result of a process that accumulates evidence in favor of each alternative until such accumulation reaches a threshold and a decision is made. However, this view has been recently questioned by an alternative proposal that suggests that, instead of accumulated, evidence is combined with an urgency signal. Both theories have been mathematically formalized and supported by a variety of decision-making tasks with constant information. However, recently, tasks with changing information have shown to be more effective to study the dynamics of decision making. Recent research using one of such tasks, the tokens task, has shown that decisions are better described by an urgency mechanism than by an accumulation one. However, the results of that study could depend on a task where all fundamental information was noiseless and always present, favoring a mechanism of non-integration, such as the urgency one. Here, we wanted to address whether the same conclusions were also supported by an experimental paradigm in which sensory evidence was removed shortly after it was provided, making working memory necessary to properly perform the task. Here, we show that, under such condition, participants' behavior could be explained by an urgency-gating mechanism that low-pass filters the mnemonic information and combines it with an urgency signal that grows with time but not by an accumulation process that integrates the same mnemonic information. Thus, our study supports the idea that, under certain situations with dynamic sensory information, decisions are better explained by an urgency-gating mechanism than by an accumulation one.

Low-dimensional dynamics for working memory and time encoding.

Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear "ramping" component of each neuron's firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.

Hidden Markov Models Predict the Future Choice Better Than a PSTH-Based Method.

Beyond average firing rate, other measurable signals of neuronal activity are fundamental to an understanding of behavior. Recently, hidden Markov models (HMMs) have been applied to neural recordings and have described how neuronal ensembles process information by going through sequences of different states. Such collective dynamics are impossible to capture by just looking at the average firing rate. To estimate how well HMMs can decode information contained in single trials, we compared HMMs with a recently developed classification method based on the peristimulus time histogram (PSTH). The accuracy of the two methods was tested by using the activity of prefrontal neurons recorded while two monkeys were engaged in a strategy task. In this task, the monkeys had to select one of three spatial targets based on an instruction cue and on their previous choice. We show that by using the single trial's neural activity in a period preceding action execution, both models were able to classify the monkeys' choice with an accuracy higher than by chance. Moreover, the HMM was significantly more accurate than the PSTH-based method, even in cases in which the HMM performance was low, although always above chance. Furthermore, the accuracy of both methods was related to the number of neurons exhibiting spatial selectivity within an experimental session. Overall, our study shows that neural activity is better described when not only the mean activity of individual neurons is considered and that therefore, the study of other signals rather than only the average firing rate is fundamental to an understanding of the dynamics of neuronal ensembles.

A Network Activity Reconfiguration Underlies the Transition from Goal to Action.

Neurons in prefrontal cortex (PF) represent mnemonic information about current goals until the action can be selected and executed. However, the neuronal dynamics underlying the transition from goal into specific actions are poorly understood. Here, we show that the goal-coding PF network is dynamically reconfigured from mnemonic to action selection states and that such reconfiguration is mediated by cell assemblies with heterogeneous excitability. We recorded neuronal activity from PF while monkeys selected their actions on the basis of memorized goals. Many PF neurons encoded the goal, but only a minority of them did so across both memory retention and action selection stages. Interestingly, about half of this minority of neurons switched their goal preference across the goal-action transition. Our computational model led us to propose a PF network composed of heterogeneous cell assemblies with single-state and bistable local dynamics able to produce both dynamical stability and input susceptibility simultaneously.

Autocorrelation Structure in the Macaque Dorsolateral, But not Orbital or Polar, Prefrontal Cortex Predicts Response-Coding Strength in a Visually Cued Strategy Task.

In previous work, we studied the activity of neurons in the dorsolateral (PFdl), orbital (PFo), and polar (PFp) prefrontal cortex while monkeys performed a strategy task with 2 spatial goals. A cue instructed 1 of 2 strategies in each trial: stay with the previous goal or shift to the alternative goal. Each trial started with a fixation period, followed by a cue. Subsequently, a delay period was followed by a "go" signal that instructed the monkeys to choose one goal. After each choice, feedback was provided. In this study, we focused on the temporal receptive fields of the neurons, as measured by the decay in autocorrelation (time constant) during the fixation period, and examined the relationship with response and strategy coding. The temporal receptive field in PFdl correlated with the response-related but not with the strategy-related modulation in the delay and the feedback periods: neurons with longer time constants in PFdl tended to show stronger and more prolonged response coding. No such correlation was found in PFp or PFo. These findings demonstrate that the temporal specialization of neurons for temporally extended computations is predictive of response coding, and neurons in PFdl, but not PFp or PFo, develop such predictive properties.

Coding of Self and Other's Future Choices in Dorsal Premotor Cortex during Social Interaction.

Representing others' intentions is central to primate social life. We explored the role of dorsal premotor cortex (PMd) in discriminating between self and others' behavior while two male rhesus monkeys performed a non-match-to-goal task in a monkey-human paradigm. During each trial, two of four potential targets were randomly presented on the right and left parts of a screen, and the monkey or the human was required to choose the one that did not match the previously chosen target. Each agent had to monitor the other's action in order to select the correct target in that agent's own turn. We report neurons that selectively encoded the future choice of the monkey, the human agent, or both. Our findings suggest that PMd activity shows a high degree of self-other differentiation during face-to-face interactions, leading to an independent representation of what others will do instead of entailing self-centered mental rehearsal or mirror-like activities.

Outcome Modulation Across Tasks in the Primate Dorsolateral Prefrontal Cortex.

Animals need to learn and to adapt to new and changing environments so that appropriate actions that lead to desirable outcomes are acquired within each context. The prefrontal cortex (PF) is known to underlie such function that directly implies that the outcome of each response must be represented in the brain for behavioral policies update. However, whether such PF signal is context dependent or it is a general representation beyond the specificity of a context is still unclear. Here, we analyzed the activity of neurons in the dorsolateral PF (PFdl) recorded while two monkeys performed two perceptual magnitude discrimination tasks. Both tasks were well known by the monkeys and unexpected changes did not occur but the difficulty of the task varied from trial to trial and thus the monkeys made mistakes in a proportion of trials. We show a context-independent coding of the response outcome with neurons maintaining similar selectivity in both task contexts. Using a classification method of the neural activity, we also show that the trial outcome could be well predicted from the activity of the same neurons in the two contexts. Altogether, our results provide evidence of high degree of outcome generality in PFdl.

Interference between Space and Time Estimations: From Behavior to Neurons.

Influences between time and space can be found in our daily life in which we are surrounded by numerous spatial metaphors to refer to time. For instance, when we move files from one folder to another in our computer a horizontal line that grows from left to right informs us about the elapsed and remaining time to finish the procedure and, similarly, in our communication we use several spatial terms to refer to time. Although with some differences in the degree of interference, not only space has an influence on time but both magnitudes influence each other. Indeed, since our childhood our estimations of time are influenced by space even when space should be irrelevant and the same occurs when estimating space with time as distractor. Such interference between magnitudes has also been observed in monkeys even if they do not use language or computers, suggesting that the two magnitudes are tightly coupled beyond communication and technology. Imaging and lesion studies have indicated that same brain areas are involved during the processing of both magnitudes and have suggested that rather than coding the specific magnitude itself the brain represents them as abstract concepts. Recent neurophysiological studies in prefrontal cortex, however, have shown that the coding of absolute and relative space and time in this area is realized by independent groups of neurons. Interestingly, instead, a high overlap was observed in this same area in the coding of goal choices across tasks. These results suggest that rather than during perception or estimation of space and time the interference between the two magnitudes might occur, at least in the prefrontal cortex, in a subsequent phase in which the goal has to be chosen or the response provided.

Independent coding of absolute duration and distance magnitudes in the prefrontal cortex.

The estimation of space and time can interfere with each other, and neuroimaging studies have shown overlapping activation in the parietal and prefrontal cortical areas. We used duration and distance discrimination tasks to determine whether space and time share resources in prefrontal cortex (PF) neurons. Monkeys were required to report which of two stimuli, a red circle or blue square, presented sequentially, were longer and farther, respectively, in the duration and distance tasks. In a previous study, we showed that relative duration and distance are coded by different populations of neurons and that the only common representation is related to goal coding. Here, we examined the coding of absolute duration and distance. Our results support a model of independent coding of absolute duration and distance metrics by demonstrating that not only relative magnitude but also absolute magnitude are independently coded in the PF. NEW & NOTEWORTHY: Human behavioral studies have shown that spatial and duration judgments can interfere with each other. We investigated the neural representation of such magnitudes in the prefrontal cortex. We found that the two magnitudes are independently coded by prefrontal neurons. We suggest that the interference among magnitude judgments might depend on the goal rather than the perceptual resource sharing.

Determining Monkey Free Choice Long before the Choice Is Made: The Principal Role of Prefrontal Neurons Involved in Both Decision and Motor Processes.

When choices are made freely, they might emerge from pre-existing neural activity. However, whether neurons in the prefrontal cortex (PF) show this anticipatory effect and, if so, in which part of the process they are involved is still debated. To answer this question, we studied PF activity in monkeys while they performed a strategy task. In this task when the stimulus changed from the previous trial, the monkeys had to shift their response to one of two spatial goals, excluding the one that had been previously selected. Under this free-choice condition, the prestimulus activity of the same neurons that are involved in decision and motor processes predicted future choices. These neurons developed the same goal preferences during the prestimulus presentation as they did later in the decision phase. In contrast, the same effect was not observed in motor-only neurons and it was present but weaker in decision-only neurons. Overall, our results suggest that the PF neuronal activity predicts upcoming actions mainly through the decision-making network that integrate in time decision and motor task aspects.

Event- and time-dependent decline of outcome information in the primate prefrontal cortex.

The prefrontal cortex (PF) is involved in outcome-based flexible adaptation in a dynamically changing environment. The outcome signal dissipates gradually over time, but the temporal dynamics of this dissipation remains unknown. To examine this issue, we analyzed the outcome-related activity of PF neurons in 2 monkeys in a distance discrimination task. The initial prestimulus period of this task varied in duration, allowing us to dissociate the effects of time and event on the decline in previous outcome-related activity -previous correct versus previous error. We observed 2 types of decline in previous outcome representation: PF neurons that ceased to encode the previous outcome as time passed (time-dependent) and neurons that maintained their signal but it decreased rapidly after the occurrence of a new external event (event-dependent). Although the time-dependent dynamics explained the decline in a greater proportion of neurons, the event-dependent decline was also observed in a significant population of neurons.

Evidence against perfect integration of sensory information during perceptual decision making.

Perceptual decision making is often modeled as perfect integration of sequential sensory samples until the accumulated total reaches a fixed decision bound. In that view, the buildup of neural activity during perceptual decision making is attributed to temporal integration. However, an alternative explanation is that sensory estimates are computed quickly with a low-pass filter and combined with a growing signal reflecting the urgency to respond and it is the latter that is primarily responsible for neural activity buildup. These models are difficult to distinguish empirically because they make similar predictions for tasks in which sensory information is constant within a trial, as in most previous studies. Here we presented subjects with a variant of the classic constant-coherence motion discrimination (CMD) task in which we inserted brief motion pulses. We examined the effect of these pulses on reaction times (RTs) in two conditions: 1) when the CMD trials were blocked and subjects responded quickly and 2) when the same CMD trials were interleaved among trials of a variable-motion coherence task that motivated slower decisions. In the blocked condition, early pulses had a strong effect on RTs but late pulses did not, consistent with both models. However, when subjects slowed their decision policy in the interleaved condition, later pulses now became effective while early pulses lost their efficacy. This last result contradicts models based on perfect integration of sensory evidence and implies that motion signals are processed with a strong leak, equivalent to a low-pass filter with a short time constant.

Motor Cost Influences Perceptual Decisions.

Perceptual decision making has been widely studied using tasks in which subjects are asked to discriminate a visual stimulus and instructed to report their decision with a movement. In these studies, performance is measured by assessing the accuracy of the participants' choices as a function of the ambiguity of the visual stimulus. Typically, the reporting movement is considered as a mere means of reporting the decision with no influence on the decision-making process. However, recent studies have shown that even subtle differences of biomechanical costs between movements may influence how we select between them. Here we investigated whether this purely motor cost could also influence decisions in a perceptual discrimination task in detriment of accuracy. In other words, are perceptual decisions only dependent on the visual stimulus and entirely orthogonal to motor costs? Here we show the results of a psychophysical experiment in which human subjects were presented with a random dot motion discrimination task and asked to report the perceived motion direction using movements of different biomechanical cost. We found that the pattern of decisions exhibited a significant bias towards the movement of lower cost, even when this bias reduced performance accuracy. This strongly suggests that motor costs influence decision making in visual discrimination tasks for which its contribution is neither instructed nor beneficial.

An embodied biologically constrained model of foraging: from classical and operant conditioning to adaptive real-world behavior in DAC-X.

Animals successfully forage within new environments by learning, simulating and adapting to their surroundings. The functions behind such goal-oriented behavior can be decomposed into 5 top-level objectives: 'how', 'why', 'what', 'where', 'when' (H4W). The paradigms of classical and operant conditioning describe some of the behavioral aspects found in foraging. However, it remains unclear how the organization of their underlying neural principles account for these complex behaviors. We address this problem from the perspective of the Distributed Adaptive Control theory of mind and brain (DAC) that interprets these two paradigms as expressing properties of core functional subsystems of a layered architecture. In particular, we propose DAC-X, a novel cognitive architecture that unifies the theoretical principles of DAC with biologically constrained computational models of several areas of the mammalian brain. DAC-X supports complex foraging strategies through the progressive acquisition, retention and expression of task-dependent information and associated shaping of action, from exploration to goal-oriented deliberation. We benchmark DAC-X using a robot-based hoarding task including the main perceptual and cognitive aspects of animal foraging. We show that efficient goal-oriented behavior results from the interaction of parallel learning mechanisms accounting for motor adaptation, spatial encoding and decision-making. Together, our results suggest that the H4W problem can be solved by DAC-X building on the insights from the study of classical and operant conditioning. Finally, we discuss the advantages and limitations of the proposed biologically constrained and embodied approach towards the study of cognition and the relation of DAC-X to other cognitive architectures.

Neural variability in premotor cortex is modulated by trial history and predicts behavioral performance.

In the study of decision making, emphasis is placed on different forms of perceptual integration, while the influence of other factors, such as memory, is ignored. In addition, it is believed that the information underlying decision making is carried in the rate of the neuronal response, while its variability is considered unspecific. Here we studied the influence of recent experience on motor decision making by analyzing the activity of neurons in the dorsal premotor area of two monkeys performing a countermanding arm task. We observe that the across-trial variability of the neural response strongly correlates with trial history-dependent changes in reaction time. Using a theoretical model of decision making, we show that a trial history-monitoring signal can explain the observed behavioral and neural modulation. Our study reveals that, in the neural processes that culminate in motor plan maturation, the evidence provided by perception and memory is reflected in mean rate and variance respectively.