Integrating Incremental Learning and Episodic Memory Models of the Hippocampal Region

Sexual offenses and the brain

Sexual offenses are a great concern worldwide due to the devastating physical and psychological consequences the victims of such crimes often experience. It is an important task to investigate potential mechanisms that may underlie sexual offending and predispose an individual for such antisocial behavior. Advanced techniques in neuroscience are increasingly used to uncover biomarkers in psychiatric disorders and organic brain disease. As this type of research is flourishing, preliminary studies with the aim to explore the neural basis of sex offenders have started to appear. To this purpose, researchers began to study the brain's structural and functional changes and the neurocognitive profiles of sex offenders, in comparison to nonoffenders and nonsex offenders, or among different subtypes of sex offenders. Most of the research to date has focused on pedophilia, with some inconsistent findings, which hampers the translation of the results into the forensic and clinical context. Any attempt to increase convergent evidence may profit from the harmonization of data analysis and avoidance of methodological inconsistencies, which can account for the different results across studies. Today, uncovering the neural basis of sex offences has to become a priority, not only for clinical interventions, but might also be important knowledge for crime prevention.

Mapping Mind-Brain Development: Towards a Comprehensive Theory

The relations between the developing mind and developing brain are explored. We outline a theory of intellectual development postulating that the mind comprises four systems of processes (domain-specific, attention and working memory, reasoning, and cognizance) developing in four cycles (episodic, realistic, rule-based, and principle-based representations, emerging at birth, 2, 6, and 11 years, respectively), with two phases in each. Changes in reasoning relate to processing efficiency in the first phase and working memory in the second phase. Awareness of mental processes is recycled with the changes in each cycle and drives their integration into the representational unit of the next cycle. Brain research shows that each type of processes is served by specialized brain networks. Domain-specific processes are rooted in sensory cortices; working memory processes are mainly rooted in hippocampal, parietal, and prefrontal cortices; abstraction and alignment processes are rooted in parietal, frontal, and prefrontal and medial cortices. Information entering these networks is available to awareness processes. Brain networks change along the four cycles, in precision, connectivity, and brain rhythms. Principles of mind-brain interaction are discussed.

A Roadmap for Understanding Memory: Decomposing Cognitive Processes into Operations and Representations

Thanks to patients Phineas Gage and Henry Molaison, we have long known that behavioral control depends on the frontal lobes, whereas declarative memory depends on the medial temporal lobes (MTL). For decades, cognitive functions-behavioral control, declarative memory-have served as labels for characterizing the division of labor in cortex. This approach has made enormous contributions to understanding how the brain enables the mind, providing a systems-level explanation of brain function that constrains lower-level investigations of neural mechanism. Today, the approach has evolved such that functional labels are often applied to brain networks rather than focal brain regions. Furthermore, the labels have diversified to include both broadly-defined cognitive functions (declarative memory, visual perception) and more circumscribed mental processes (recollection, familiarity, priming). We ask whether a process-a high-level mental phenomenon corresponding to an introspectively-identifiable cognitive event-is the most productive label for dissecting memory. For example, recollection conflates a neurocomputational operation (pattern completion-based retrieval) with a class of representational content (associative, high-dimensional memories). Because a full theory of memory must identify operations and representations separately, and specify how they interact, we argue that processes like recollection constitute inadequate labels for characterizing neural mechanisms. Instead, we advocate considering the component operations and representations of processes like recollection in isolation. For the organization of memory, the evidence suggests that pattern completion is recapitulated widely across the ventral visual stream and MTL, but the division of labor between sites within this pathway can be explained by representational content.

Unexplored territory: Beneficial effects of novelty on memory

Exploring novel environments enhances learning in animals. Due to differing traditions, research into the effects of spatial novelty on learning in humans is scarce. Recent developments of affordable and fMRI-compatible virtual reality (VR) and mobile EEG systems can help bridge the gap between the two literatures. One promising study showed that spatial novelty also promotes learning in humans. It still remains largely unknown, however, which aspect of novelty underlies the beneficial effect on memory, as novelty, expectations, and volition are often confounded in animal studies. In humans, these factors can be experimentally manipulated, but such studies are currently lacking. Future studies in humans could combine pharmacological interventions, neuroimaging and VR or use mobile EEG to help elucidate whether the plasticity enhancing mechanisms observed in animals, also exist in humans. When the aspects of exploring a novel environment underlying beneficial memory effects have been identified, effective novelty-exposure interventions could be designed to improve learning and counteract age-related memory decline.

How to Characterize the Function of a Brain Region

Many brain regions have been defined, but a comprehensive formalization of each region's function in relation to human behavior is still lacking. Current knowledge comes from various fields, which have diverse conceptions of 'functions'. We briefly review these fields and outline how the heterogeneity of associations could be harnessed to disclose the computational function of any region. Aggregating activation data from neuroimaging studies allows us to characterize the functional engagement of a region across a range of experimental conditions. Furthermore, large-sample data can disclose covariation between brain region features and ecological behavioral phenotyping. Combining these two approaches opens a new perspective to determine the behavioral associations of a brain region, and hence its function and broader role within large-scale functional networks.

The implicit learning of metrical and non-metrical rhythms in blind and sighted adults

Forming temporal expectancies plays a crucial role in our survival as it allows us to identify the occurrence of temporal deviants that might signal potential dangers. The dynamic attending theory suggests that temporal expectancies are formed more readily for rhythms that imply a beat (i.e., metrical rhythms) compared to those that do not (i.e., nonmetrical rhythms). Moreover, metrical frameworks can be used to detect temporal deviants. Although several studies have demonstrated that congenital or early blindness correlates with modality-specific neural changes that reflect compensatory mechanisms, few have examined whether blind individuals show a learning advantage for auditory rhythms and whether learning can occur unintentionally and without awareness, that is, implicitly. We compared blind to sighted controls in their ability to implicitly learn metrical and nonmetrical auditory rhythms. We reasoned that the loss of sight in blindness might lead to improved sensitivity to rhythms and predicted that the blind learn rhythms more readily than the sighted. We further hypothesized that metrical rhythms are learned more readily than nonmetrical rhythms. Results partially confirmed our predictions; the blind group learned nonmetrical rhythms more readily than the sighted group but the blind group learned metrical rhythms less readily than the sighted group. Only the sighted group learned metrical rhythms more readily than nonmetrical rhythms. The blind group demonstrated awareness of the nonmetrical rhythms while learning was implicit for all other conditions. Findings suggest that improved deviant-sensitivity might have provided the blind group a learning advantage for nonmetrical rhythms. Future research could explore the plastic changes that affect deviance-detection and stimulus-specific adaptation in blindness.

Contextual modulation of hippocampal activity during picture naming

Picture naming is a standard task used to probe language processes in healthy and impaired speakers. It recruits a broad neural network of language related areas, among which the hippocampus is rarely included. However, the hippocampus could play a role during picture naming, subtending, for example, implicit learning of the links between pictured objects and their names. To test this hypothesis, we recorded hippocampal activity during plain picture naming, without memorization requirement; we further assessed whether this activity was modulated by contextual factors such as repetition priming and semantic interference. Local field potentials recorded from intracerebral electrodes implanted in the healthy hippocampi of epileptic patients revealed a specific and reliable pattern of activity, markedly modulated by repetition priming and semantic context. These results indicate that the hippocampus is recruited during picture naming, presumably in relation to implicit learning, with contextual factors promoting differential hippocampal processes, possibly subtended by different sub-circuitries.

Responses of neurons in the medial temporal lobe during encoding and recognition of face-scene pairs

Associations between co-occurring stimuli are formed in the medial temporal lobe (MTL). Here, we recorded from 508 single and multi-units in the MTL while participants learned and retrieved associations between unfamiliar faces and unfamiliar scenes. Participant's memories for the face-scene pairs were later tested using cued recall and recognition tests. The results show that neurons in the parahippocampal cortex are most likely to respond with changes from baseline firing to these stimuli during both encoding and recognition, and this region showed the greatest proportion of cells showing differential responses depending on the phase of the task. Furthermore, we found that cells in the parahippocampal cortex that responded during both encoding and recognition were more likely to show decreases from baseline firing than cells that were only recruited during recognition, which were more likely to show increases in firing. Since all stimuli shown during recognition were familiar to the patients, these findings suggest that with familiarity, cell responses become more sharply tuned. No neurons in this region, however, were found to be affected by recombining face/scene pairs. Neurons in other MTL regions, particularly the hippocampus, were sensitive to stimulus configurations. Thus, the results support the idea that neurons in the parahippocampal cortex code for features of stimuli and neurons in the hippocampus are more likely to represent their specific configurations.

Abnormal white matter integrity in rapists as indicated by diffusion tensor imaging

BACKGROUND

Recent research has documented structural brain abnormalities in various criminal offenders. However, there have been few brain imaging studies of sex offenders, and none on white matter integrity. The current study tested the hypothesis that rapists, when compared to matched controls, would show abnormal cortical and subcortical white matter integrity.

RESULTS

Rapists showed significantly increased fractional anisotropy in the internal capsul e in the thalamus, caudate, and globus pallidus, and also in white matter tracts near the angular gyrus, posterior cingulate, frontal pole, lateral occipital cortex, and genu compared to controls matched for age, gender, and educational status. Reduced fractional anisotropy was observed in rapists in the posterior cingulum and in the inferior fronto-occipital fasciculus.

CONCLUSIONS

To our knowledge, this is the first study indicating white matter abnormalities in rapists. Findings indicate abnormalities in white matter connectivity in brain regions involved in reward/motivation and moral judgment, which may predispose rapists to be both over-responsive to sexual reward stimuli and also to make inappropriate moral decisions.

Hitting the reset button: An ERP investigation of memory for temporal context

This study explored how temporal context influences recognition. In an ERP experiment, subjects were asked to judge whether pictures, presented one at a time, had been seen since the previous appearance of a special reset screen. The reset screen separated sequences of successively presented stimuli and signaled a change in temporal context. A "new-repeat" picture was one that had been seen before but was to be called "new" because it had not appeared since the previous reset screen. New-repeat pictures elicited a more negative FN400 component than did "old" pictures even though both had seen before during the experiment. This suggests that familiarity, as indexed by the FN400, is sensitive to temporal context. An earlier frontopolar old/new effect distinguished pictures that were seen for the first time in the experiment from all other pictures. The late positive component (LPC), which is typically greater for old stimuli, was smaller for new-repeat pictures than for pictures seen for the first time in the experiment. Finally, individual differences in task performance were predicted by the differences in amplitude of P3b that was evoked by the onset of the reset screen.

Integration of exteroceptive and interoceptive information within the hippocampus: a computational study

Many episodic memory studies have critically implicated the hippocampus in the rapid binding of sensory information from the perception of the external environment, reported by exteroception. Other structures in the medial temporal lobe, especially the amygdala, have been more specifically linked with emotional dimension of episodic memories, reported by interoception. The hippocampal projection to the amygdala is proposed as a substrate important for the formation of extero-interoceptive associations, allowing adaptive behaviors based on past experiences. Recently growing evidence suggests that hippocampal activity observed in a wide range of behavioral tasks could reflect associations between exteroceptive patterns and their emotional valences. The hippocampal computational models, therefore, need to be updated to elaborate better interpretation of hippocampal-dependent behaviors. In earlier models, interoceptive features, if not neglected, are bound together with other exteroceptive features through autoassociative learning mechanisms. This way of binding integrates both kinds of features at the same level, which is not always suitable for example in the case of pattern completion. Based on the anatomical and functional heterogeneity along the septotemporal and transverse axes of the hippocampus, we suggest instead that distinct hippocampal subregions may be engaged in the representation of these different types of information, each stored apart in autoassociative memories but linked together in a heteroassociative way. The model is developed within the hard constraint of rapid, even single trial, learning of episodic memories. The performance of the model is assessed quantitatively and its resistance to interference is demonstrated through a series of numerical experiments. An experiment of reversal learning in patients with amnesic cognitive impairment is also reproduced.

A pavlovian model of the amygdala and its influence within the medial temporal lobe

Recent advances in neuroscience give us a better view of the inner structure of the amygdala, of its relations with other regions in the Medial Temporal Lobe (MTL) and of the prominent role of neuromodulation. They have particularly shed light on two kinds of neurons in the basal nucleus of the amygdala, the so-called fear neurons and extinction neurons. Fear neurons mediate context-dependent fear by receiving contextual information from the hippocampus, whereas extinction neurons are linked with the medial prefrontal cortex (mPFC) and involved in fear extinction. The computational model of the amygdala that we describe in this paper is primarily a model of pavlovian conditioning, but its architecture also emphasizes the central role of the amygdala in the MTL memory processes through three main information flows. (i) Thalamic and higher order sensory cortical inputs including from the perirhinal cortex are received in the lateral amygdalar nucleus, where CS-US associations can be acquired. (ii) These associations are subsequently modulated, in the basal nucleus of the amygdala, by contextual inputs coming from the hippocampus and the mPFC. Basal fear and extinction neurons indicate the currently valid association to their main targets including in the MTL and the mPFC. (iii) The competition for the choice of the pavlovian response is ultimately performed by projection of these amygdalar neurons in the central nucleus of the amygdala where, beyond motor responding, a hormonal response, including cholinergic modulation, is also triggered via the basal forebrain. In turn, acetylcholine modulates activation in the basal nucleus and facilitates learning in the hippocampus. Based on biologically founded arguments, our model replicates a number of biological experiments, proposes some predictions about the role of amygdalar regions and describes pavlovian conditioning as a distributed systemic learning, binding memory processes in the MTL.

A perfusion fMRI investigation of thematic and categorical context effects in the spoken production of object names

The context in which objects are presented influences the speed at which they are named. We employed the blocked cyclic naming paradigm and perfusion functional magnetic resonance imaging (fMRI) to investigate the mechanisms responsible for interference effects reported for thematically and categorically related compared to unrelated contexts. Naming objects in categorically homogeneous contexts induced a significant interference effect that accumulated from the second cycle onwards. This interference effect was associated with significant perfusion signal decreases in left middle and posterior lateral temporal cortex and the hippocampus. By contrast, thematically homogeneous contexts facilitated naming latencies significantly in the first cycle and did not differ from heterogeneous contexts thereafter, nor were they associated with any perfusion signal changes compared to heterogeneous contexts. These results are interpreted as being consistent with an account in which the interference effect both originates and has its locus at the lexical level, with an incremental learning mechanism adapting the activation levels of target lexical representations following access. We discuss the implications of these findings for accounts that assume thematic relations can be active lexical competitors or assume mandatory involvement of top-down control mechanisms in interference effects during naming.

A model of curved saccade trajectories: spike rate adaptation in the brainstem as the cause of deviation away

The trajectory of saccades to a target is often affected whenever there is a distractor in the visual field. Distractors can cause a saccade to deviate towards their location or away from it. The oculomotor mechanisms that produce deviation towards distractors have been thoroughly explored in behavioral, neurophysiological and computational studies. The mechanisms underlying deviation away, on the other hand, remain unclear. Behavioral findings suggest a mechanism of spatially focused, top-down inhibition in a saccade map, and deviation away has become a tool to investigate such inhibition. However, this inhibition hypothesis has little neuroanatomical or neurophysiological support, and recent findings go against it. Here, we propose that deviation away results from an unbalanced saccade drive from the brainstem, caused by spike rate adaptation in brainstem long-lead burst neurons. Adaptation to stimulation in the direction of the distractor results in an unbalanced drive away from it. An existing model of the saccade system was extended with this theory. The resulting model simulates a wide range of findings on saccade trajectories, including findings that have classically been interpreted to support inhibition views. Furthermore, the model replicated the effect of saccade latency on deviation away, but predicted this effect would be absent with large (400 ms) distractor-target onset asynchrony. This prediction was confirmed in an experiment, which demonstrates that the theory both explains classical findings on saccade trajectories and predicts new findings.

Why trace and delay conditioning are sometimes (but not always) hippocampal dependent: a computational model

A recurrent-network model provides a unified account of the hippocampal region in mediating the representation of temporal information in classical eyeblink conditioning. Much empirical research is consistent with a general conclusion that delay conditioning (in which the conditioned stimulus CS and unconditioned stimulus US overlap and co-terminate) is independent of the hippocampal system, while trace conditioning (in which the CS terminates before US onset) depends on the hippocampus. However, recent studies show that, under some circumstances, delay conditioning can be hippocampal-dependent and trace conditioning can be spared following hippocampal lesion. Here, we present an extension of our prior trial-level models of hippocampal function and stimulus representation that can explain these findings within a unified framework. Specifically, the current model includes adaptive recurrent collateral connections that aid in the representation of intra-trial temporal information. With this model, as in our prior models, we argue that the hippocampus is not specialized for conditioned response timing, but rather is a general-purpose system that learns to predict the next state of all stimuli given the current state of variables encoded by activity in recurrent collaterals. As such, the model correctly predicts that hippocampal involvement in classical conditioning should be critical not only when there is an intervening trace interval, but also when there is a long delay between CS onset and US onset. Our model simulates empirical data from many variants of classical conditioning, including delay and trace paradigms in which the length of the CS, the inter-stimulus interval, or the trace interval is varied. Finally, we discuss model limitations, future directions, and several novel empirical predictions of this temporal processing model of hippocampal function and learning.

Towards a unified model of pavlovian conditioning: short review of trace conditioning models

There are three basic paradigms of classical conditioning: delay, trace and context conditioning where presentation of a conditioned stimulus (CS) or a context typically predicts an unconditioned stimulus (US). In delay conditioning CS and US normally coterminate, whereas in trace conditioning an interval of time exists between CS termination and US onset. The modeling of trace conditioning is a rather difficult computational problem and is a challenge to the behavior and connectionist approaches mainly due to a time gap between CS and US. To account for trace conditioning, Pavlov (Conditioned reflexes: an investigation of the physiological activity of the cerebral cortex, Oxford University Press, London, 1927) postulated the existence of a stimulus "trace" in the nervous system. Meanwhile, there exist many other options for solving this association problem. There are several excellent reviews of computational models of classical conditioning but none has thus far been devoted to trace conditioning. Eight representative models of trace conditioning aimed at building a prospective model are being reviewed below in a brief form. As a result, one of them, comprising the most important features of its predecessors, can be suggested as a real candidate for a unified model of trace conditioning.

Characterizing cognitive aging of spatial and contextual memory in animal models

Episodic memory, especially memory for contextual or spatial information, is particularly vulnerable to age-related decline in humans and animal models of aging. The continuing improvement of virtual environment technology for testing humans signifies that widely used procedures employed in the animal literature for examining spatial memory could be developed for examining age-related cognitive decline in humans. The current review examines cross species considerations for implementing these tasks and translating findings across different levels of analysis. The specificity of brain systems as well as gaps in linking human and animal laboratory models is discussed.

A single-trace dual-process model of episodic memory: a novel computational account of familiarity and recollection

Dual-process theories of episodic memory state that retrieval is contingent on two independent processes: familiarity (providing a sense of oldness) and recollection (recovering events and their context). A variety of studies have reported distinct neural signatures for familiarity and recollection, supporting dual-process theory. One outstanding question is whether these signatures reflect the activation of distinct memory traces or the operation of different retrieval mechanisms on a single memory trace. We present a computational model that uses a single neuronal network to store memory traces, but two distinct and independent retrieval processes access the memory. The model is capable of performing familiarity and recollection-based discrimination between old and new patterns, demonstrating that dual-process models need not to rely on multiple independent memory traces, but can use a single trace. Importantly, our putative familiarity and recollection processes exhibit distinct characteristics analogous to those found in empirical data; they diverge in capacity and sensitivity to sparse and correlated patterns, exhibit distinct ROC curves, and account for performance on both item and associative recognition tests. The demonstration that a single-trace, dual-process model can account for a range of empirical findings highlights the importance of distinguishing between neuronal processes and the neuronal representations on which they operate.

Dynamics and robustness of familiarity memory

When presented with an item or a face, one might have a sense of recognition without the ability to recall when or where the stimulus has been encountered before. This sense of recognition is called familiarity memory. Following previous computational studies of familiarity memory, we investigate the dynamical properties of familiarity discrimination and contrast two different familiarity discriminators: one based on the energy of the neural network and the other based on the time derivative of the energy. We show how the familiarity signal decays rapidly after stimulus presentation. For both discriminators, we calculate the capacity using mean field analysis. Compared to recall capacity (the classical associative memory in Hopfield nets), both the energy and the slope discriminators have bigger capacity, yet the energy-based discriminator has a higher capacity than one based on its time derivative. Finally, both discriminators are found to have a different noise dependence.

Human medial temporal lobe neurons respond preferentially to personally relevant images

People with whom one is personally acquainted tend to elicit richer and more vivid memories than people with whom one does not have a personal connection. Recent findings from neurons in the human medial temporal lobe (MTL) have shown that individual cells respond selectively and invariantly to representations of famous people [Quian Quiroga R, Reddy L, Kreiman G, Koch C, Fried I (2005) Nature 435(7045):1102-1107]. Observing these cells, we wondered whether photographs of personally relevant individuals, such as family members, might be more likely to generate such responses. To address this issue, we recorded the activity of 2,330 neurons in the human MTL while patients viewed photographs of varying personal relevance: previously unknown faces and landscapes, familiar but not necessarily personally relevant faces and landscapes, and finally, photographs of the patients themselves, their families, and the experimenters. Our findings indicate that personally relevant photographs are indeed more likely to elicit selective responses in MTL neurons than photographs of individuals with whom the patients have had no personal contact. These findings further suggest that relevant stimuli are encoded by a larger proportion of neurons than less relevant stimuli, given that familiar or personally relevant items are linked to a larger variety of experiences and memories of these experiences.

A neurocomputational model of classical conditioning phenomena: a putative role for the hippocampal region in associative learning

Some existing models of hippocampal function simulate performance in classical conditioning tasks using the error backpropagation algorithm to guide learning (Gluck, M.A., and Myers, C.E., (1993). Hippocampal mediation of stimulus representation: a computational theory. Hippocampus, 3(4), 491-516.). This algorithm is not biologically plausible because it requires information to be passed backward through layers of nodes and assumes that the environment provides information to the brain about what correct outputs should be. Here, we show that the same information-processing function proposed for the hippocampal region in the Gluck and Myers (1993) model can also be implemented in a network without using the backpropagation algorithm. Instead, our newer instantiation of the theory uses only (a) Hebbian learning methods which match more closely with synaptic and associative learning mechanisms ascribed to the hippocampal region and (b) a more plausible representation of input stimuli. We demonstrate here that this new more biologically plausible model is able to simulate various behavioral effects, including latent inhibition, acquired equivalence, sensory preconditioning, negative patterning, and context shift effects. In addition, the newer model is able to address some new phenomena including the effect of the number of training trials on blocking and overshadowing.

Multiple memory stores and operant conditioning: a rationale for memory's complexity

Why does the brain contain more than one memory system? Genetic algorithms can play a role in elucidating this question. Here, model animals were constructed containing a dorsal striatal layer that controlled actions, and a ventral striatal layer that controlled a dopaminergic learning signal. Both layers could gain access to three modeled memory stores, but such access was penalized as energy expenditure. Model animals were then selected on their fitness in simulated operant conditioning tasks. Results suggest that having access to multiple memory stores and their representations is important in learning to regulate dopamine release, as well as in contextual discrimination. For simple operant conditioning, as well as stimulus discrimination, hippocampal compound representations turned out to suffice, a counterintuitive result given findings that hippocampal lesions tend not to affect performance in such tasks. We argue that there is in fact evidence to support a role for compound representations and the hippocampus in even the simplest conditioning tasks.

Universal memory mechanism for familiarity recognition and identification

Macaque monkeys were tested on a delayed-match-to-multiple-sample task, with either a limited set of well trained images (in randomized sequence) or with never-before-seen images. They performed much better with novel images. False positives were mostly limited to catch-trial image repetitions from the preceding trial. This result implies extremely effective one-shot learning, resembling Standing's finding that people detect familiarity for 10,000 once-seen pictures (with 80% accuracy) (Standing, 1973). Familiarity memory may differ essentially from identification, which embeds and generates contextual information. When encountering another person, we can say immediately whether his or her face is familiar. However, it may be difficult for us to identify the same person. To accompany the psychophysical findings, we present a generic neural network model reproducing these behaviors, based on the same conservative Hebbian synaptic plasticity that generates delay activity identification memory. Familiarity becomes the first step toward establishing identification. Adding an inter-trial reset mechanism limits false positives for previous-trial images. The model, unlike previous proposals, relates repetition-recognition with enhanced neural activity, as recently observed experimentally in 92% of differential cells in prefrontal cortex, an area directly involved in familiarity recognition. There may be an essential functional difference between enhanced responses to novel versus to familiar images: The maximal signal from temporal cortex is for novel stimuli, facilitating additional sensory processing of newly acquired stimuli. The maximal signal for familiar stimuli arising in prefrontal cortex facilitates the formation of selective delay activity, as well as additional consolidation of the memory of the image in an upstream cortical module.

Computational models can replicate the capacity of human recognition memory

The capacity of human recognition memory was investigated by Standing, who presented several groups of participants with different numbers of pictures (from 20 to 10 000), and subsequently tested their ability to distinguish between previously presented and novel pictures. The estimated number of pictures retained in recognition memory by different groups when plotted as a logarithmic function of the number of pictures presented formed a straight line, representing a power-law relationship. Here, we investigate if published models of familiarity discrimination can replicate Standing's results. We first consider a simplified assumption that visual stimuli are represented by uncorrelated patterns of firing of visual neurons providing input to the familiarity discrimination network. We show that for this case three models (Familiarity discrimination based on Energy (FamE), Anti-Hebbian and Info-max) can reproduce the observed power-law relationship when their synaptic weights are appropriately initialized. For more realistic assumptions on neural representation of stimuli, the FamE model is no longer able to reproduce the power-law relationship in simulations, while the Anti-Hebbian and Info-max can reproduce it. Nevertheless, the slopes of the power-law relationships produced by the models in all simulations differ from that observed by Standing. We discuss possible reasons for this difference, including separate contributions of familiarity and recollection processes, and describe experimentally testable predictions based on our analysis.

Autoshaping and automaintenance: a neural-network approach

This article presents an interpretation of autoshaping, and positive and negative automaintenance, based on a neural-network model. The model makes no distinction between operant and respondent learning mechanisms, and takes into account knowledge of hippocampal and dopaminergic systems. Four simulations were run, each one using an A-B-A design and four instances of feedfoward architectures. In A, networks received a positive contingency between inputs that simulated a conditioned stimulus (CS) and an input that simulated an unconditioned stimulus (US). Responding was simulated as an output activation that was neither elicited by nor required for the US. B was an omission-training procedure. Response directedness was defined as sensory feedback from responding, simulated as a dependence of other inputs on responding. In Simulation 1, the phenomena were simulated with a fully connected architecture and maximally intense response feedback. The other simulations used a partially connected architecture without competition between CS and response feedback. In Simulation 2, a maximally intense feedback resulted in substantial autoshaping and automaintenance. In Simulation 3, eliminating response feedback interfered substantially with autoshaping and automaintenance. In Simulation 4, intermediate autoshaping and automaintenance resulted from an intermediate response feedback. Implications for the operant-respondent distinction and the behavior-neuroscience relation are discussed.

Context effects on familiarity are familiarity effects of context — An electrophysiological study

Within dual-process accounts of recognition memory, familiarity (as opposed to recollection) is often referred to as a rather automatic and context-free process. Thus, in episodic object recognition, familiarity and its electrophysiological ERP signature are supposed to index prior occurrence of an object independent of the context the object was originally encountered in, e.g., [Ecker, U.K.H., Zimmer, H.D., Groh-Bordin, C., in press. Color and context: An ERP study on intrinsic and extrinsic feature binding in episodic memory. Mem. Cogn.]). Yet, contextual sensitivity of familiarity has also been reported (e.g., [Tsivilis, D., Otten, L.J., Rugg, M.D., 2001. Context effects on the neural correlates of recognition memory: An electrophysiological study. Neuron 31, 497-505.]). We argue that considering attentional and perceptual factors of target processing is vital in understanding these conflicting results. Presenting target objects on contextual landscape scenes, we introduced a cueing technique designed to focus subjects' attention on target processing. We demonstrate that context effects on familiarity are diminished if the attentional impact of contextual stimuli is experimentally controlled, arguing that contextual influences on object familiarity are indirect and mediated by factors such as salience and attentional capture. Results suggest that salient context stimuli may elicit an independent familiarity signal instead of directly impacting on the familiarity signal of the target object. We conclude that (a) object familiarity is in principle a rather automatic and context-free process, and that (b) the study of episodic memory can profit substantially from adopting a dynamic processing perspective.

Neural-network simulations of two context-dependence phenomena

This paper describes simulations of two context-dependence phenomena in Pavlovian conditioning, using a neural-network model that draws on knowledge from neuroscience and makes no distinction between operant and respondent learning mechanisms. One phenomenon is context specificity or the context-shift effect, the decrease of conditioned responding (CR) when the conditioned stimulus (CS) is tested in a context different from the one in which it had been paired with the unconditioned stimulus (US). The other effect is renewal, the recovery of CR in the training context after extinction in another context. For specificity (simulation 1), two neural networks were first given 200 CS-US pairings in a context. Then, the CS was tested either in the training context or a new context. Output activations in the new context were substantially lower. For renewal (simulation 2), two networks were first given 200 CS-US pairings in a context, then 100 extinction trials in either the same context or a new one, and then tested back in the training context. Output activations during the test phase were substantially higher after extinction in a new context. The results are interpreted in terms of the dynamics of activations and weights.

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

Computational models of the hippocampal region link psychological theories of associative learning with their underlying physiological and anatomical substrates. Our approach to theory development began with a broad description of the computations that depend on the hippocampal region in classical conditioning (Gluck and Myers, 1993 and Gluck and Myers, 2001). In this initial model, the hippocampal region was treated as an Information-processing system that transformed stimulus representations, compressing (making more similar) representations of inputs that co-occur or are otherwise redundant, while differentiating (or making less similar) representations of inputs that predict different future events. This model led to novel predictions for the behavioral consequences of hippocampal-region lesions in rodents and of brain damage in humans who have amnesia or are in the earliest stages of Alzheimer's disease. Many of these predictions have, since been confirmed by our lab and others. Functional brain imaging studies have provided further supporting evidence. In more recent computational modeling, we have shown how some aspects of this proposed information-processing function could emerge from known anatomical and physiological characteristics of the hippocampal region, including the entorhinal cortex and the septo-hippocampal cholinergic system. The modeling to date lays the groundwork for future directions that increase the depth of detail of the biological modeling, as well as the breadth of behavioral phenomena addressed. In particular, we are working now to reconcile these kinds of incremental associative learning models with other models of the hippocampal region that account for the rapid formation of declarative memories.