Hippocampus, cortex, and basal ganglia: insights from computational models of complementary learning systems

A hippocampus-accumbens code guides goal-directed appetitive behavior

The dorsal hippocampus (dHPC) is a key brain region for the expression of spatial memories, such as navigating towards a learned reward location. The nucleus accumbens (NAc) is a prominent projection target of dHPC and implicated in value-based action selection. Yet, the contents of the dHPC→NAc information stream and their acute role in behavior remain largely unknown. Here, we found that optogenetic stimulation of the dHPC→NAc pathway while mice navigated towards a learned reward location was both necessary and sufficient for spatial memory-related appetitive behaviors. To understand the task-relevant coding properties of individual NAc-projecting hippocampal neurons (dHPC→NAc), we used in vivo dual-color two-photon imaging. In contrast to other dHPC neurons, the dHPC→NAc subpopulation contained more place cells, with enriched spatial tuning properties. This subpopulation also showed enhanced coding of non-spatial task-relevant behaviors such as deceleration and appetitive licking. A generalized linear model revealed enhanced conjunctive coding in dHPC→NAc neurons which improved the identification of the reward zone. We propose that dHPC routes specific reward-related spatial and behavioral state information to guide NAc action selection.

Out of control: computational dynamic control dysfunction in stress- and anxiety-related disorders

Control theory, which has played a central role in technological progress over the last 150 years, has also yielded critical insights into biology and neuroscience. Recently, there has been a surging interest in integrating control theory with computational psychiatry. Here, we review the state of the field of using control theory approaches in computational psychiatry and show that recent research has mapped a neural control circuit consisting of frontal cortex, parietal cortex, and the cerebellum. This basic feedback control circuit is modulated by estimates of reward and cost via the basal ganglia as well as by arousal states coordinated by the insula, dorsal anterior cingulate cortex, amygdala, and locus coeruleus. One major approach within the broader field of control theory, known as proportion-integral-derivative (PID) control, has shown promise as a model of human behavior which enables precise and reliable estimates of underlying control parameters at the individual level. These control parameters correlate with self-reported fear and with both structural and functional variation in affect-related brain regions. This suggests that dysfunctional engagement of stress and arousal systems may suboptimally modulate parameters of domain-general goal-directed control algorithms, impairing performance in complex tasks involving movement, cognition, and affect. Future directions include clarifying the causal role of control deficits in stress- and anxiety-related disorders and developing clinically useful tools based on insights from control theory.

Aberrations in temporal dynamics of cognitive processing induced by Parkinson's disease and Levodopa

The motor symptoms of Parkinson's disease (PD) have been shown to significantly improve by Levodopa. However, despite the widespread adoption of Levodopa as a standard pharmaceutical drug for the treatment of PD, cognitive impairments linked to PD do not show visible improvement with Levodopa treatment. Furthermore, the neuronal and network mechanisms behind the PD-induced cognitive impairments are not clearly understood. In this work, we aim to explain these cognitive impairments, as well as the ones exacerbated by Levodopa, through examining the differential dynamic patterns of the phase-amplitude coupling (PAC) during cognitive functions. EEG data recorded in an auditory oddball task performed by a cohort consisting of controls and a group of PD patients during both on and off periods of Levodopa treatment were analyzed to derive the temporal dynamics of the PAC across the brain. We observed distinguishing patterns in the PAC dynamics, as an indicator of information binding, which can explain the slower cognitive processing associated with PD in the form of a latency in the PAC peak time. Thus, considering the high-level connections between the hippocampus, the posterior and prefrontal cortices established through the dorsal and ventral striatum acting as a modulatory system, we posit that the primary issue with cognitive impairments of PD, as well as Levodopa's cognitive deficit side effects, can be attributed to the changes in temporal dynamics of dopamine release influencing the modulatory function of the striatum.

Spiking neural network-based computational modeling of episodic memory

In this research article, a spiking neural network-based simulation of the hippocampus is performed to model the functionalities of episodic memory. The purpose of the simulation is to find a computational model through the biological architecture of the hippocampus and correct values for their architectural biological parameters to support the episodic memory functionalities. The episodic store of the model is represented by the collection of events, where each event is further subdivided into coactive activities of experience. The model has tried to mimic the three functionalities of episodic memory, which are pattern separation, pattern association, and their recallings. In pattern separation model used the dentate biological connectivity to generate almost different output patterns corresponding to similar input patterns to reduce interference between two similar memory traces so that ambiguity can be reduced during recalling. In pattern association, an STDP based event encoding and forgetting mechanism are used to mimic the encoding function of the CA3 region in which the coactive activities get associated with each other. A decoder is proposed based on CA1, which can answer the stored event related queries. Along with these functionalities model also supports recalling and encoding based forgetting. Experimental work is performed on the model for the given set of events to check for the pattern separation efficiency, pattern completion efficiency and to check the capability of decoding the answer. An empirical analysis of the results is done and compared with the SMRITI model of episodic memory.

Mir125b-2 imprinted in human but not mouse brain regulates hippocampal function and circuit in mice

Genomic imprinting predominantly occurs in the placenta and brain. Few imprinted microRNAs have been identified in the brain, and their functional roles in the brain are not clear. Here we show paternal, but not maternal, expression of MIR125B2 in human but not mouse brain. Moreover, Mir125b-2^m-/p- mice showed impaired learning and memory, and anxiety, whose functions were hippocampus-dependent. Hippocampal granule cells from Mir125b-2^m-/p- mice displayed increased neuronal excitability, increased excitatory synaptic transmission, and decreased inhibitory synaptic transmission. Glutamate ionotropic receptor NMDA type subunit 2A (Grin2a), a key regulator of synaptic plasticity, was physically bound by miR-125b-2 and upregulated in the hippocampus of Mir125b-2^m-/p- mice. Taken together, our findings demonstrate MIR125B2 imprinted in human but not mouse brain, mediated learning, memory, and anxiety, regulated excitability and synaptic transmission in hippocampal granule cells, and affected hippocampal expression of Grin2a. Our work provides functional mechanisms of a species-specific imprinted microRNA in the brain.

NILRNN: A Neocortex-Inspired Locally Recurrent Neural Network for Unsupervised Feature Learning in Sequential Data

Unsupervised feature learning refers to the problem of learning useful feature extraction functions from unlabeled data. Despite the great success of deep learning networks in this task in recent years, both for static and for sequential data, these systems can in general still not compete with the high performance of our brain at learning to extract useful representations from its sensory input. We propose the Neocortex-Inspired Locally Recurrent Neural Network: a new neural network for unsupervised feature learning in sequential data that brings ideas from the structure and function of the neocortex to the well-established fields of machine learning and neural networks. By mimicking connection patterns in the feedforward circuits of the neocortex, our system tries to generalize some of the ideas behind the success of convolutional neural networks to types of data other than images. To evaluate the performance of our system at extracting useful features, we have trained different classifiers using those and other learnt features as input and we have compared the obtained accuracies. Our system has shown to outperform other shallow feature learning systems in this task, both in terms of the accuracies achieved and in terms of how fast the classification task is learnt. The results obtained confirm our system as a state-of-the-art shallow feature learning system for sequential data, and suggest that extending it to or integrating it into deep architectures may lead to new successful networks that are competent at dealing with complex sequential tasks.

The neuroecology of the water-to-land transition and the evolution of the vertebrate brain

The water-to-land transition in vertebrate evolution offers an unusual opportunity to consider computational affordances of a new ecology for the brain. All sensory modalities are changed, particularly a greatly enlarged visual sensorium owing to air versus water as a medium, and expanded by mobile eyes and neck. The multiplication of limbs, as evolved to exploit aspects of life on land, is a comparable computational challenge. As the total mass of living organisms on land is a hundredfold larger than the mass underwater, computational improvements promise great rewards. In water, the midbrain tectum coordinates approach/avoid decisions, contextualized by water flow and by the animal's body state and learning. On land, the relative motions of sensory surfaces and effectors must be resolved, adding on computational architectures from the dorsal pallium, such as the parietal cortex. For the large-brained and long-living denizens of land, making the right decision when the wrong one means death may be the basis of planning, which allows animals to learn from hypothetical experience before enactment. Integration of value-weighted, memorized panoramas in basal ganglia/frontal cortex circuitry, with allocentric cognitive maps of the hippocampus and its associated cortices becomes a cognitive habit-to-plan transition as substantial as the change in ecology. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

Age group differences in learning-related activity reflect task stage, not learning stage

Healthy aging is accompanied by declines in the ability to learn associations between events, even when their relationship cannot be described. Previous functional magnetic resonance imaging (fMRI) studies have attributed these implicit associative learning (IAL) deficits to differential engagement of the hippocampus and basal ganglia in older relative to younger adults in early and late stages of the task, respectively. However, these task stages have been confounded with age group differences in learning performance that emerge later and to a lesser degree in older adults. To disentangle the effects of task stage from learning stage (i.e., when there is significant evidence of learning) on age group differences in the neural substrates of IAL, we acquired fMRI data while 28 younger (20.8 ± 2.3 years) and 22 older (73.6 ± 6.8 years) healthy adults completed the Triplets Learning Task, in which the location of two cues predicted the location of a target with high (HF) or low (LF) frequency. When matched for task stage, results revealed worse learning performance and increased IAL-related activity in the hippocampus during the early stage and in the globus pallidum during the late stage in older relative to younger adults. However, when matched for learning stage, there were no significant age group differences in learning performance or IAL-related activity. Thus, although learning emerges later for older adults, they are engaging similar brain regions as younger adults when learning the associations, suggesting that previous reports of age group differences reflect effects of age on task stage, but not learning stage.

Learning, Neurogenesis, and Effects of Flavonoids on Learning

Learning and memory are two of our mind's most magical abilities. Different brain regions have roles in processing and storing different types of memories. The hippocampus is the part of the brain responsible for receiving information and storing it in the neocortex. One of the most impressive characteristics of the hippocampus is its capacity for neurogenesis, which is a process in which new neurons are produced and then transformed into mature neurons and finally integrated into neural circuits. The neurogenesis process in the hippocampus, an example of neuroplasticity in the adult brain, is believed to aid hippocampal-dependent learning and memory. New neurons are constantly produced in the hippocampus and integrated into the pre-existing neuronal network; this allows old memories already stored in the neocortex to be removed from the hippocampus and replaced with new ones. Factors affecting neurogenesis in the hippocampus may also affect hippocampal-dependent learning and memory. The flavonoids can particularly exert powerful actions in mammalian cognition and improve hippocampal-dependent learning and memory by positively affecting hippocampal neurogenesis.

Regional Differences in S-Nitrosylation in the Cortex, Striatum, and Hippocampus of Juvenile Male Mice

Nitric oxide (NO) is a multifunctional neurotransmitter that plays a major role in neuronal and synaptic functions. S-nitrosylation (SNO), the NO-mediated protein posttransitional modification (PTM), is known to regulate physiological and pathological processes in the brain. However, the physiological role in different neuroanatomical brain regions has not been well investigated. To understand the role of SNO in the brain of juvenile WT mice, we used SNOTRAP technology. We mapped the SNO-proteome in three different neuroanatomical regions: cortex, striatum, and hippocampus. By conducting systems biology analysis, we found that the three brain regions share similar biological processes (BP) including biogenesis and developmental processes. Exclusive and different BP and molecular functions were found for each of the regions. Unraveling the BP and signaling mechanisms of SNO in the cortex, striatum, and hippocampus may help to understand the functional differences between the three regions under physiological conditions.

Reward-predictive representations generalize across tasks in reinforcement learning

In computer science, reinforcement learning is a powerful framework with which artificial agents can learn to maximize their performance for any given Markov decision process (MDP). Advances over the last decade, in combination with deep neural networks, have enjoyed performance advantages over humans in many difficult task settings. However, such frameworks perform far less favorably when evaluated in their ability to generalize or transfer representations across different tasks. Existing algorithms that facilitate transfer typically are limited to cases in which the transition function or the optimal policy is portable to new contexts, but achieving "deep transfer" characteristic of human behavior has been elusive. Such transfer typically requires discovery of abstractions that permit analogical reuse of previously learned representations to superficially distinct tasks. Here, we demonstrate that abstractions that minimize error in predictions of reward outcomes generalize across tasks with different transition and reward functions. Such reward-predictive representations compress the state space of a task into a lower dimensional representation by combining states that are equivalent in terms of both the transition and reward functions. Because only state equivalences are considered, the resulting state representation is not tied to the transition and reward functions themselves and thus generalizes across tasks with different reward and transition functions. These results contrast with those using abstractions that myopically maximize reward in any given MDP and motivate further experiments in humans and animals to investigate if neural and cognitive systems involved in state representation perform abstractions that facilitate such equivalence relations.

Stereotyped, automatized and habitual behaviours: are they similar constructs under the control of the same cerebral areas?

Comprehensive knowledge about higher executive functions of motor control has been covered in the last decades. Critical goals have been targeted through many different technological approaches. An abundant flow of new results greatly progressed our ability to respond at better-posited answers to look more than ever at the challenging neural system functioning. Behaviour is the observable result of the invisible, as complex cerebral functioning. Many pathological states are approached after symptomatology categorisation of behavioural impairments is achieved. Motor, non-motor and psychiatric signs are greatly shared by many neurological/psychiatric disorders. Together with the cerebral cortex, the basal ganglia contribute to the expression of behaviour promoting the correct action schemas and the selection of appropriate sub-goals based on the evaluation of action outcomes. The present review focus on the basic classification of higher motor control functioning, taking into account the recent advances in basal ganglia structural knowledge and the computational model of basal ganglia functioning. We discuss about the basal ganglia capability in executing ordered motor patterns in which any single movement is linked to each other into an action, and many actions are ordered into each other, giving them a syntactic value to the final behaviour. The stereotypic, automatized and habitual behaviour's constructs and controls are the expression of successive stages of rule internalization and categorisation aimed in producing the perfect spatial-temporal control of motor command.

How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning

Despite a growing body of research devoted to the study of how humans encode environmental patterns, there is still no clear consensus about the nature of the neurocognitive mechanisms underpinning statistical learning nor what factors constrain or promote its emergence across individuals, species, and learning situations. Based on a review of research examining the roles of input modality and domain, input structure and complexity, attention, neuroanatomical bases, ontogeny, and phylogeny, ten core principles are proposed. Specifically, there exist two sets of neurocognitive mechanisms underlying statistical learning. First, a "suite" of associative-based, automatic, modality-specific learning mechanisms are mediated by the general principle of cortical plasticity, which results in improved processing and perceptual facilitation of encountered stimuli. Second, an attention-dependent system, mediated by the prefrontal cortex and related attentional and working memory networks, can modulate or gate learning and is necessary in order to learn nonadjacent dependencies and to integrate global patterns across time. This theoretical framework helps clarify conflicting research findings and provides the basis for future empirical and theoretical endeavors.

Neural substrates of mnemonic discrimination: A whole-brain fMRI investigation

INTRODUCTION

A fundamental component of episodic memory is the ability to differentiate new and highly similar events from previously encountered events. Numerous functional magnetic resonance imaging (fMRI) studies have identified hippocampal involvement in this type of mnemonic discrimination (MD), but few studies have assessed MD-related activity in regions beyond the hippocampus. Therefore, the current fMRI study examined whole-brain activity in healthy young adults during successful discrimination of the test phase of the Mnemonic Similarity Task.

METHOD

In the study phase, participants made "indoor"/"outdoor" judgments to a series of objects. In the test phase, they made "old"/"new" judgments to a series of probe objects that were either repetitions from the memory set (targets), similar to objects in the memory set (lures), or novel. We assessed hippocampal and whole-brain activity consistent with MD using a step function to identify where activity to targets differed from activity to lures with varying degrees of similarity to targets (high, low), responding to them as if they were novel.

RESULTS

Results revealed that the hippocampus and occipital cortex exhibited differential activity to repeated stimuli relative to even highly similar stimuli, but only hippocampal activity predicted discrimination performance.

CONCLUSIONS

These findings are consistent with the notion that successful MD is supported by the hippocampus, with auxiliary processes supported by cortex (e.g., perceptual discrimination).

Associations of dietary carbohydrate and fatty acid intakes with cognition among children

OBJECTIVE

To investigate the cross-sectional associations of dietary carbohydrate and fatty acid intakes with cognition in mid-childhood.

DESIGN

Dietary carbohydrate and fatty acid intakes were assessed using 4-d food records, and cognition was evaluated using the Raven's Coloured Progressive Matrices (RCPM) score. The cross-sectional associations of dietary carbohydrate and fatty acid intakes with cognition were investigated using linear regression analyses adjusted for age, sex, body fat percentage, household income, parental education and daily energy intake.

SETTING

The baseline examinations of the Physical Activity and Nutrition in Children study.

PARTICIPANTS

A population-based sample of 487 children (250 boys, 237 girls) aged 6-8 years living in the city of Kuopio, Finland.

RESULTS

A higher dietary intake of fructose (standardised regression coefficient, β = 0·24, P < 0·001), total fibre (β = 0·16, P = 0·02) and soluble fibre (β = 0·15, P = 0·02) was associated with a higher RCPM score in boys. Other dietary carbohydrates and fatty acids, including total carbohydrates, glucose, sucrose, starch, insoluble fibre, total fat, SFAs, MUFAs, PUFAs, palmitic acid (C16), stearic acid (C18), linoleic acid (C18:2), α-linoleic acid (C18:3), arachidonic acid (C20:4), EPA (C20:5n-3) and DHA (C22:6n-6), were not associated with the RCPM score in boys. Dietary carbohydrates or fatty acids were not associated with the RCPM score in girls.

CONCLUSIONS

Higher dietary fructose and fibre intakes were associated with better cognition in boys, but not in girls. Dietary fatty acids were not related to cognition in boys or in girls.

Context-Dependent Behavior in Parkinson’s Disease With Freezing of Gait

Background. Context-dependent behavior is a phenomenon in which people demonstrate superior performance in the context where a motor task was originally learned, but show poorer performance in an unfamiliar context. Previous studies found that people with Parkinson’s disease (PD) demonstrated greater context-dependency than nondisabled adults. Moreover, the frontostriatal circuit appeared to play a role in mediating context-dependent behavior. Neuroimaging studies showed that people with PD and freezing of gait (FoG) had difficulty recruiting the frontostriatal circuit when performing a set-shifting task, known to be mediated by this neural network. Objective. This study aimed to investigate whether individuals with PD and FoG (PD + FoG) would be more context-dependent than those without FoG (PD − FoG). Furthermore, the association between context-dependent behavior and set-shifting ability would be determined. Methods. Sixteen individuals with PD + FoG, 15 participants with PD − FoG, and 15 nondisabled adults (Control) were recruited. The participants practiced 3 numerical sequences, each associated with a specific context. One day following practice, the participants were tested under 2 conditions: the sequence-context associations remained the same as practice or were changed. Set-shifting ability was measured by the Trail Making Test (TMT). Results. Compared to the PD − FoG group, the PD + FoG group showed a greater decrement in normalized motor performance when the sequence-context associations were changed. Context-dependency correlated with the TMT in the PD − FoG group but not in the PD + FoG or Control groups. Conclusion. While people with PD + FoG appeared to be more context-dependent than individuals without FoG, a relationship between context-dependent behavior and set-shifting existed only in those without FoG.

Positive reward prediction errors during decision-making strengthen memory encoding

Dopamine is thought to provide reward prediction error signals to temporal lobe memory systems, but the role of these signals in episodic memory has not been fully characterized. Here we developed an incidental memory paradigm to (i) estimate the influence of reward prediction errors on the formation of episodic memories, (ii) dissociate this influence from surprise and uncertainty, (iii) characterize the role of temporal correspondence between prediction error and memoranda presentation and (iv) determine the extent to which this influence is dependent on memory consolidation. We found that people encoded incidental memoranda more strongly when they gambled for potential rewards. Moreover, the degree to which gambling strengthened encoding scaled with the reward prediction error experienced when memoranda were presented (and not before or after). This encoding enhancement was detectable within minutes and did not differ substantially after 24 h, indicating that it is not dependent on memory consolidation. These results suggest a computationally and temporally specific role for reward prediction error signalling in memory formation.

Muscarinic activity in hippocampus and entorhinal cortex is crucial for spatial and fear memory retrieval

BACKGROUND

Hippocampus and entorhinal cortex are key players of learning and memory. Despite their established role in memory processes, the contribution of muscarinic receptor activity in these brain regions during memory retrieval remains elusive. This study was aimed to assess the role of hippocampal CA1 and medial entorhinal cortex muscarinic receptors in memory retrieval.

METHOD

Mice were implanted with bilateral cannulas in the hippocampus CA1 and medial entorhinal cortex. After recovery they were trained for Morris water maze test, novel object recognition test and contextual fear conditioning. Scopolamine was infused 10 min prior to retrieval test.

RESULTS

Pre-test scopolamine infusion in hippocampal CA1 and medial entorhinal cortex significantly reduced overall exploration of objects (p<0.001). Similarly, pre-retrieval inactivation dorsal hippocampal CA1 and medial entorhinal cortex muscarinic activity caused significant impairment of spatial and fear memories retrieval (p<0.05).

CONCLUSION

These findings showed vital role of muscarinic activity in retrieving hippocampal and entorhinal cortex dependent memories and suggest a possible target for treating retrograde amnesia.

A Population-Based Model of the Temporal Memory in the Hippocampus

Spatial and temporal dimensions are fundamental for orientation, adaptation, and survival of organisms. Hippocampus has been identified as the main neuroanatomical structure involved both in space and time perception and their internal representation. Dorsal hippocampus lesions showed a leftward shift (toward shorter durations) in peak-interval procedures, whereas ventral lesions shifted the peak time toward longer durations. We previously explained hippocampus lesion experimental findings by assuming a topological map model of the hippocampus with shorter durations memorized ventrally and longer durations more dorsal. Here we suggested a possible connection between the abstract topological maps model of the hippocampus that stored reinforcement times in a spatially ordered memory register and the "time cells" of the hippocampus. In this new model, the time cells provide a uniformly distributed time basis that covers the entire to-be-learned temporal duration. We hypothesized that the topological map of the hippocampus stores the weights that reflect the contribution of each time cell to the average temporal field that determines the behavioral response. The temporal distance between the to-be-learned criterion time and the time of the peak activity of each time cell provides the error signal that determines the corresponding weight correction. Long-term potentiation/depression could enhance/weaken the weights associated to the time cells that peak closer/farther to the criterion time. A coincidence detector mechanism, possibly under the control of the dopaminergic system, could be involved in our suggested error minimization and learning algorithm.

Striking a chord with healthy aging: memory system cooperation is related to preserved configural response learning in older adults

Evidence from tasks that primarily tap either hippocampal- or striatal-based memory systems suggests that although these systems often compete for control of behavior, aging is associated with greater cooperation between them. This may stem from altered prefrontal cortex function. Here, we use a configural response task designed to engage both memory systems to test how age affects their interaction with cortical regions including the prefrontal cortex. We found that although older and younger adults learned just as well, older adults showed greater initial activation in cortical networks associated with visuospatial-action mapping and resolving conflict for competing memory representations. Older adults also showed greater functional coupling of the striatum with the left inferior frontal gyrus, in parallel with similar hippocampal coupling to ventral visual regions as young adults. Overall, our results support the proposal that aging is associated with more cooperative memory systems, but we did not find that greater cooperation is associated with less interaction between the prefrontal cortex and core memory system structures during learning.

A neural model of normal and abnormal learning and memory consolidation: adaptively timed conditioning, hippocampus, amnesia, neurotrophins, and consciousness

How do the hippocampus and amygdala interact with thalamocortical systems to regulate cognitive and cognitive-emotional learning? Why do lesions of thalamus, amygdala, hippocampus, and cortex have differential effects depending on the phase of learning when they occur? In particular, why is the hippocampus typically needed for trace conditioning, but not delay conditioning, and what do the exceptions reveal? Why do amygdala lesions made before or immediately after training decelerate conditioning while those made later do not? Why do thalamic or sensory cortical lesions degrade trace conditioning more than delay conditioning? Why do hippocampal lesions during trace conditioning experiments degrade recent but not temporally remote learning? Why do orbitofrontal cortical lesions degrade temporally remote but not recent or post-lesion learning? How is temporally graded amnesia caused by ablation of prefrontal cortex after memory consolidation? How are attention and consciousness linked during conditioning? How do neurotrophins, notably brain-derived neurotrophic factor (BDNF), influence memory formation and consolidation? Is there a common output path for learned performance? A neural model proposes a unified answer to these questions that overcome problems of alternative memory models.

Role of the dorsolateral prefrontal cortex in context-dependent motor performance

Context-dependent motor performance is a phenomenon in which people perform better in the environmental context where they originally practised a task. Some animal and computer simulation studies have suggested that context-dependent performance may be associated with neural activation of the dorsolateral prefrontal cortex (DLPFC). This study aimed to determine the role of the DLPFC in context-dependent motor performance by perturbing the neural processing of the DLPFC with repetitive transcranial magnetic stimulation (rTMS) in healthy adults. Thirty healthy adults were recruited into the Control, rTMS DLPFC and rTMS Vertex groups. The participants practised three finger sequences associated with a specific incidental context (a coloured circle and a location on the computer screen). One day following practice, the rTMS groups received 1 Hz rTMS prior to the testing conditions in which the sequence-context associations remained the same as practice (SAME) or changed (SWITCH). All three groups improved significantly over practice on day 1. The second day testing results showed that the DLPFC group had a significantly lower decrease in motor performance under the SWITCH condition than the Control and Vertex groups. This finding suggests a specific role of the DLPFC in context-dependent motor performance.

Examining Procedural Learning and Corticostriatal Pathways for Individual Differences in Language: Testing Endophenotypes of DRD2/ANKK1

The aim of the study was to explore whether genetic variation in the dopaminergic system is associated with procedural learning and the corticostriatal pathways in individuals with developmental language impairment (DLI). We viewed these two systems as endophenotypes and hypothesized that they would be more sensitive indicators of genetic effects than the language phenotype itself. Thus, we genotyped two SNPs in the DRD2/ANKK1 gene complex, and tested for their associations to the phenotype of DLI and the two endophenotypes. Results showed that individuals with DLI revealed poor procedural learning abilities and abnormal structures of the basal ganglia. Genetic variation in DRD2/ANKK1 was associated with procedural learning abilities and with microstructural differences of the caudate nucleus. The association of the language phenotype with these DRD2/ANKK1 polymorphisms was not significant, but the phenotype was significantly associated with the two endophenotypes. We suggest that procedural learning and the corticostriatal pathways could be used as effective endophenotypes to aid molecular genetic studies searching for genes predisposing to DLI.

Effects of high-fat diet exposure on learning & memory

The associations between consumption of a high-fat or 'Western' diet and metabolic disorders such as obesity, diabetes, and cardiovascular disease have long been recognized and a great deal of evidence now suggests that diets high in fat can also have a profound impact on the brain, behavior, and cognition. Here, we will review the techniques most often used to assess learning and memory in rodent models and discuss findings from studies assessing the cognitive effects of high-fat diet consumption. The review will then consider potential underlying mechanisms in the brain and conclude by reviewing emerging literature suggesting that maternal consumption of a high-fat diet may have effects on the learning and memory of offspring.

Adolescent development of context-dependent stimulus-reward association memory and its neural correlates

Expression of learned stimulus-reward associations based on context is essential for regulation of behavior to meet situational demands. Contextual regulation improves during development, although the developmental progression of relevant neural and cognitive processes is not fully specified. We therefore measured neural correlates of flexible, contextual expression of stimulus-reward associations in pre/early-adolescent children (ages 9-13 years) and young adults (ages 19-22 years). After reinforcement learning using standard parameters, a contextual reversal manipulation was used whereby contextual cues indicated that stimulus-reward associations were the same as previously reinforced for some trials (consistent trials) or were reversed on other trials (inconsistent trials). Subjects were thus required to respond according to original stimulus-reward associations vs. reversed associations based on trial-specific contextual cues. Children and young adults did not differ in reinforcement learning or in relevant functional magnetic resonance imaging (fMRI) correlates. In contrast, adults outperformed children during contextual reversal, with better performance specifically for inconsistent trials. fMRI signals corresponding to this selective advantage included greater activity in lateral prefrontal cortex (LPFC), hippocampus, and dorsal striatum for young adults relative to children. Flexible expression of stimulus-reward associations based on context thus improves via adolescent development, as does recruitment of brain regions involved in reward learning and contextual expression of memory. HighlightsEarly-adolescent children and young adults were equivalent in reinforcement learning.Adults outperformed children in contextual expression of stimulus-reward associations.Adult advantages correlated with increased activity of relevant brain regions.Specific neurocognitive developmental changes support better contextual regulation.

Interaction between basal ganglia and limbic circuits in learning and memory processes

Hippocampus and striatum play distinctive roles in memory processes since declarative and non-declarative memory systems may act independently. However, hippocampus and striatum can also be engaged to function in parallel as part of a dynamic system to integrate previous experience and adjust behavioral responses. In these structures the formation, storage, and retrieval of memory require a synaptic mechanism that is able to integrate multiple signals and to translate them into persistent molecular traces at both the corticostriatal and hippocampal/limbic synapses. The best cellular candidate for this complex synthesis is represented by long-term potentiation (LTP). A common feature of LTP expressed in these two memory systems is the critical requirement of convergence and coincidence of glutamatergic and dopaminergic inputs to the dendritic spines of the neurons expressing this form of synaptic plasticity. In experimental models of Parkinson's disease abnormal accumulation of α-synuclein affects these two memory systems by altering two major synaptic mechanisms underlying cognitive functions in cholinergic striatal neurons, likely implicated in basal ganglia dependent operative memory, and in the CA1 hippocampal region, playing a central function in episodic/declarative memory processes.

The problem of multimodal concurrent serial order in behavior

The "problem of serial order in behavior," as formulated and discussed by Lashley (1951), is arguably more pervasive and more profound both than originally stated and than currently appreciated. We spell out two complementary aspects of what we term the generalized problem of behavior: (i) multimodality, stemming from the disparate nature of the sensorimotor variables and processes that underlie behavior, and (ii) concurrency, which reflects the parallel unfolding in time of these processes and of their asynchronous interactions. We illustrate these on a number of examples, with a special focus on language, briefly survey the computational approaches to multimodal concurrency, offer some hypotheses regarding the manner in which brains address it, and discuss some of the broader implications of these as yet unresolved issues for cognitive science.

Neural computations mediating one-shot learning in the human brain

Incremental learning, in which new knowledge is acquired gradually through trial and error, can be distinguished from one-shot learning, in which the brain learns rapidly from only a single pairing of a stimulus and a consequence. Very little is known about how the brain transitions between these two fundamentally different forms of learning. Here we test a computational hypothesis that uncertainty about the causal relationship between a stimulus and an outcome induces rapid changes in the rate of learning, which in turn mediates the transition between incremental and one-shot learning. By using a novel behavioral task in combination with functional magnetic resonance imaging (fMRI) data from human volunteers, we found evidence implicating the ventrolateral prefrontal cortex and hippocampus in this process. The hippocampus was selectively “switched” on when one-shot learning was predicted to occur, while the ventrolateral prefrontal cortex was found to encode uncertainty about the causal association, exhibiting increased coupling with the hippocampus for high-learning rates, suggesting this region may act as a “switch,” turning on and off one-shot learning as required.

A combination of neuroimaging and computational modeling suggests that a part of ventrolateral prefrontal cortex, in cooperation with the hippocampus, is responsible for switching between incremental and one-shot strategies for learning about causal relationships. Read the Synopsis.

There are at least two distinct learning strategies for identifying the relationship between a cause and its consequence: (1) incremental learning, in which we gradually acquire knowledge through trial and error, and (2) one-shot learning, in which we rapidly learn from only a single pairing of a potential cause and a consequence. Little is known about how the brain switches between these two forms of learning. In this study, we provide evidence that the amount of uncertainty about the relationship between cause and consequence mediates the transition between incremental and one-shot learning. Specifically, the more uncertainty there is about the causal relationship, the higher the learning rate that is assigned to that stimulus. By imaging the brain while participants were performing the learning task, we also found that uncertainty about the causal association is encoded in the ventrolateral prefrontal cortex and that the degree of coupling between this region and the hippocampus increases during one-shot learning. We speculate that this prefrontal region may act as a “switch,” turning on and off one-shot learning as required.

Aberrant functional connectivity in Papez circuit correlates with memory performance in cognitively intact middle-aged APOE4 carriers

The main objective of this study is to detect the early changes in resting-state Papez circuit functional connectivity using the hippocampus as the seed, and to determine the associations between altered functional connectivity (FC) and the episodic memory performance in cognitively intact middle-aged apolipoprotein E4 (APOE4) carriers who are at risk of Alzheimer's disease (AD). Forty-six cognitively intact, middle-aged participants, including 20 APOE4 carriers and 26 age-, sex-, and education-matched noncarriers were studied. The resting-state FC of the hippocampus (HFC) was compared between APOE4 carriers and noncarriers. APOE4 carriers showed significantly decreased FC in brain areas that involve learning and memory functions, including the frontal, cingulate, thalamus and basal ganglia regions. Multiple linear regression analysis showed significant correlations between HFC and the episodic memory performance. Conjunction analysis between neural correlates of memory and altered HFC showed the overlapping regions, especially the subcortical regions such as thalamus, caudate nucleus, and cingulate cortices involved in the Papez circuit. Thus, changes in connectivity in the Papez circuit may be used as an early risk detection for AD.

Dissociable forms of repetition priming: a computational model

Nondeclarative memory and novelty processing in the brain is an actively studied field of neuroscience, and reducing neural activity with repetition of a stimulus (repetition suppression) is a commonly observed phenomenon. Recent findings of an opposite trend-specifically, rising activity for unfamiliar stimuli-question the generality of repetition suppression and stir debate over the underlying neural mechanisms. This letter introduces a theory and computational model that extend existing theories and suggests that both trends are, in principle, the rising and falling parts of an inverted U-shaped dependence of activity with respect to stimulus novelty that may naturally emerge in a neural network with Hebbian learning and lateral inhibition. We further demonstrate that the proposed model is sufficient for the simulation of dissociable forms of repetition priming using real-world stimuli. The results of our simulation also suggest that the novelty of stimuli used in neuroscientific research must be assessed in a particularly cautious way. The potential importance of the inverted-U in stimulus processing and its relationship to the acquisition of knowledge and competencies in humans is also discussed.

Allocating structure to function: the strong links between neuroplasticity and natural selection

A central question in brain evolution is how species-typical behaviors, and the neural function-structure mappings supporting them, can be acquired and inherited. Advocates of brain modularity, in its different incarnations across scientific subfields, argue that natural selection must target domain-dedicated, separately modifiable neural subsystems, resulting in genetically-specified functional modules. In such modular systems, specification of neuron number and functional connectivity are necessarily linked. Mounting evidence, however, from allometric, developmental, comparative, systems-physiological, neuroimaging and neurological studies suggests that brain elements are used and reused in multiple functional systems. This variable allocation can be seen in short-term neuromodulation, in neuroplasticity over the lifespan and in response to damage. We argue that the same processes are evident in brain evolution. Natural selection must preserve behavioral functions that may co-locate in variable amounts with other functions. In genetics, the uses and problems of pleiotropy, the re-use of genes in multiple networks have been much discussed, but this issue has been sidestepped in neural systems by the invocation of modules. Here we highlight the interaction between evolutionary and developmental mechanisms to produce distributed and overlapping functional architectures in the brain. These adaptive mechanisms must be robust to perturbations that might disrupt critical information processing and action selection, but must also recognize useful new sources of information arising from internal genetic or environmental variability, when those appear. These contrasting properties of "robustness" and "evolvability" have been discussed for the basic organization of body plan and fundamental cell physiology. Here we extend them to the evolution and development, "evo-devo," of brain structure.

Vision, reanimated and reimagined

The publication in 1982 of David Marr's Vision has delivered a singular boost and a course correction to the science of vision. Thirty years later, cognitive science is being transformed by the new ways of thinking about what it is that the brain computes, how it does that, and, most importantly, why cognition requires these computations and not others. This ongoing process still owes much of its impetus and direction to the sound methodology, engaging style, and unique voice of Marr's Vision.

Early gray-matter and white-matter concentration in infancy predict later language skills: a whole brain voxel-based morphometry study

Magnetic resonance imaging (MRI) brain scans were obtained from 19 infants at 7 months. Expressive and receptive language performance was assessed at 12 months. Voxel-based morphometry (VBM) identified brain regions where gray-matter and white-matter concentrations at 7 months correlated significantly with children's language scores at 12 months. Early gray-matter concentration in the right cerebellum, early white-matter concentration in the right cerebellum, and early white-matter concentration in the left posterior limb of the internal capsule (PLIC)/cerebral peduncle were positively and strongly associated with infants' receptive language ability at 12 months. Early gray-matter concentration in the right hippocampus was positively and strongly correlated with infants' expressive language ability at 12 months. Our results suggest that the cerebellum, PLIC/cerebral peduncle, and the hippocampus may be associated with early language development. Potential links between these structural predictors and infants' linguistic functions are discussed.

Explicit pre-training instruction does not improve implicit perceptual-motor sequence learning

Memory systems theory argues for separate neural systems supporting implicit and explicit memory in the human brain. Neuropsychological studies support this dissociation, but empirical studies of cognitively healthy participants generally observe that both kinds of memory are acquired to at least some extent, even in implicit learning tasks. A key question is whether this observation reflects parallel intact memory systems or an integrated representation of memory in healthy participants. Learning of complex tasks in which both explicit instruction and practice is used depends on both kinds of memory, and how these systems interact will be an important component of the learning process. Theories that posit an integrated, or single, memory system for both types of memory predict that explicit instruction should contribute directly to strengthening task knowledge. In contrast, if the two types of memory are independent and acquired in parallel, explicit knowledge should have no direct impact and may serve in a "scaffolding" role in complex learning. Using an implicit perceptual-motor sequence learning task, the effect of explicit pre-training instruction on skill learning and performance was assessed. Explicit pre-training instruction led to robust explicit knowledge, but sequence learning did not benefit from the contribution of pre-training sequence memorization. The lack of an instruction benefit suggests that during skill learning, implicit and explicit memory operate independently. While healthy participants will generally accrue parallel implicit and explicit knowledge in complex tasks, these types of information appear to be separately represented in the human brain consistent with multiple memory systems theory.

Early markers of ongoing action-effect learning

Acquiring knowledge about the relationship between stimulus conditions, one’s own actions, and the resulting consequences or effects, is one prerequisite for intentional action. Previous studies have shown that such contextualized associations between actions and their effects (S-R-E associations) can be picked up very quickly. The present study examined how such weakly practiced associations might affect overt behavior during the process of initial learning and during subsequent retrieval, and how these two measures are inter-related. We examined incidental (S-)R-E learning in the context of trial-and-error S-R learning and in the context of instruction-based S-R learning. Furthermore, as a control condition, common outcome (CO) learning blocks were included in which all responses produced one common sound effect, hence precluding differential (S-)R-E learning. Post-learning retrieval of R-E associations was tested by re-using previously produced sound effects as novel imperative stimuli combined with actions that were either compatible or incompatible with the previously encountered R-E mapping. The central result was that the size of the compatibility effect could be predicted by the size of relative response slowing during ongoing learning in the preceding acquisition phase, both in trial-and-error learning and in instruction-based learning. Importantly, this correlation was absent for the CO control condition, precluding accounts based on unspecific factors. Instead, the results suggest that differential outcomes are “actively” integrated into action planning and that this takes additional planning time. We speculate that this might be especially true for weakly practiced (S-)R-E associations before an initial goal-directed action mode transitions into a more stimulus-based action mode.

Monitoring performance degradation of cerebellar functions using computational neuroscience methods: implications on neurological diseases

Neurodegeneration is a major cause of human disease. Within the cerebellum, neuronal degeneration and/or dysfunction has been associated with many diseases, including several forms of cerebellar ataxia, since normal cerebellar function is paramount for proper motor coordination, balance, and motor learning. The cerebellum represents a well-established neural circuit. Determining the effects of neuronal loss is of great importance for understanding the fundamental workings of the cerebellum and disease-associated dysfunctions. This paper presents computational modeling of cerebellar function in relation to neurodegeneration either affecting a specific cerebellar cell type, such as granule cells or Purkinje cells, or more generally affecting cerebellar cells and the implications on effects in relation to performance degradation throughout the progression of cell death. The results of the models show that the overall number of cells, as a percentage of the total cell number in the model, of a particular type and, primarily, their proximity to the circuit output, and not the neuronal convergence due to the relative number of cells of a particular type, is the main indicator of the gravity of the functional deficit caused by the degradation of that cell type. Specifically, the greater the percentage loss of neurons of a specific type and the closer proximity of those cells to the deep cerebellar neurons, the greater the deficit caused by the neuronal cell loss. These findings contribute to the understanding of the functional consequences of neurodegeneration and the functional importance of specific connectivity within a neuronal circuit.

Dissociable neural systems of sequence learning

Although current theories all point to distinct neural systems for sequence learning, no consensus has been reached on which factors crucially define this distinction. Dissociable judgment-linked versus motor-linked and implicit versus explicit neural systems have been proposed. This paper reviews these two distinctions, yet concludes that these traditional dichotomies prove insufficient to account for all data on sequence learning and its neural organization. Instead, a broader theoretical framework is necessary providing a more continuous means of dissociating sequence learning systems. We argue that a more recent theory, dissociating multidimensional versus unidimensional neural systems, might provide such framework, and we discuss this theory in relation to more general principles of associative learning and recent imaging findings.