ReviewNeuronal ensembles in memory processes
Introduction
The study of neurophysiology was drastically influenced by Cajal and Sherrington that considered individual neurons and the linear flow of information as the axis to understand neuronal microcircuits [1], [2]. On the other hand, the exploration of memory was strongly impacted by Semon and Lashley that pondered the loci of engrams as the guiding principle to comprehend learning processes [3], [4]. Remarkably, both conjectures have evolved to converge in the idea proposed by Lorente de Nó [5] and developed by Hebb [6], that the interaction between recurrent groups of neurons defining ensembles could explain the mechanisms that support brain functions [7], [8], [9], [10], [11], [12], [13].
In the last decade, the knowledge about neuronal ensembles in different brain areas has been expanded by the refinement of optical techniques to simultaneously record and manipulate the activity of identified neuronal populations [14], [15], [16], [17], [18]. A neuronal ensemble is a group of neurons with coordinated and recurrent activity related to a particular function, experimental condition or feature of a mental state, and could be mathematically represented as a population vector [8]. In this way, groups of neurons related to several brain functions have been described all over the brain. It has been proposed that neuronal ensembles could work as distributed systems [19] where ensembles in different brain regions may support distinctive aspects of brain computations [20], [21], provide general contextual information as an integrated index [22], [23], [24] or work as classification systems [25]. In parallel, the study of memory engrams has been catapulted by the development of engram tagging strategies that allow the visualization and manipulation of cells involved in learning paradigms [22], [26], [27], [28], [29], [30], [31], [32], [33]. For memory processes a permeating idea is that neuronal ensembles could represent specific attributes of engrams [9], [10], [12], [34]. Despite the nuances in the working mechanisms of general brain functions and memories, engram’s questions have revolved around which neurons are active instead of how population activity patterns define memory traces.
The relevance of recent studies linking population activity and interventional paradigms resides in the demonstration that the targeted activation of selected neuronal ensembles can enhance, disrupt or evoke learned behaviors [35], [36], [37], [38] suggesting that a few neurons could engage a cascade of events across brain areas to control memory processes.
Extensive contemporary descriptions of what is a neuronal ensemble or what is a memory engram have been recently published [8], [10]. Ergo, the focus of this short review is to propose how memory engrams could be interpreted from the neuronal ensemble framework with the purpose to move forward concepts and questions that could drive new experiments to understand the role of neuronal ensembles in the storage, retrieval and update of information.
Section snippets
Role of neuronal ensembles in memory processes
Understanding the role of different brain structures in learning requires the careful consideration of the type of memory studied [39]. The ideal scenario is to use at first simple learning paradigms and then scale them to complex memories or behaviors [8], [40]. Studies of engrams have focused on the identification of neurons associated with memory acquisition, consolidation and retrieval. In this way, it has been described that the enhancement of intrinsic excitability bias the inclusion of
Interacting neuronal ensembles define memory engrams
How could the interaction between neuronal ensembles be related to memory processes? It has been proposed that in order to create a neuronal ensemble a group of neurons should fire together in a physiologically meaningful time window allowing the strengthening of the functional connections between such neurons [6]. Along these lines, the genesis of ensembles could begin with the acquisition of information followed by the creation of an internal model of the world. After that, it is necessary to
Neuronal ensemble mechanisms related to engram reconfiguration
Experiments on engrams have shown that memories follow long-term potentiation (LTP) processes that can be measured as increased synapses or enhanced weights in the connectivity [81] between engram cells [43]. Accordingly, it has been shown that the shrinking of synapses is causally related to the deletion of memory traces in motor cortex [82]. Weak and strong memories label the same number of neurons suggesting that the strength of a memory is not dependent on the number of cells but on the
Pattern completion and pattern separation of neuronal ensembles to understand memory processes
So far, the mechanisms described for neuronal ensemble formation and reconfiguration highlighted the importance of characterizing population activity patterns to understand memory engrams. Hence, the next question should focus on how neuronal ensemble dynamics could be related to pattern completion and pattern separation, that are the hallmarks of memory storage and retrieval. Using simultaneous two-photon imaging and two-photon optogenetics the property of pattern completion was demonstrated
Neuronal ensembles and memory processes in pathological conditions
Neurodegenerative disorders are an increasing health problem due to higher life expectancy worldwide. It has been broadly demonstrated that brain disorders are related to pathological neuronal synchronization [111]; however, how sequential activity patterns between neuronal ensembles are related to neurodegenerative disorders [112], [113] and how optogenetics could be used to restore loss functions in memory impaired animal models are emergent fields in neuroscience [114].
It has been shown that
Computational approach to study neuronal ensembles related to memory
Once accepted that neuronal ensembles could work as basic building blocks [5], [13], the last question would be how to define a neuronal ensemble in a way that can be generalized to brain computations related to memory processes. One of many solutions is to define neuronal ensembles as multidimensional population vectors where the dimensionality of the vectors is given by the total number of recorded cells instead of the temporal changes of individual cells [8], [40], [88], [122].
Conclusions and perspectives
The methods to characterize neuronal ensembles and their functional connectivity properties could be useful to depict different attributes of an engram allowing the understanding of the role of specific neurons in the consolidation and retrieval of a whole memory trace. The conjoint use of the engram and ensemble frameworks could reveal the circuit mechanisms of generalized brain functions, including memory processes, bridging both fields.
Studying the temporal and spatial properties of neuronal
Funding source
Dirección General de Asuntos del Personal Académico de la Universidad Nacional Autónoma de México PAPIIT (IA201421). CONACyT (CF6653, CF154039).
Declaration of competing interest
The author declares no competing financial interests.
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