Associative Memory Recall Research Articles

Steroid hormone (20-hydroxyecdysone) modulates the acquisition of aversive olfactory memories in pollen forager honeybees

Here, we examine effects of the steroid hormone, 20-hydroxyecdysone (20-E), on associative olfactory learning in the honeybee, Apis mellifera. 20-E impaired the bees' ability to associate odors with punishment during aversive conditioning, but did not interfere with their ability to associate odors with a food reward (appetitive learning). The steroid had a significant impact also on the expression of amine-receptor genes in centers of the brain involved in the formation and recall of associative olfactory memories (mushroom bodies). 20-E increased expression of the dopamine receptor gene, Amdop2, and reduced the expression of the putative dopamine/ecdysone receptor gene, Amgpcr19. Interestingly, Amgpcr19 tended to be highly expressed in the brains of foragers that exhibited strong aversive learning, but expressed at lower levels in bees that performed well in appetitive learning assays. In 2-d-old bees, transcript levels of the same gene could be reduced by queen mandibular pheromone, a pheromone that blocks aversive learning in young worker bees. As ecdysteroid levels rise to a peak ∼2 d after adult emergence and then fall to low levels in foragers, we examined aversive learning also in young worker bees. Aversive learning performance in 2-d-old bees was consistently poor. The results of this study indicate that learning in honeybees can be modulated by ecdysteroids. They highlight, in addition, a potential involvement of the putative dopamine/ecdysone receptor, AmGPCR19, in hormonal regulation of associative olfactory learning in the honeybee.

Associative memory model with long-tail-distributed Hebbian synaptic connections

The postsynaptic potentials of pyramidal neurons have a non-Gaussian amplitude distribution with a heavy tail in both hippocampus and neocortex. Such distributions of synaptic weights were recently shown to generate spontaneous internal noise optimal for spike propagation in recurrent cortical circuits. However, whether this internal noise generation by heavy-tailed weight distributions is possible for and beneficial to other computational functions remains unknown. To clarify this point, we construct an associative memory (AM) network model of spiking neurons that stores multiple memory patterns in a connection matrix with a lognormal weight distribution. In AM networks, non-retrieved memory patterns generate a cross-talk noise that severely disturbs memory recall. We demonstrate that neurons encoding a retrieved memory pattern and those encoding non-retrieved memory patterns have different subthreshold membrane-potential distributions in our model. Consequently, the probability of responding to inputs at strong synapses increases for the encoding neurons, whereas it decreases for the non-encoding neurons. Our results imply that heavy-tailed distributions of connection weights can generate noise useful for AM recall.

Strategic Manipulations for Associative Memory and the Role of Verbal Processing Abilities in Schizophrenia

Individuals with schizophrenia demonstrate episodic memory (EM) deficits and abnormal EM-related brain activity. Experimental encoding manipulations significantly benefit memory performance in schizophrenia, suggesting that a strategic processing deficit may contribute to memory impairment. However, few studies have investigated the combined effects of encoding and retrieval strategies on EM in schizophrenia. The current study examined the impact of encoding and retrieval strategies on associative memory and brain activity in schizophrenia. We also assessed the role of verbal processing ability in response to strategic memory interventions in schizophrenia. Behavioral and functional neuroimaging data were collected from 23 participants with schizophrenia and 24 comparison subjects while performing associative memory encoding and recall tasks. Behaviorally, both schizophrenia participants and controls benefited from memory strategies and showed significant associations between verbal processing ability and recall. Additionally, among schizophrenia participants, encoding strategy use was associated with enhanced brain activity in multiple brain areas. Schizophrenia participants also demonstrated significant associations between verbal processing ability and encoding-related brain activity in prefrontal cortex. Findings suggest that memory performance and brain activity in schizophrenia can be enhanced via strategic manipulations, and individual differences in cognitive abilities in schizophrenia can affect behavioral and neurobiological responses to strategic memory interventions.

NMDA Receptors Are Not Required for Pattern Completion During Associative Memory Recall

Pattern completion, the ability to retrieve complete memories initiated by subsets of external cues, has been a major focus of many computation models. A previously study reports that such pattern completion requires NMDA receptors in the hippocampus. However, such a claim was derived from a non-inducible gene knockout experiment in which the NMDA receptors were absent throughout all stages of memory processes as well as animal's adult life. This raises the critical question regarding whether the previously described results were truly resulting from the requirement of the NMDA receptors in retrieval. Here, we have examined the role of the NMDA receptors in pattern completion via inducible knockout of NMDA receptors limited to the memory retrieval stage. By using two independent mouse lines, we found that inducible knockout mice, lacking NMDA receptor in either forebrain or hippocampus CA1 region at the time of memory retrieval, exhibited normal recall of associative spatial reference memory regardless of whether retrievals took place under full-cue or partial-cue conditions. Moreover, systemic antagonism of NMDA receptor during retention tests also had no effect on full-cue or partial-cue recall of spatial water maze memories. Thus, both genetic and pharmacological experiments collectively demonstrate that pattern completion during spatial associative memory recall does not require the NMDA receptor in the hippocampus or forebrain.

Acquisition, Extinction, and Recall of Opiate Reward Memory Are Signaled by Dynamic Neuronal Activity Patterns in the Prefrontal Cortex

The medial prefrontal cortex (mPFC) comprises an important component in the neural circuitry underlying drug-related associative learning and memory processing. Neuronal activation within mPFC circuits is correlated with the recall of opiate-related drug-taking experiences in both humans and other animals. Using an unbiased associative place conditioning procedure, we recorded mPFC neuronal populations during the acquisition, recall, and extinction phases of morphine-related associative learning and memory. Our analyses revealed that mPFC neurons show increased activity both in terms of tonic and phasic activity patterns during the acquisition phase of opiate reward-related memory and demonstrate stimulus-locked associative activity changes in real time, during the recall of opiate reward memories. Interestingly, mPFC neuronal populations demonstrated divergent patterns of bursting activity during the acquisition versus recall phases of newly acquired opiate reward memory, versus the extinction of these memories, with strongly increased bursting during the recall of an extinction memory and no associative bursting during the recall of a newly acquired opiate reward memory. Our results demonstrate that neurons within the mPFC are involved in both the acquisition, recall, and extinction of opiate-related reward memories, showing unique patterns of tonic and phasic activity patterns during these separate components of the opiate-related reward learning and memory recall.

Balanced Dopamine Is Critical for Pattern Completion during Associative Memory Recall

Pattern completion, the ability to retrieve complete memories initiated by partial cues, is a critical feature of the memory process. However, little is known regarding the molecular and cellular mechanisms underlying this process. To study the role of dopamine in memory recall, we have analyzed dopamine transporter heterozygous knockout mice (DAT+/−), and found that while these mice possess normal learning, consolidation, and memory recall under full cue conditions, they exhibit specific deficits in pattern completion under partial cue condition. This form of memory recall deficit in the dopamine transporter heterozygous knockout mice can be reversed by a low dose of the dopamine antagonist haloperidol, further confirming that the inability to retrieve memory patterns is a result of dopamine imbalance. Therefore, our results reveal that a delicate control of the brain's dopamine level is critical for pattern completion during associative memory recall.

Roles of Aminergic Neurons in Formation and Recall of Associative Memory in Crickets

We review recent progress in the study of roles of octopaminergic (OA-ergic) and dopaminergic (DA-ergic) signaling in insect classical conditioning, focusing on our studies on crickets. Studies on olfactory learning in honey bees and fruit-flies have suggested that OA-ergic and DA-ergic neurons convey reinforcing signals of appetitive unconditioned stimulus (US) and aversive US, respectively. Our work suggested that this is applicable to olfactory, visual pattern, and color learning in crickets, indicating that this feature is ubiquitous in learning of various sensory stimuli. We also showed that aversive memory decayed much faster than did appetitive memory, and we proposed that this feature is common in insects and humans. Our study also suggested that activation of OA- or DA-ergic neurons is needed for appetitive or aversive memory recall, respectively. To account for this finding, we proposed a model in which it is assumed that two types of synaptic connections are strengthened by conditioning and are activated during memory recall, one type being connections from neurons representing conditioned stimulus (CS) to neurons inducing conditioned response and the other being connections from neurons representing CS to OA- or DA-ergic neurons representing appetitive or aversive US, respectively. The former is called stimulus–response (S–R) connection and the latter is called stimulus–stimulus (S–S) connection by theorists studying classical conditioning in vertebrates. Results of our studies using a second-order conditioning procedure supported our model. We propose that insect classical conditioning involves the formation of S–S connection and its activation for memory recall, which are often called cognitive processes.

Genetic inactivation of D-amino acid oxidase enhances extinction and reversal learning in mice

Activation of the N-methyl-D-aspartate receptor (NMDAR) glycine site has been shown to accelerate adaptive forms of learning that may benefit psychopathologies involving cognitive and perseverative disturbances. In this study, the effects of increasing the brain levels of the endogenous NMDAR glycine site agonist D-serine, through the genetic inactivation of its catabolic enzyme D-amino acid oxidase (DAO), were examined in behavioral tests of learning and memory. In the Morris water maze task (MWM), mice carrying the hypofunctional Dao1(G181R) mutation demonstrated normal acquisition of a single platform location but had substantially improved memory for a new target location in the subsequent reversal phase. Furthermore, Dao1(G181R) mutant animals exhibited an increased rate of extinction in the MWM that was similarly observed following pharmacological administration of D-serine (600 mg/kg) in wild-type C57BL/6J mice. In contextual and cued fear conditioning, no alterations were found in initial associative memory recall; however, extinction of the contextual fear memory was facilitated in mutant animals. Thus, an augmented level of D-serine resulting from reduced DAO activity promotes adaptive learning in response to changing conditions. The NMDAR glycine site and DAO may be promising therapeutic targets to improve cognitive flexibility and inhibitory learning in psychiatric disorders such as schizophrenia and anxiety syndromes.

Activation of human hippocampal formation reflects success in both encoding and cued recall of paired associates

Contemporary theories of hippocampal function suggest that both encoding and retrieval of episodic memories may be accomplished by neural circuitry embedded within the same anatomical structures, but neuroimaging support for this hypothesis has been ambiguous. Recent studies suggest that the best available indicators of hippocampal encoding and retrieval operations are selective activations due to novelty, encoding success, and recall success in a paired associate learning paradigm. In the current study, both encoding and cued recall of paired associate words were conducted during a single session of fMRI scanning. Bilateral activation in the medial temporal lobe was detected for encoding word pairs vs. a fixation baseline and for encoding novel word pairs vs. repeated word pairs. These activations were stronger in subjects who successfully memorized more word pairs. In cued recall, greater responses were seen in higher performing subjects. In lower performing subjects, responses were greater to cue words whose paired associate was correctly recalled than to cue words whose correct associate had been forgotten (or not encoded). The difference between correct and incorrect trials was more pronounced on repeated presentations of the same cue words, but not apparent on their first presentation alone. Overlap of encoding and retrieval effects was maximal in the middle of the longitudinal extent of the right hippocampus, with one additional locus of overlap outside the MTL, in left occipitotemporal cortex. The conjunction of these effects suggests that it is correct to view both encoding and recall of associative memories as functions of an integrated hippocampal system.

An intelligent character recognizer for Telugu scripts using multiresolution analysis and associative memory

The present work is an attempt to develop a robust character recognizer for Telugu texts. We aim at designing a recognizer, which exploits the inherent characteristics of the Telugu Script. Our proposed method uses wavelet multi-resolution analysis for the purpose extracting features and associative memory model to accomplish the recognition tasks. Our system learns the style and font from the document itself and then it recognizes the remaining characters in the document. The major contribution of the present study can be outlined as follows. It is a robust OCR system for Telugu printed text. It avoids feature extraction process and it exploits the inherent characteristics of the Telugu character by a clever selection of Wavelet Basis function, which extracts the invariant features of the characters. It has a Hopfield-based Dynamic Neural Network for the purpose of learning and recognition. This is important because it overcomes the inherent difficulties of memory limitation and spurious states in the Hopfield Network. The DNN has been demonstrated to be efficient for associative memory recall. However, though it is normally not suitable for image processing application, the multi-resolution analysis reduces the sizes of the images to make the DNN applicable to the present domain. Our experimental results show extremely promising results.

Spike Train Timing-Dependent Associative Modification of Hippocampal CA3 Recurrent Synapses by Mossy Fibers

In the CA3 region of the hippocampus, extensive recurrent associational/commissural (A/C) connections made by pyramidal cells may function as a network for associative memory storage and recall. We here report that long-term potentiation (LTP) at the A/C synapses can be induced by association of brief spike trains in mossy fibers (MFs) from the dentate gyrus and A/C fibers. This LTP not only required substantial overlap between spike trains in MFs and A/C fibers, but also depended on the temporal order of these spike trains in a manner not predicted by the well-known rule of spike timing-dependent plasticity and requiring activation of type 1 metabotropic glutamate receptors. Importantly, spike trains in a putative single MF input provided effective postsynaptic activity for the induction of LTP at A/C synapses. Thus, the timing of spike trains in individual MFs may code information that is crucial for the associative modification of CA3 recurrent synapses.

Requirement for hippocampal CA3 NMDA receptors in associative memory recall.

Pattern completion, the ability to retrieve complete memories on the basis of incomplete sets of cues, is a crucial function of biological memory systems. The extensive recurrent connectivity of the CA3 area of hippocampus has led to suggestions that it might provide this function. We have tested this hypothesis by generating and analyzing a genetically engineered mouse strain in which the N-methyl-D-asparate (NMDA) receptor gene is ablated specifically in the CA3 pyramidal cells of adult mice. The mutant mice normally acquired and retrieved spatial reference memory in the Morris water maze, but they were impaired in retrieving this memory when presented with a fraction of the original cues. Similarly, hippocampal CA1 pyramidal cells in mutant mice displayed normal place-related activity in a full-cue environment but showed a reduction in activity upon partial cue removal. These results provide direct evidence for CA3 NMDA receptor involvement in associative memory recall.

Associative memory in chronic schizophrenia: a computational model

We developed a computer model to simulate associative memory recall of patients with chronic schizophrenia. Model inputs consisted of words derived from normative data that differed in terms of connectivity and network size, with the former quantitatively represented by parametric weights and the latter by the specific number of word associates that formed a particular network. Previous behavioral studies of normal subjects indicated better recall for words of high connectivity-small network (HCSN), followed by low connectivity-small network (LCSN), high connectivity-large network (HCLN), and low connectivity-large network (LCLN). This pattern of recall differed from that observed in behavioral studies of schizophrenic patients, which showed better recall for high connectivity words, regardless of network size. Holding constant network size while manipulating connection weights effectively simulated this schizophrenic pattern of recall. That is, manipulation of parametric weights coupled with a slight increase in noise significantly and reliably elicited the response pattern of abnormal connectivity demonstrated in the prior behavioral study of patients with chronic schizophrenia. An increase in noise was a necessary, but insufficient step in modeling the response pattern of abnormal connectivity. These findings provide support for the use of computational models to investigate dynamics of associative word recall in patients with chronic schizophrenia.

Neural fuzzy relational systems with a new learning algorithm

Fuzzy relational systems can represent symbolic knowledge in a formal numerical (subsymbolic) framework, with the aid of fuzzy relation equations. The disadvantage of this methodology is the need for a priori knowledge in order to construct the fuzzy relation equation. In this paper, a neural network model is proposed in order to represent fuzzy relational systems without the need of the construction of the fuzzy relation equation. The network ensures the ideal perfect recall of fuzzy associative memories when the a posteriori constructed fuzzy relation equation has a non-empty solution set. It is actually a single layer of generalized neurons (compositional neurons) that perform the sup- t-norm composition. An on-line learning algorithm adapting the weights of its interconnections is incorporated into the neural network. These weights are actually the elements of the fuzzy relation representing the fuzzy relational system. The algorithm is based on the knowledge about the topographic structure of the respective fuzzy relation.

Fault Diagnostic Schemes Based on Fuzzy Cognitive Map

Two fault diagnostic schemes based on Fuzzy Cognitive Map (FCM) are proposed. One is a basic diagnostic algorithm and the other is an incipient fault diagnostic algorithm by using pattern match based on FCM's special property, i.e., Temporal Associative Memory (TAM) recall. The basic diagnostic algorithm can be considered as a transition of signed-directed graph to FCM framework. Important idea behind the incipient fault diagnosis consists in fault FCM model and its TAM recall. Furthermore, an enhanced diagnostic algorithm based on Fuzzy Time Cognitive Map (FTCM) is proposed. Using the FTCM instead of FCM, problems concerned with propagation delay in generic FCM can be easily overcome.

A MOS circuit for a nonmonotonic neural network with excellent retrieval capabilities

An MOS circuit is proposed for implementing a nonmonotonic transfer characteristic of a neural network. The present research is motivated by the recent results of theoretical studies showing excellent equilibrium properties of networks with the nonmonotonic neural units. These properties include enhancement of storage capacity and complete elimination of noise in associative memory recall. The simple form of the transfer characteristic enables one to implement it with a simple electrical circuit of standard MOS transistors. SPICE simulation results are shown for the behavior of the neural units in associative memory recall.

Some statistical analysis of the recall probability of parallel intraconnected bidirectional associative memory

This paper addressed a statistical analysis for the recall of parallel intraconnected bidirectional associative memory—Modified Intraconnected Bidirectional Associative Memory 0 (MIBAM) and proved the conclusions: two MIBAM with the equal total number of neurons have the equal recalling probability form pairs of stored pattern pairs ifm is not too large. So they have the same capacity and same error correcting ability, i. e., their performances are statistically equivalent. The results of simulation support the conclusions well.

Memory Recall by Quasi-Fixed-Point Attractors in Oscillator Neural Networks

It is shown that approximate fixed-point attractors rather than synchronized oscillations can be employed by a wide class of neural networks of oscillators to achieve an associative memory recall. This computational ability of oscillator neural networks is ensured by the fact that reduced dynamic equations for phase variables in general involve two terms that can be respectively responsible for the emergence of synchronization and cessation of oscillations. Thus the cessation occurs in memory retrieval if the corresponding term dominates in the dynamic equations. A bottomless feature of the energy function for such a system makes the retrieval states quasi-fixed points, which admit continual rotating motion to a small portion of oscillators, when an extensive number of memory patterns are embedded. An approximate theory based on the self-consistent signal-to-noise analysis enables one to study the equilibrium properties of the neural network of phase variables with the quasi-fixed-point attractors. As far as the memory retrieval by the quasi-fixed points is concerned, the equilibrium properties including the storage capacity of oscillator neural networks are proved to be similar to those of the Hopfield type neural networks.