New Model of Heteroasociative min Memory Robust to Acquisition Noise

Associative memories in min and max algebra are of great interest for pattern recognition. One property of these is that they are one-shot, that is, in an attempt they converge to the solution without having to iterate. These memories have proven to be very efficient, but they manifest some weakness with mixed noise. If an appropriate kernel is not used, that is, a subset of the pattern to be recalled that is not affected by noise, memories fail noticeably. A possible problem for building kernels with sufficient conditions, using binary and gray-scale images, is not knowing how the noise is registered in these images. A solution to this problem is presented by analyzing the behavior of the acquisition noise. What is new about this analysis is that, noise can be mapped to a distance obtained by a distance transform. Furthermore, this analysis provides the basis for a new model of min heteroassociative memory that is robust to the acquisition/mixed noise. The proposed model is novel because min associative memories are typically inoperative to mixed noise. The new model of heteroassocitative memory obtains very interesting results with this type of noise.

Retrograde Amnesia – A Question of Disturbed Calcium Levels?

Retrograde amnesia is the inability to remember events or information. The successful acquisition and memory of information is required before retrograde amnesia may occur. Often, the trigger for retrograde amnesia is a traumatic event. Loss of memories may be caused in two ways: either by loss/erasure of the memory itself or by the inability to access the memory, which is still present. In general, memories and learning are associated with a positive connotation although the extinction of unpleasant experiences and memories of traumatic events may be highly welcome. In contrast to the many experimental models addressing learning deficits caused by anterograde amnesia, the incapability to acquire new information, retrograde amnesia could so far only be investigated sporadically in human patients and in a limited number of model systems. Apart from models and diseases in which neurodegeneration or dementia like Alzheimer’s disease result in loss of memory, retrograde amnesia can be elicited by various drugs of which alcohol is the most prominent one and exemplifies the non-specific effects and the variable duration. External or internal impacts like traumatic brain injury, stroke, or electroconvulsive treatments may similarly result in variable degrees of retrograde amnesia. In this review, I will discuss a new genetic approach to induce retrograde amnesia in a mouse model and raise the hypothesis that retrograde amnesia is caused by altered intracellular calcium homeostasis. Recently, we observed that neuronal loss of neuroplastin resulted in retrograde amnesia specifically for associative memories. Neuroplastin is tightly linked to the expression of the main Ca2+ extruding pumps, the plasma membrane calcium ATPases (PMCAs). Therefore, neuronal loss of neuroplastin may block the retrieval and storage of associative memories by interference with Ca2+ signaling cascades. The possibility to elicit retrograde amnesia in a controlled manner allows to investigate the underlying mechanisms and may provide a deeper understanding of the molecular and circuit processes of memory.

Complementary population codes in the dorsal and ventral hippocampus during associative learning

Memories are multifaceted and layered, incorporating external stimuli and internal states, and at multiple levels of resolution. Although the hippocampus is essential for memory, it remains unclear if distinct aspects of experience are encoded within different hippocampal subnetworks during learning. By tracking the same dCA1 or vCA1 neurons across cue-outcome learning, we find detailed and externally based (stimulus identity) representations in dCA1, and broad and internally based (stimulus relevance) signals in vCA1 that emerge with learning. These dorsoventral differences were observed regardless of cue modality or outcome valence, and representations within each region were largely stable for days after learning. These results identify how the hippocampus encodes associative memories, and show that hippocampal ensembles not only link experiences, but also imbue relationships with meaning and highlight behaviorally relevant information. Together, these complementary dynamics across hippocampal subnetworks allow for rich, diverse representation of experiences.

Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation

Change is ubiquitous in living beings. In particular, the connectome and neural representations can change. Nevertheless, behaviors and memories often persist over long times. In a standard model, associative memories are represented by assemblies of strongly interconnected neurons. For faithful storage these assemblies are assumed to consist of the same neurons over time. Here we propose a contrasting memory model with complete temporal remodeling of assemblies, based on experimentally observed changes of synapses and neural representations. The assemblies drift freely as noisy autonomous network activity and spontaneous synaptic turnover induce neuron exchange. The gradual exchange allows activity-dependent and homeostatic plasticity to conserve the representational structure and keep inputs, outputs, and assemblies consistent. This leads to persistent memory. Our findings explain recent experimental results on temporal evolution of fear memory representations and suggest that memory systems need to be understood in their completeness as individual parts may constantly change.

Mouse visual cortex areas represent perceptual and semantic features of learned visual categories

Associative memories are stored in distributed networks extending across multiple brain regions. However, it is unclear to what extent sensory cortical areas are part of these networks. Using a paradigm for visual category learning in mice, we investigated whether perceptual and semantic features of learned category associations are already represented at the first stages of visual information processing in the neocortex. Mice learned categorizing visual stimuli, discriminating between categories and generalizing within categories. Inactivation experiments showed that categorization performance was contingent on neuronal activity in the visual cortex. Long-term calcium imaging in nine areas of the visual cortex identified changes in feature tuning and category tuning that occurred during this learning process, most prominently in the postrhinal area (POR). These results provide evidence for the view that associative memories form a brain-wide distributed network, with learning in early stages shaping perceptual representations and supporting semantic content downstream.

Memory for individual items is related to nonreinforced preference change

It is commonly assumed that memories contribute to value-based decisions. Nevertheless, most theories of value-based decision-making do not account for memory influences on choice. Recently, new interest has emerged in the interactions between these two fundamental processes, mainly using reinforcement-based paradigms. Here, we aimed to study the role memory processes play in preference change following the nonreinforced cue-approach training (CAT) paradigm. In CAT, the mere association of cued items with a speeded motor response influences choices. Previous studies with this paradigm showed that a single training session induces a long-lasting effect of enhanced preferences for high-value trained stimuli, that is maintained for several months. We hypothesized that CAT increases memory of trained items, leading to enhanced accessibility of their positive associative memories and in turn to preference changes. In two preregistered experiments, we found evidence that memory is enhanced for trained items and that better memory is correlated with enhanced preferences at the individual item level, both immediately and 1 mo following CAT. Our findings suggest that memory plays a central role in value-based decision-making following CAT, even in the absence of external reinforcements. These findings contribute to new theories relating memory and value-based decision-making and set the groundwork for the implementation of novel nonreinforced behavioral interventions that lead to long-lasting behavioral change.