The physical basis of memory

Autoimmune hypothesis of Alzheimer's disease: unanswered question

Alzheimer's disease (AD) was described more than a century ago. However, there are still no effective approaches to its treatment, which may suggest that the search for the cure is not being conducted in the most productive direction. AD begins as selective impairments of declarative memory with no deficits in other cognitive functions. Therefore, understanding of the AD pathogenesis has to include the understanding of this selectivity. Currently, the main efforts aimed at prevention and treatment of AD are based on the dominating hypothesis for the AD pathogenesis: the amyloid hypothesis. But this hypothesis does not explain selective memory impairments since β-amyloid accumulates extracellularly and should be toxic to all types of cerebral neurons, not only to "memory engram neurons." To explain selective memory impairment, I propose the autoimmune hypothesis of AD, based on the analysis of risk factors for AD and molecular mechanisms of memory formation. Memory formation is associated with epigenetic modifications of chromatin in memory engram neurons and, therefore, might be accompanied by the expression of memory-specific proteins recognized by the adaptive immune system as "non-self" antigens. Normally, the brain is protected by the blood-brain barrier (BBB). All risk factors for AD provoke BBB disruptions, possibly leading to an autoimmune reaction against memory engram neurons. This reaction would make them selectively sensitive to tauopathy. If this hypothesis is confirmed, the strategies for AD prevention and treatment would be radically changed.

Neurobiological Causal Models of Language Processing

The language faculty is physically realized in the neurobiological infrastructure of the human brain. Despite significant efforts, an integrated understanding of this system remains a formidable challenge. What is missing from most theoretical accounts is a specification of the neural mechanisms that implement language function. Computational models that have been put forward generally lack an explicit neurobiological foundation. We propose a neurobiologically informed causal modeling approach which offers a framework for how to bridge this gap. A neurobiological causal model is a mechanistic description of language processing that is grounded in, and constrained by, the characteristics of the neurobiological substrate. It intends to model the generators of language behavior at the level of implementational causality. We describe key features and neurobiological component parts from which causal models can be built and provide guidelines on how to implement them in model simulations. Then we outline how this approach can shed new light on the core computational machinery for language, the long-term storage of words in the mental lexicon and combinatorial processing in sentence comprehension. In contrast to cognitive theories of behavior, causal models are formulated in the "machine language" of neurobiology which is universal to human cognition. We argue that neurobiological causal modeling should be pursued in addition to existing approaches. Eventually, this approach will allow us to develop an explicit computational neurobiology of language.

Memory: Synaptic or Cellular, That Is the Question

According to the commonly accepted opinion, memory engrams are formed and stored at the level of neural networks due to a change in the strength of synaptic connections between neurons. This hypothesis of synaptic plasticity (HSP), formulated by Donald Hebb in the 1940s, continues to dominate the directions of experimental studies and the interpretations of experimental results in the field. The universal acceptance of the HSP has transformed it from a hypothesis into an incontrovertible theory. In this article, I show that the entire body of experimental and clinical data obtained in studies of long-term memory in mammals and humans is inconsistent with the HSP. Instead, these data suggest that long-term memory is formed and stored at the intracellular level where it is reliably protected from ongoing synaptic activity, including pathological epileptic activity. It seems that the generally accepted HSP became a serious obstacle to understanding the mechanisms of memory and that progress in this field requires rethinking this doctrine and shifting experimental efforts toward exploring the intracellular mechanisms.

A possible mechanism of neural read-out from a molecular engram

What is the physical basis of declarative memory? The predominant view holds that stored information is embedded in the structure of a neural net, that is, in the signs and weights of its synaptic connections. An alternative possibility is that storage and processing are separated, and that the engram is encoded chemically, most probably in the sequence of a nucleic acid. One deterrent to adoption of the latter hypothesis has been the difficulty of envisaging how neural actively could be converted to and from a molecular code. Our purpose here is limited to suggesting how a molecular sequence could be read out from nucleic acid to neural activity by means of nanopores.

The molecular memory code and synaptic plasticity: A synthesis

The most widely accepted view of memory in the brain holds that synapses are the storage sites of memory, and that memories are formed through associative modification of synapses. This view has been challenged on conceptual and empirical grounds. As an alternative, it has been proposed that molecules within the cell body are the storage sites of memory, and that memories are formed through biochemical operations on these molecules. This paper proposes a synthesis of these two views, grounded in a computational model of memory. Synapses are conceived as storage sites for the parameters of an approximate posterior probability distribution over latent causes. Intracellular molecules are conceived as storage sites for the parameters of a generative model. The model stipulates how these two components work together as part of an integrated algorithm for learning and inference.

Problems and Mysteries of the Many Languages of Thought

"What is the structure of thought?" is as central a question as any in cognitive science. A classic answer to this question has appealed to a Language of Thought (LoT). We point to emerging research from disparate branches of the field that supports the LoT hypothesis, but also uncovers diversity in LoTs across cognitive systems, stages of development, and species. Our letter formulates open research questions for cognitive science concerning the varieties of rules and representations that underwrite various LoT-based systems and how these variations can help researchers taxonomize cognitive systems.

The molecular diversity of plasticity mechanisms underlying memory: An evolutionary perspective

Experience triggers molecular cascades in organisms (learning) that lead to alterations (memory) to allow the organism to change its behavior based on experience. Understanding the molecular mechanisms underlying memory, particularly in the nervous system of animals, has been an exciting scientific challenge for neuroscience. We review what is known about forms of neuronal plasticity that underlie memory highlighting important issues in the field: (1) the importance of being able to measure how neurons are activated during learning to identify the form of plasticity that underlies memory, (2) the many distinct forms of plasticity important for memories that naturally decay both within and between organisms, and (3) unifying principles underlying the formation and maintenance of long-term memories. Overall, the diversity of molecular mechanisms underlying memories that naturally decay contrasts with more unified molecular mechanisms implicated in long-lasting changes. Despite many advances, important questions remain as to which mechanisms of neuronal plasticity underlie memory.

Quantitative properties of the creation and activation of a cell-intrinsic duration-encoding engram

The engram encoding the interval between the conditional stimulus (CS) and the unconditional stimulus (US) in eyeblink conditioning resides within a small population of cerebellar Purkinje cells. CSs activate this engram to produce a pause in the spontaneous firing rate of the cell, which times the CS-conditional blink. We developed a Bayesian algorithm that finds pause onsets and offsets in the records from individual CS-alone trials. We find that the pause consists of a single unusually long interspike interval. Its onset and offset latencies and their trial-to-trial variability are proportional to the CS-US interval. The coefficient of variation (CoV = σ/μ) are comparable to the CoVs for the conditional eye blink. The average trial-to-trial correlation between the onset latencies and the offset latencies is close to 0, implying that the onsets and offsets are mediated by two stochastically independent readings of the engram. The onset of the pause is step-like; there is no decline in firing rate between the onset of the CS and the onset of the pause. A single presynaptic spike volley suffices to trigger the reading of the engram; and the pause parameters are unaffected by subsequent volleys. The Fano factors for trial-to-trial variations in the distribution of interspike intervals within the intertrial intervals indicate pronounced non-stationarity in the endogenous spontaneous spiking rate, on which the CS-triggered firing pause supervenes. These properties of the spontaneous firing and of the engram read out may prove useful in finding the cell-intrinsic, molecular-level structure that encodes the CS-US interval.

Understanding the physical basis of memory: Molecular mechanisms of the engram

Memory, defined as the storage and use of learned information in the brain, is necessary to modulate behavior and critical for animals to adapt to their environments and survive. Despite being a cornerstone of brain function, questions surrounding the molecular and cellular mechanisms of how information is encoded, stored, and recalled remain largely unanswered. One widely held theory is that an engram is formed by a group of neurons that are active during learning, which undergoes biochemical and physical changes to store information in a stable state, and that are later reactivated during recall of the memory. In the past decade, the development of engram labeling methodologies has proven useful to investigate the biology of memory at the molecular and cellular levels. Engram technology allows the study of individual memories associated with particular experiences and their evolution over time, with enough experimental resolution to discriminate between different memory processes: learning (encoding), consolidation (the passage from short-term to long-term memories), and storage (the maintenance of memory in the brain). Here, we review the current understanding of memory formation at a molecular and cellular level by focusing on insights provided using engram technology.

What kind of network is the brain?

The different areas of the cerebral cortex are linked by a network of white matter, comprising the myelinated axons of pyramidal cells. Is this network a neural net, in the sense that representations of the world are embodied in the structure of the net, its pattern of nodes, and connections? Or is it a communications network, where the same physical substrate carries different information from moment to moment? This question is part of the larger question of whether the brain is better modeled by connectionism or by symbolic artificial intelligence (AI), but we review it in the specific context of the psychophysics of stimulus comparison and the format and protocol of information transmission over the long-range tracts of the brain.

Behaviorist approaches to investigating memory and learning: A primer for synthetic biology and bioengineering

The fields of developmental biology, biomedicine, and artificial life are being revolutionized by advances in synthetic morphology. The next phase of synthetic biology and bioengineering is resulting in the construction of novel organisms (biobots), which exhibit not only morphogenesis and physiology but functional behavior. It is now essential to begin to characterize the behavioral capacity of novel living constructs in terms of their ability to make decisions, form memories, learn from experience, and anticipate future stimuli. These synthetic organisms are highly diverse, and often do not resemble familiar model systems used in behavioral science. Thus, they represent an important context in which to begin to unify and standardize vocabulary and techniques across developmental biology, behavioral ecology, and neuroscience. To facilitate the study of behavior in novel living systems, we present a primer on techniques from the behaviorist tradition that can be used to probe the functions of any organism – natural, chimeric, or synthetic – regardless of the details of their construction or origin. These techniques provide a rich toolkit for advancing the fields of synthetic bioengineering, evolutionary developmental biology, basal cognition, exobiology, and robotics.

Information and the single cell

Understanding the evolution of cognition requires an understanding of the costs and benefits of neural computation. This requires analysis of neuronal circuitry in terms of information-processing efficiency, ultimately cashed out in terms of ATP expenditures relative to adaptive problem-solving abilities. Despite a preoccupation in neuroscience with the synapse as the source of stored neural information, it is clear that, along with synaptic weights and electrochemical dynamics, neurons have multiple mechanisms which store and process information, including 'wetware' (protein phosphorylation, gene transcription, and so on) and cell morphology (dendritic form). Insights into non-synaptic information-processing can be gained by examining the surprisingly complex abilities of single-celled organisms ('cellular cognition') because neurons share many of the same abilities. Cells provide the fundamental level at which information processing interfaces with gene expression, and cell-internal information-processing mechanisms are both powerful and energetically efficient. Understanding cellular computation should be a central goal of research on cognitive evolution.

The central importance of nuclear mechanisms in the storage of memory

The neurobiological nature of the memory trace (engram) remains controversial. The most widely accepted hypothesis at present is that long-term memory is stored as stable, learning-induced changes in synaptic connections. This hypothesis, the synaptic plasticity hypothesis of memory, is supported by extensive experimental data gathered from over 50 years of research. Nonetheless, there are important mnemonic phenomena that the synaptic plasticity hypothesis cannot, or cannot readily, account for. Furthermore, recent work indicates that epigenetic and genomic mechanisms play heretofore underappreciated roles in memory. Here, we critically assess the evidence that supports the synaptic plasticity hypothesis and discuss alternative non-synaptic, nuclear mechanisms of memory storage, including DNA methylation and retrotransposition. We argue that long-term encoding of memory is mediated by nuclear processes; synaptic plasticity, by contrast, represents a means of relatively temporary memory storage. In addition, we propose that memories are evaluated for their mnemonic significance during an initial period of synaptic storage; if assessed as sufficiently important, the memories then undergo nuclear encoding.