Heteroassociative storage of hippocampal pattern sequences in the CA3 subregion

Camargo, Raphael Y. de; Recio, Renan Schiavolin; Reyes, Marcelo Bussotti

doi:10.7717/peerj.4203

Cited by 9 publications

(12 citation statements)

References 66 publications

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“…The most promising candidate for this pacemaker is probably the gamma oscillation (approx. 40-120 Hz), as suggested previously [ 40 , 95 – 97 ]. The timescale of theta phase precession, which might serve to compress behavioral sequences on the timescale of seconds into the range of tens of milliseconds, fits well with the timescale of gamma oscillations.…”

Section: Discussionsupporting

confidence: 79%

Storage fidelity for sequence memory in the hippocampal circuit

et al. 2018

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Episodic memories have been suggested to be represented by neuronal sequences, which are stored and retrieved from the hippocampal circuit. A special difficulty is that realistic neuronal sequences are strongly correlated with each other since computational memory models generally perform poorly when correlated patterns are stored. Here, we study in a computational model under which conditions the hippocampal circuit can perform this function robustly. During memory encoding, CA3 sequences in our model are driven by intrinsic dynamics, entorhinal inputs, or a combination of both. These CA3 sequences are hetero-associated with the input sequences, so that the network can retrieve entire sequences based on a single cue pattern. We find that overall memory performance depends on two factors: the robustness of sequence retrieval from CA3 and the circuit’s ability to perform pattern completion through the feedforward connectivity, including CA3, CA1 and EC. The two factors, in turn, depend on the relative contribution of the external inputs and recurrent drive on CA3 activity. In conclusion, memory performance in our network model critically depends on the network architecture and dynamics in CA3.

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Section: Discussionsupporting

confidence: 79%

Storage fidelity for sequence memory in the hippocampal circuit

et al. 2018

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“…The proposed model works with neural networks that generate reproducible sequences of states at steady rates. Heteroassociative networks (Sompolinsky and Kanter, 1986;Camargo et al, 2018) can be used to implement our model as a biological neural network, as well as Synfire chains (Abeles, 1991;Miyata et al, 2013). The later introduced the idea of a rapid sequential activation of groups of neurons, representing the states in our model.…”

Section: Discussionmentioning

confidence: 99%

“…The later introduced the idea of a rapid sequential activation of groups of neurons, representing the states in our model. Accordingly, heteroassociative networks are promising as a biological implementation of the model, since they can produce long sequences of state activation within a single network while performing pattern completion (Camargo et al, 2018). This kind of networks are present in both the CA3 (Lisman et al, 2005) and CA1 (Miyata et al, 2013) subregions of the hippocampus, which is involved in some time-related tasks (Meck et al, 1984).…”

Section: Discussionmentioning

confidence: 99%

“…The model consists of a heteroassociative network represented by a series of successive states (Sompolinsky and Kanter, 1986;Camargo et al, 2018). Each state corresponds to a subset of active neural units within a Local Field Potential (LFP) oscillation.…”

Section: Learning Mechanismmentioning

confidence: 99%

“…Indeed, natural brain oscillations play a crucial role in many aspects of be-havior in the hippocampus (Klimesch, 1999;Belluscio et al, 2012), the thalamus (Steriade et al, 1993), striatum (Berke et al, 2004), amygdala (Halgren et al, 1977), and other areas. Interestingly, following the idea of series of active "states" proposed by LeT, areas such as the hippocampus are important for timing behavior (Meck et al, 1984) and appear to operate on sequences of states (Lisman et al, 2005;Buzsáki and Tingley, 2018), which could be chained using heteroassociative rules (Sompolinsky and Kanter, 1986;Camargo et al, 2018). The hippocampus has neurons, called time cells (Eichenbaum, 2014), that fire at specific moments during the timed interval.…”

Section: Introductionmentioning

confidence: 99%

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A Model for the Peak-Interval Task Based on Neural Oscillation-Delimited States

Varella

Reyes

Caetano

et al. 2018

Preprint

Self Cite

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Specific mechanisms underlying how the brain keeps track of time are largely unknown. Several existing computational models of timing reproduce behavioral results obtained with experimental psychophysical tasks, but only a few tackle the underlying biological mechanisms, such as the synchronized neural activity that occurs throughout brain areas. In this paper, we introduce a model for the peak-interval task based on neuronal network properties. We consider that Local Field Potential (LFP) oscillation cycles specify a sequence of states, represented as neuronal ensembles. Repeated presentation of time intervals during training reinforces the connections of specific ensembles to downstream networks. Later, during the peak-interval procedure, these downstream networks are reactivated by previously experienced neuronal ensembles, triggering actions at the learned time intervals. The model reproduces experimental response patterns from individual rats in the peak-interval procedure, satisfying relevant properties such as the Weber law. Finally, we provide a biological interpretation of the parameters of the model.

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A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Guo

Zou

et al. 2021

Advanced Intelligent Systems

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Neuromorphic electronics, an emerging field that aims for building electronic mimics of the biological brain, holds promise for reshaping the frontiers of information technology and enabling a more intelligent and efficient computing paradigm. As their biological brain counterpart, the neuromorphic electronic systems are complex, having multiple levels of organization. Inspired by David Marr's famous three‐level analytical framework developed for neuroscience, the advances in neuromorphic electronic systems are selectively surveyed and given significance to these research endeavors as appropriate from the computational level, algorithmic level, or implementation level. Under this framework, the problem of how to build a neuromorphic electronic system is defined in a tractable way. In conclusion, the development of neuromorphic electronic systems confronts a similar challenge to the one neuroscience confronts, that is, the limited constructability of the low‐level knowledge (implementations and algorithms) to achieve high‐level brain‐like (human‐level) computational functions. An opportunity arises from the communication among different levels and their codesign. Neuroscience lab‐on‐neuromorphic chip platforms offer additional opportunity for mutual benefit between the two disciplines.

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Heteroassociative storage of hippocampal pattern sequences in the CA3 subregion

Cited by 9 publications

References 66 publications

Storage fidelity for sequence memory in the hippocampal circuit

Storage fidelity for sequence memory in the hippocampal circuit

A Model for the Peak-Interval Task Based on Neural Oscillation-Delimited States

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

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