Cortico-hippocampal interaction and adaptive stimulus representation: a neurocomputational theory of associative learning and memory

The limited role of hippocampal declarative memory in transient semantic activation during online language processing

Recent findings point to a role for hippocampus in the moment-by-moment processing of language, including the use and generation of semantic features in certain contexts. What role the hippocampus might play in the processing of semantic relations in spoken language comprehension, however, is unknown. Here we test patients with bilateral hippocampal damage and dense amnesia in order to examine the necessity of hippocampus for lexico-semantic mapping processes in spoken language understanding. In two visual-world eye-tracking experiments, we monitor eye movements to images that are semantically related to spoken words and sentences. We find no impairment in amnesia, relative to matched healthy comparison participants. These findings suggest, at least for close semantic links and simple language comprehension tasks, a lack of necessity for hippocampus in lexico-semantic mapping between spoken words and simple pictures.

Differential Representations of Perceived and Retrieved Visual Information in Hippocampus and Cortex

Memory retrieval is thought to depend on interactions between hippocampus and cortex, but the nature of representation in these regions and their relationship remains unclear. Here, we performed an ultra-high field fMRI (7T) experiment, comprising perception, learning and retrieval sessions. We observed a fundamental difference between representations in hippocampus and high-level visual cortex during perception and retrieval. First, while object-selective posterior fusiform cortex showed consistent responses that allowed us to decode object identity across both perception and retrieval one day after learning, object decoding in hippocampus was much stronger during retrieval than perception. Second, in visual cortex but not hippocampus, there was consistency in response patterns between perception and retrieval, suggesting that substantial neural populations are shared for both perception and retrieval. Finally, the decoding in hippocampus during retrieval was not observed when retrieval was tested on the same day as learning suggesting that the retrieval process itself is not sufficient to elicit decodable object representations. Collectively, these findings suggest that while cortical representations are stable between perception and retrieval, hippocampal representations are much stronger during retrieval, implying some form of reorganization of the representations between perception and retrieval.

Green model to adapt classical conditioning learning in the hippocampus

Compared with the biological paradigms of classical conditioning, non-adaptive computational models are not capable of realistically simulating the biological behavioural functions of the hippocampal regions, because of their implausible requirement for a large number of learning trials, which can be on the order of hundreds. Additionally, these models did not attain a unified, final stable state even after hundreds of learning trials. Conversely, the output response has a different threshold for similar tasks in various models with prolonged transient response of unspecified status via the training or even testing phases. Accordingly, a green model is a combination of adaptive neuro-computational hippocampal and cortical models that is proposed by adaptively updating the whole weights in all layers for both intact networks and lesion networks using instar and outstar learning rules with adaptive resonance theory (ART). The green model sustains and expands the classical conditioning biological paradigms of the non-adaptive models. The model also overcomes the irregular output response behaviour by using the proposed feature of adaptivity. Further, the model successfully simulates the hippocampal regions without passing the final output response back to the whole network, which is considered to be biologically implausible. The results of the Green model showed a significant improvement confirmed by empirical studies of different tasks. In addition, the results indicated that the model outperforms the previously published models. All the obtained results successfully and quickly attained a stable, desired final state (with a unified concluding state of either "1" or "0") with a significantly shorter transient duration.

Hippocampal-neocortical interactions sharpen over time for predictive actions

When an action is familiar, we are able to anticipate how it will change the state of the world. These expectations can result from retrieval of action-outcome associations in the hippocampus and the reinstatement of anticipated outcomes in visual cortex. How does this role for the hippocampus in action-based prediction change over time? We use high-resolution fMRI and a dual-training behavioral paradigm to examine how the hippocampus interacts with visual cortex during predictive and nonpredictive actions learned either three days earlier or immediately before the scan. Just-learned associations led to comparable background connectivity between the hippocampus and V1/V2, regardless of whether actions predicted outcomes. However, three-day-old associations led to stronger background connectivity and greater differentiation between neural patterns for predictive vs. nonpredictive actions. Hippocampal prediction may initially reflect indiscriminate binding of co-occurring events, with action information pruning weaker associations and leading to more selective and accurate predictions over time.

In familiar environments, humans automatically anticipate the sensory consequences of their motor actions. Here, the authors show how action-based predictions arise from interactions between the hippocampus and visual cortex, and how these interactions strengthen and weaken over time.

Hyper-SUMOylation of K+ Channels in Sudden Unexplained Death in Epilepsy: Isolation and Primary Culture of Dissociated Hippocampal Neurons from Newborn Mice for Subcellular Localization

The physiological characteristics of rat and murine hippocampal neurons are widely studied, especially because of the involvement of the hippocampus in learning, memory, and neurological functions. Primary cultures of hippocampal neurons are commonly used to discover cellular and molecular mechanisms in neurobiology. By isolating and culturing individual hippocampal neurons, neuroscientists are able to investigate the activity of neurons at the individual cell and single synapse level, and to analyze properties related to cellular structure, cellular trafficking, and individual protein subcellular localization or protein-protein interaction using a variety of biochemical techniques. Conclusions addressed from such research are critical for testing theories related to memory, learning, and neurological functions. Here, we will describe how to isolate and culture primary hippocampal cells from newborn mice. The hippocampus may be isolated from newborn mice in as short as 2 min, and the cell cultures can be maintained for up to 2 weeks, and then ready for investigation of subcellular localization of K⁺ channel proteins and interaction with SUMO-specific protease 2 (SENP2). The protocol provides a fast and efficient technique for the culture of neuronal cells from mouce hippocampal tissue, and will ensure the immunocytochemistry detection of subcellular localization or protein-protein interactions in neurological research.

Situating Human Sexual Conditioning

Conditioning is often thought of as a basic, automatic learning process that has limited applicability to higher-level human behavior. In addition, conditioning is seen as separable from, and even secondary to, "innate" processes. These ideas involve some misconceptions. The aim of this article is to provide a clearer, more refined sense of human sexual conditioning. After providing some background information and reviewing what is known from laboratory conditioning studies, human sexual conditioning is compared to sexual conditioning in nonhumans, to "innate" sexual responding, and to other types of human learning processes. Recommendations for moving forward in human sexual conditioning research are included.

Role of hippocampus in polymodal-cue guided tasks in rats

To examine how signals from different sensory modalities are integrated to generate an appropriate goal-oriented behavior, we trained rats in an eight-arm radial maze to visit a cue arm provided with intramaze cues from different sensory modalities, i.e. visual, tactile and auditory, in order to obtain a reward. When the same rats were then examined on test trials in which the cue arm contained one of the stimuli that the animals were trained with (i.e. light, sound or rough sheet), they showed a significant impairment with respect to the performance on the polymodal-cue task. The contribution of the dorsal hippocampus to the acquisition and retention of polymodal-cue guided task was also examined. We found that rats with dorsal hippocampal lesions before training showed a significant deficit in the acquisition of polymodal-cue oriented task that improved with overtraining. The selective lesion of the dorsal hippocampus after training disrupted memory retention, but the animals' performance improved following retraining of the polymodal task. All hippocampal lesioned rats displayed an impaired performance on the unimodal test. These findings suggest that the dorsal hippocampus contributes to the processing of multimodal sensory information for the associative memory formation and consolidation.

Hippocampal BOLD response during category learning predicts subsequent performance on transfer generalization

To test a prediction of our previous computational model of cortico-hippocampal interaction (Gluck and Myers [1993, 2001]) for characterizing individual differences in category learning, we studied young healthy subjects using an fMRI-adapted category-learning task that has two phases, an initial phase in which associations are learned through trial-and-error feedback followed by a generalization phase in which previously learned rules can be applied to novel associations (Myers et al. [2003]). As expected by our model, we found a negative correlation between learning-related hippocampal responses and accuracy during transfer, demonstrating that hippocampal adaptation during learning is associated with better behavioral scores during transfer generalization. In addition, we found an inverse relationship between Blood Oxygenation Level Dependent (BOLD) activity in the striatum and that in the hippocampal formation and the orbitofrontal cortex during the initial learning phase. Conversely, activity in the dorsolateral prefrontal cortex, orbitofrontal cortex and parietal lobes dominated over that of the hippocampal formation during the generalization phase. These findings provide evidence in support of theories of the neural substrates of category learning which argue that the hippocampal region plays a critical role during learning for appropriately encoding and representing newly learned information so that that this learning can be successfully applied and generalized to subsequent novel task demands.

Repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex improves probabilistic category learning

Traditionally, it has been thought that probabilistic category learning, one of the implicit memory functions, is dependent on the basal ganglia. However, there is growing evidence indicating the involvement of the dorsolateral prefrontal cortex (DLPFC) in probabilistic category learning. In order to identify the causal role of DLPFC in probabilistic category learning, we investigated whether repetitive transcranial magnetic stimulation (rTMS) over the left DLPFC influences the learning ability in healthy subjects using the weather prediction task (WPT). Application of 10 Hz rTMS over the left DLPFC induced significant improvement in the total hit rate during the total trials of the WPT, compared with sham stimulation. Specifically, the improvement was significant in the early and late learning blocks of the WPT, but not in the intermediate block of learning. These results indicate that the left DLPFC may play an important role in probabilistic category learning, possibly by influencing not only on embodied information application in late stage of the learning but also on memory encoding for working memory demands in early stage of the learning via frontostriatal and frontohippocampal circuits, respectively. They also may lend support to the possibility that rTMS can improve implicit learning ability in patients with basal ganglia disorders.

Isolation and culture of hippocampal neurons from prenatal mice

Primary cultures of rat and murine hippocampal neurons are widely used to reveal cellular mechanisms in neurobiology. By isolating and growing individual neurons, researchers are able to analyze properties related to cellular trafficking, cellular structure and individual protein localization using a variety of biochemical techniques. Results from such experiments are critical for testing theories addressing the neural basis of memory and learning. However, unambiguous results from these forms of experiments are predicated on the ability to grow neuronal cultures with minimum contamination by other brain cell types. In this protocol, we use specific media designed for neuron growth and careful dissection of embryonic hippocampal tissue to optimize growth of healthy neurons while minimizing contaminating cell types (i.e. astrocytes). Embryonic mouse hippocampal tissue can be more difficult to isolate than similar rodent tissue due to the size of the sample for dissection. We show detailed dissection techniques of hippocampus from embryonic day 19 (E19) mouse pups. Once hippocampal tissue is isolated, gentle dissociation of neuronal cells is achieved with a dilute concentration of trypsin and mechanical disruption designed to separate cells from connective tissue while providing minimum damage to individual cells. A detailed description of how to prepare pipettes to be used in the disruption is included. Optimal plating densities are provided for immuno-fluorescence protocols to maximize successful cell culture. The protocol provides a fast (approximately 2 hr) and efficient technique for the culture of neuronal cells from mouse hippocampal tissue.

Surprised at all the entropy: hippocampal, caudate and midbrain contributions to learning from prediction errors

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts.

Shared impairment in associative learning in schizophrenia and bipolar disorder

BACKGROUND

Schizophrenia (SCZ) and bipolar disorder (BD) share some cognitive commonalities. However, the role of associative learning, which is a cornerstone of human cognition mainly relying on hippocampus, has been under-investigated. We assessed behavioral performance during associative learning in a group of SCZ, BD and healthy controls (HC).

METHODS

Nineteen patients with SCZ (36 ± 8.1 years; 13 males, 6 females; all Caucasians), 14 patients with BD (41 ± 9.6 years; 5 males, 9 females; all Caucasians) and 45 HC (27.7 ± 6.9 years; 18 males, 27 females; all Caucasians) were studied. Learning was assessed using an established object-location paired-associative learning paradigm. Subjects learned associations between nine equi-familiar common objects and locations in a nine-location grid. Performance data were analyzed in a repeated measures analysis of variance with time (repeated) and group as factors.

RESULTS

Learning curves (performance = (1-e(-k x time)) fitted to average performance data in the three groups revealed lower learning rates in SCZ and BD (k = 0.17 and k = 0.34) than HC (k = 0.78). Significant effects of group (F = 11.05, p < 0.001) and time (F = 122.06, p < 0.001) on learning performance were observed.

CONCLUSIONS

Our study showed that associative learning is impaired in both SCZ and BD, being potentially not affected by medication. Future studies should investigate the neural substrates of learning deficits in SCZ and BD, particularly focusing on hippocampus function and glutamatergic transmission.

Computational cognitive models of prefrontal-striatal-hippocampal interactions in Parkinson's disease and schizophrenia

Disruption to different components of the prefrontal cortex, basal ganglia, and hippocampal circuits leads to various psychiatric and neurological disorders including Parkinson's disease (PD) and schizophrenia. Medications used to treat these disorders (such as levodopa, dopamine agonists, antipsychotics, among others) affect the prefrontal-striatal-hippocampal circuits in a complex fashion. We have built models of prefrontal-striatal and striatal-hippocampal interactions which simulate cognitive dysfunction in PD and schizophrenia. In these models, we argue that the basal ganglia is key for stimulus-response learning, the hippocampus for stimulus-stimulus representational learning, and the prefrontal cortex for stimulus selection during learning about multidimensional stimuli. In our models, PD is associated with reduced dopamine levels in the basal ganglia and prefrontal cortex. In contrast, the cognitive deficits in schizophrenia are associated primarily with hippocampal dysfunction, while the occurrence of negative symptoms is associated with frontostriatal deficits in a subset of patients. In this paper, we review our past models and provide new simulation results for both PD and schizophrenia. We also describe an extended model that includes simulation of the different functional role of D1 and D2 dopamine receptors in the basal ganglia and prefrontal cortex, a dissociation we argue is essential for understanding the non-uniform effects of levodopa, dopamine agonists, and antipsychotics on cognition. Motivated by clinical and physiological data, we discuss model limitations and challenges to be addressed in future models of these brain disorders.

A neural model of hippocampal-striatal interactions in associative learning and transfer generalization in various neurological and psychiatric patients

Building on our previous neurocomputational models of basal ganglia and hippocampal region function (and their modulation by dopamine and acetylcholine, respectively), we show here how an integration of these models can inform our understanding of the interaction between the basal ganglia and hippocampal region in associative learning and transfer generalization across various patient populations. As a common test bed for exploring interactions between these brain regions and neuromodulators, we focus on the acquired equivalence task, an associative learning paradigm in which stimuli that have been associated with the same outcome acquire a functional similarity such that subsequent generalization between these stimuli increases. This task has been used to test cognitive dysfunction in various patient populations with damages to the hippocampal region and basal ganglia, including studies of patients with Parkinson's disease (PD), schizophrenia, basal forebrain amnesia, and hippocampal atrophy. Simulation results show that damage to the hippocampal region-as in patients with hippocampal atrophy (HA), hypoxia, mild Alzheimer's (AD), or schizophrenia-leads to intact associative learning but impaired transfer generalization performance. Moreover, the model demonstrates how PD and anterior communicating artery (ACoA) aneurysm-two very different brain disorders that affect different neural mechanisms-can have similar effects on acquired equivalence performance. In particular, the model shows that simulating a loss of dopamine function in the basal ganglia module (as in PD) leads to slow acquisition learning but intact transfer generalization. Similarly, the model shows that simulating the loss of acetylcholine in the hippocampal region (as in ACoA aneurysm) also results in slower acquisition learning. We argue from this that changes in associative learning of stimulus-action pathways (in the basal ganglia) or changes in the learning of stimulus representations (in the hippocampal region) can have similar functional effects.