A cognitive and associative memory

By introducing a physiological constraint in the auto-correlation matrix memory, the system is found to acquire an ability in cognition i.e. the ability to identify and input pattern by its proximity to any one of the stored memories. The physiological constraint here is that the attribute of a given synapse (i.e. excitatory or inhibitory) is uniquely determined by the neuron it belongs. Thus the synaptic coupling is generally not symmetric. Analytical and numerical analyses revealed that the present model retrieves a memory if an input pattern is close to the pattern of the stored memories; if not, it gives a clear response by going into a special mode where almost all neurons are in the same state in each time step. This uniform mode may be stationary or periodic, depending on whether or not the number of the excitatory neurons exceeds the number of inhibitory neurons.     

Complementary molecular models of learning and memory.

The functional capabilities of the brain are formally characterizable interms of a finite system along with a memory space which it can manipulate. Two types of learning are possible: (1) modification-based learning, associated with alternate realizations of the finite system; (2) memory-based learning, associated with the assimilation, manipulation, and retrieval of memories. Constructive models which fulfill these conditions and which at the same time operate on the basis of molecular information processing principles have certain general features. We describe these features in terms of two interfaced submodels, the first for the finite system and the second for the memory space. The finite system may be realized by networks of neurons in which the specificity of enzyme molecules controls the nerve impulse. Such a realization is amenable to modification-based learning mediated by processes analogous to those of natural evolution and selective theories of antibody synthesis. The memory space is realizable by networks of neurons in which the conformation of dendritic receptor molecules controls the nerve impulse. In this case certain neurons firing in response to an external input undergo sensitization at the dendrites and in such a way that they are loadable and later callable by reference neurons, thereby allowing for reconstruction of manipulation of the firing pattern associated with this input. The overall construction makes a large number of biochemical, anatomical, physiological, and psychological predictions which are either testable or in good agreement with fact.     

Sequential use of mushroom body neuron subsets during drosophila odor memory processing

Drosophila mushroom bodies (MB) are bilaterally symmetric multilobed brain structures required for olfactory memory. Previous studies suggested that neurotransmission from MB neurons is only required for memory retrieval. Our unexpected observation that Dorsal Paired Medial (DPM) neurons, which project only to MB neurons, are required during memory storage but not during acquisition or retrieval, led us to revisit the role of MB neurons in memory processing. We show that neurotransmission from the alpha'beta' subset of MB neurons is required to acquire and stabilize aversive and appetitive odor memory, but is dispensable during memory retrieval. In contrast, neurotransmission from MB alphabeta neurons is only required for memory retrieval. These data suggest a dynamic requirement for the different subsets of MB neurons in memory and are consistent with the notion that recurrent activity in an MB alpha'beta' neuron-DPM neuron loop is required to stabilize memories formed in the MB alphabeta neurons.     

Drosophila dorsal paired medial neurons provide a general mechanism for memory consolidation

Memories are formed, stabilized in a time-dependent manner, and stored in neural networks. In Drosophila, retrieval of punitive and rewarded odor memories depends on output from mushroom body (MB) neurons, consistent with the idea that both types of memory are represented there. Dorsal Paired Medial (DPM) neurons innervate the mushroom bodies, and DPM neuron output is required for the stability of punished odor memory. Here we show that stable reward-odor memory is also DPM neuron dependent. DPM neuron expression of amnesiac (amn) in amn mutant flies restores wild-type memory. In addition, disrupting DPM neurotransmission between training and testing abolishes reward-odor memory, just as it does with punished memory. We further examined DPM-MB connectivity by overexpressing a DScam variant that reduces DPM neuron projections to the MB alpha, beta, and gamma lobes. DPM neurons that primarily project to MB alpha' and beta' lobes are capable of stabilizing punitive- and reward-odor memory, implying that both forms of memory have similar circuit requirements. Therefore, our results suggest that the fly employs the local DPM-MB circuit to stabilize punitive- and reward-odor memories and that stable aspects of both forms of memory may reside in mushroom body alpha' and beta' lobe neurons.     

Memories in context

Context-dependent associative memories are models that allow the retrieval of different vectorial responses given a same vectorial stimulus, depending on the context presented to the memory. The contextualization is obtained by doing the Kronecker product between two vectorial entries to the associative memory: the key stimulus and the context. These memories are able to display a wide variety of behaviors that range from all the basic operations of the logical calculus (including fuzzy logics) to the selective extraction of features from complex vectorial patterns. In the present contribution, we show that a context-dependent memory matrix stores a large amount of possible virtual associative memories, that awaken in the presence of a context. We show how the vectorial context allows a memory matrix to be representable in terms of its singular-value decomposition. We describe a neural interpretation of the model in which the Kronecker product is performed on the same neurons that sustain the memory. We explored, with numerical experiments, the reliability of chains of contextualized associations. In some cases, random disconnection produces the emergence of oscillatory behaviors of the system. Our results show that associative chains retain their performances for relatively large dimensions. Finally, we analyze the properties of some modules of context-dependent autoassociative memories inserted in recursive nets: the perceptual autoorganization in the presence of ambiguous inputs (e.g. the disambiguation of the Necker’s cube figure), the construction of intersection filters, and the feature extraction capabilities.     

Diverse mechanisms to remember various odors

Two dorsal paired medial (DPM) neurons express the Amnesiac neuropeptide and project onto mushroom bodies, the Drosophila olfactory memory center. In this issue of Neuron, Keene et al. show that higher-level brain circuits process various olfactory memories differently. DPM neurons are required during acquisition of some odors and during memory consolidation of others. These findings reveal a surprising level of complexity for the formation of olfactory memories in Drosophila.     

Three dopamine pathways induce aversive odor memories with different stability

Animals acquire predictive values of sensory stimuli through reinforcement. In the brain of Drosophila melanogaster, activation of two types of dopamine neurons in the PAM and PPL1 clusters has been shown to induce aversive odor memory. Here, we identified the third cell type and characterized aversive memories induced by these dopamine neurons. These three dopamine pathways all project to the mushroom body but terminate in the spatially segregated subdomains. To understand the functional difference of these dopamine pathways in electric shock reinforcement, we blocked each one of them during memory acquisition. We found that all three pathways partially contribute to electric shock memory. Notably, the memories mediated by these neurons differed in temporal stability. Furthermore, combinatorial activation of two of these pathways revealed significant interaction of individual memory components rather than their simple summation. These results cast light on a cellular mechanism by which a noxious event induces different dopamine signals to a single brain structure to synthesize an aversive memory.     

Are Autobiographical Memories Inherently Social? Evidence from an fMRI Study

The story of our lifetime – our narrative self – is constructed from our autobiographical memories. A central claim of social psychology is that this narrative self is inherently social: When we construct our lives, we do so in a real or imagined interaction. This predicts that self-referential processes which are involved in recall of autobiographical memories overlap with processes involved in social interactions. Indeed, previous functional MRI studies indicate that regions in the medial prefrontal cortex (mPFC) are activated during autobiographical memory recall and virtual communication. However, no fMRI study has investigated recall of autobiographical memories in a real-life interaction. We developed a novel paradigm in which participants overtly reported self-related and other-related memories to an experimenter, whose non-verbal reactions were being filmed and online displayed to the participants in the scanner. We found that recall of autobiographical vs. non-autobiographical memories was associated with activation of the mPFC, as was recall in the social as compared to a non-social control condition; however, both contrasts involved different non-overlapping regions within the mPFC. These results indicate that self-referential processes involved in autobiographical memory recall are different from processes supporting social interactions, and argue against the hypothesis that autobiographical memories are inherently social.     

Neural coding for the retrieval of multiple memory patterns

We investigate the retrieval dynamics in a feature-based semantic memory model, in which the features are coded by neurons of the Hindmarsh–Rose type in the chaotic regime. We consider the retrieval process as consisting of the synchronized firing activity of the neurons coding for the same memory pattern. The retrieval dynamics is investigated for multiple patterns, with particular attention to the case of overlapping memories. In this case, we hypothesize a dynamical nontransitive mechanism based on synchronization, that allows for a shared feature to participate in multiple memory representations. The problem of the choice of a cognitive plausible time-scale for the retrieval analysis is investigated by analyzing the information that can be inferred from finite-time analyses. Different types of indicators are proposed in order to evaluate the temporal dynamics of the neurons engaged in the retrieval process. We interpret the simulation results as suggestive of a role for chaotic dynamics in allowing for flexible composition of elementary meaningful units in memory representations.     

Neural coding for the retrieval of multiple memory patterns

We investigate the retrieval dynamics in a feature-based semantic memory model, in which the features are coded by neurons of the Hindmarsh-Rose type in the chaotic regime. We consider the retrieval process as consisting of the synchronized firing activity of the neurons coding for the same memory pattern. The retrieval dynamics is investigated for multiple patterns, with particular attention to the case of overlapping memories. In this case, we hypothesize a dynamical nontransitive mechanism based on synchronization, that allows for a shared feature to participate in multiple memory representations. The problem of the choice of a cognitive plausible time-scale for the retrieval analysis is investigated by analyzing the information that can be inferred from finite-time analyses. Different types of indicators are proposed in order to evaluate the temporal dynamics of the neurons engaged in the retrieval process. We interpret the simulation results as suggestive of a role for chaotic dynamics in allowing for flexible composition of elementary meaningful units in memory representations.     

Mechanisms of long-term memory

Long-term potentiation (LTP) of the hippocampus provides an excellent model on which to build hypotheses for the laying down of memories in the cerebral cortex. After repetitive activation, the primary happening seems to be the increase in transmitter sensitivity brought about by the increased Ca2+ in the recipient neurons. There may be secondary presynaptic changes. The extreme duration of the LTP may require structural changes of the synaptic spines. The hippocampus plays an essential role in the laying down of cognitive memories, the pathway to the frontal lobe being via the MD thalamus. The thalamo-cortical fibres activate stellate cells whose axons make climbing fibre-like ramifications up the apical dendrites of the pyramidal cells. On the Marr hypothesis repetitive conjunction of synaptic activation by these climbing fibres with synaptic activation by horizontal fibres on the apical dendrites produces prolonged potentiation of the horizontal fibre synapses, which is the neural basis of memory. Presumably this is a consequence of the raised Ca2+ of the apical dendrites, acting as it does on the hippocampal LTP. It will be considered how this elemental unit for cerebral memory can be developed into the varieties of cerebral memories that have been located by study of the regional cerebral blood flow during their retrieval. These sites for memory are in the frontal lobe, usually in the superior prefrontal area.     

A working memory model for serial order that stores information in the intrinsic excitability properties of neurons

Models for temporary information storage in neuronal populations are dominated by mechanisms directly dependent on synaptic plasticity. There are nevertheless other mechanisms available that are well suited for creating short-term memories. Here we present a model for working memory which relies on the modulation of the intrinsic excitability properties of neurons, instead of synaptic plasticity, to retain novel information for periods of seconds to minutes. We show that it is possible to effectively use this mechanism to store the serial order in a sequence of patterns of activity. For this we introduce a functional class of neurons, named gate interneurons, which can store information in their membrane dynamics and can literally act as gates routing the flow of activations in the principal neurons population. The presented model exhibits properties which are in close agreement with experimental results in working memory. Namely, the recall process plays an important role in stabilizing and prolonging the memory trace. This means that the stored information is correctly maintained as long as it is being used. Moreover, the working memory model is adequate for storing completely new information, in time windows compatible with the notion of “one-shot” learning (hundreds of milliseconds).     

Neuronal signalling of fear memory

The learning and remembering of fearful events depends on the integrity of the amygdala, but how are fear memories represented in the activity of amygdala neurons? Here, we review recent electrophysiological studies indicating that neurons in the lateral amygdala encode aversive memories during the acquisition and extinction of Pavlovian fear conditioning. Studies that combine unit recording with brain lesions and pharmacological inactivation provide evidence that the lateral amygdala is a crucial locus of fear memory. Extinction of fear memory reduces associative plasticity in the lateral amygdala and involves the hippocampus and prefrontal cortex. Understanding the signalling of aversive memory by amygdala neurons opens new avenues for research into the neural systems that support fear behaviour.     

A mathematical model for the decay of short-term memory with age

A mathematical neural net model based on our previous studies (Anninos et al, 1970; Anninos, 1972) is proposed here to show that the short-term memory of events decays with man age. In particular, in this work we try to explain why recent memories die out with age before the establishment of permanent memory. As it was shown we lose some connections due to the loss of a large number of neurons with age, and in our model this corresponds to a smaller hysteresis loop which, according to our assumption, represents the short-term memory of an event. Thus we showed that if we decrease the number of connections even further the hysteresis loop will vanish to a single curve which, according to Katchalsky & Oplatka (1969), corresponds to a memory-less system.     

A model of late long-term potentiation simulates aspects of memory maintenance

Late long-term potentiation (L-LTP) denotes long-lasting strengthening of synapses between neurons. L-LTP appears essential for the formation of long-term memory, with memories at least partly encoded by patterns of strengthened synapses. How memories are preserved for months or years, despite molecular turnover, is not well understood. Ongoing recurrent neuronal activity, during memory recall or during sleep, has been hypothesized to preferentially potentiate strong synapses, preserving memories. This hypothesis has not been evaluated in the context of a mathematical model representing ongoing activity and biochemical pathways important for L-LTP. In this study, ongoing activity was incorporated into two such models - a reduced model that represents some of the essential biochemical processes, and a more detailed published model. The reduced model represents synaptic tagging and gene induction simply and intuitively, and the detailed model adds activation of essential kinases by Ca(2+). Ongoing activity was modeled as continual brief elevations of Ca(2+). In each model, two stable states of synaptic strength/weight resulted. Positive feedback between synaptic weight and the amplitude of ongoing Ca(2+) transients underlies this bistability. A tetanic or theta-burst stimulus switches a model synapse from a low basal weight to a high weight that is stabilized by ongoing activity. Bistability was robust to parameter variations in both models. Simulations illustrated that prolonged periods of decreased activity reset synaptic strengths to low values, suggesting a plausible forgetting mechanism. However, episodic activity with shorter inactive intervals maintained strong synapses. Both models support experimental predictions. Tests of these predictions are expected to further understanding of how neuronal activity is coupled to maintenance of synaptic strength. Further investigations that examine the dynamics of activity and synaptic maintenance can be expected to help in understanding how memories are preserved for up to a lifetime in animals including humans.     

Spiking neural network model for memorizing sequences with forward and backward recall

We present an oscillatory network of conductance based spiking neurons of Hodgkin–Huxley type as a model of memory storage and retrieval of sequences of events (or objects). The model is inspired by psychological and neurobiological evidence on sequential memories. The building block of the model is an oscillatory module which contains excitatory and inhibitory neurons with all-to-all connections. The connection architecture comprises two layers. A lower layer represents consecutive events during their storage and recall. This layer is composed of oscillatory modules. Plastic excitatory connections between the modules are implemented using an STDP type learning rule for sequential storage. Excitatory neurons in the upper layer project star-like modifiable connections toward the excitatory lower layer neurons. These neurons in the upper layer are used to tag sequences of events represented in the lower layer. Computer simulations demonstrate good performance of the model including difficult cases when different sequences contain overlapping events. We show that the model with STDP type or anti-STDP type learning rules can be applied for the simulation of forward and backward replay of neural spikes respectively.     

Dopamine is required for learning and forgetting in Drosophila

Psychological studies in humans and behavioral studies of model organisms suggest that forgetting is a common and biologically regulated process, but the molecular, cellular, and circuit mechanisms underlying forgetting are poorly understood. Here we show that the bidirectional modulation of a small subset of dopamine neurons (DANs) after olfactory learning regulates the rate of forgetting of both punishing (aversive) and rewarding (appetitive) memories. Two of these DANs, MP1 and MV1, exhibit synchronized ongoing activity in the mushroom body neuropil in alive and awake flies before and after learning, as revealed by functional cellular imaging. Furthermore, while the mushroom-body-expressed dDA1 dopamine receptor is essential for the acquisition of memory, we show that the dopamine receptor DAMB, also highly expressed in mushroom body neurons, is required for forgetting. We propose?a dual role for dopamine: memory acquisition through dDA1 signaling and forgetting through DAMB signaling in the mushroom body neurons.