Transient cell assembly networks encode stable spatial memories

Babichev, Andrey; Dabaghian, Yu.

doi:10.1038/s41598-017-03423-3

Cited by 13 publications

(24 citation statements)

References 79 publications

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“…Second, line or ring formations would often break apart and re-form new configurations that typically involved other agents or formations that were able to phase-synchronize with elements of the subgroup. These alternating disintegrative and aggregative dynamics may be consistent with analyses of persistent homologies in place-cell networks with transient connectivity (Babichev and Dabaghian, 2017).…”

Section: Emergent Swarming Behaviorssupporting

confidence: 81%

Cognitive swarming in complex environments with attractor dynamics and oscillatory computing

et al. 2020

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Neurobiological theories of spatial cognition developed with respect to recording data from relatively small and/or simplistic environments compared to animals' natural habitats. It has been unclear how to extend theoretical models to large or complex spaces. Complementarily, in autonomous systems technology, applications have been growing for distributed control methods that scale to large numbers of lowfootprint mobile platforms. Animals and many-robot groups must solve common problems of navigating complex and uncertain environments. Here, we introduce the 'NeuroSwarms' control framework to investigate whether adaptive, autonomous swarm control of minimal artificial agents can be achieved by direct analogy to neural circuits of rodent spatial cognition. NeuroSwarms analogizes agents to neurons and swarming groups to recurrent networks. We implemented neuron-like agent interactions in which mutually visible agents operate as if they were reciprocally-connected place cells in an attractor network. We attributed a phase state to agents to enable patterns of oscillatory synchronization similar to hippocampal models of theta-rhythmic (5-12 Hz) sequence generation. We demonstrate that multi-agent swarming and rewardapproach dynamics can be expressed as a mobile form of Hebbian learning and that NeuroSwarms supports a single-entity paradigm that directly informs theoretical models of animal cognition. We present emergent behaviors including phase-organized rings and trajectory sequences that interact with environmental cues and geometry in large, fragmented mazes. Thus, Neu-roSwarms is a model artificial spatial system that integrates autonomous control and theoretical neuroscience to potentially uncover common principles to advance both domains.

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Section: Emergent Swarming Behaviorssupporting

confidence: 81%

Cognitive swarming in complex environments with attractor dynamics and oscillatory computing

et al. 2020

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“…In the current model, enabled by a much more powerful Zigzag persistent homology theory [ 34 – 36 ], we employ an alternative approach, in which the links of the coactivity graph appear instantly following pairwise place cell coactivity events. Thus, in contrast with the model discussed in [ 58 ], the current model involves no selection of the “winning” coactivity links, which one might hold responsible for stabilizing the shapes of the flickering coactivity complexes. Nevertheless, this model demonstrates the same effect: the large-scale topological shapes of resulting coactivity complexes stabilize, given that the connections decay sufficiently slowly and have sufficiently broadly distributed lifetimes.…”

Section: Discussionmentioning

confidence: 88%

“…Previously, we investigated this effect using an alternative model of transient cell assemblies, in which the connections were constructed by identifying the pool of cells that spike within a certain “coactivity window,” , and building the coactivity graph from the most frequently cofiring pairs of neurons [ 58 ]. The accumulation of topological information within each -period, was then described using persistent homology theory techniques.…”

Section: Discussionmentioning

confidence: 99%

Robust spatial memory maps encoded by networks with transient connections

2018

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The spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space—a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long period. However, the neuronal substrate that produces this map is transient: the synaptic connections in the hippocampus and in the downstream neuronal networks never cease to form and to deteriorate at a rapid rate. How can the brain maintain a robust, reliable representation of space using a network that constantly changes its architecture? We address this question using a computational framework that allows evaluating the effect produced by the decaying connections between simulated hippocampal neurons on the properties of the cognitive map. Using novel Algebraic Topology techniques, we demonstrate that emergence of stable cognitive maps produced by networks with transient architectures is a generic phenomenon. The model also points out that deterioration of the cognitive map caused by weakening or lost connections between neurons may be compensated by simulating the neuronal activity. Lastly, the model explicates the importance of the complementary learning systems for processing spatial information at different levels of spatiotemporal granularity.

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“…The algebraic-topological properties of the coactivity complexes were studied in Dabaghian et al ( 2012 ), Babichev et al ( 2016b ), and Babichev and Dabaghian ( 2017a , b ). There it was demonstrated that if place cell populations operate within biological parameters, then the number of topological loops in different dimensions of the coactivity complex—the Betti numbers

(Munkres, 2000 )—match the Betti numbers of the environment

.…”

Section: Resultsmentioning

confidence: 99%

“…Importantly, the learning times and other global characteristics of

produced via algebraic topology techniques are insensitive to many details of the place cell spiking activity (Dabaghian et al, 2012 ; Babichev et al, 2016b ; Babichev and Dabaghian, 2017a , b ). For example, the learning time T min depends mostly on the mean place field sizes and the mean peak firing rates, but it does not depend strongly on the spatial layout of the place fields or on the limited spiking variations.…”

Section: Resultsmentioning

confidence: 99%

Topological Schemas of Memory Spaces

Babichev

Dabaghian

2018

Front. Comput. Neurosci.

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Hippocampal cognitive map—a neuronal representation of the spatial environment—is widely discussed in the computational neuroscience literature for decades. However, more recent studies point out that hippocampus plays a major role in producing yet another cognitive framework—the memory space—that incorporates not only spatial, but also non-spatial memories. Unlike the cognitive maps, the memory spaces, broadly understood as “networks of interconnections among the representations of events,” have not yet been studied from a theoretical perspective. Here we propose a mathematical approach that allows modeling memory spaces constructively, as epiphenomena of neuronal spiking activity and thus to interlink several important notions of cognitive neurophysiology. First, we suggest that memory spaces have a topological nature—a hypothesis that allows treating both spatial and non-spatial aspects of hippocampal function on equal footing. We then model the hippocampal memory spaces in different environments and demonstrate that the resulting constructions naturally incorporate the corresponding cognitive maps and provide a wider context for interpreting spatial information. Lastly, we propose a formal description of the memory consolidation process that connects memory spaces to the Morris' cognitive schemas-heuristic representations of the acquired memories, used to explain the dynamics of learning and memory consolidation in a given environment. The proposed approach allows evaluating these constructs as the most compact representations of the memory space's structure.

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Transient cell assembly networks encode stable spatial memories

Cited by 13 publications

References 79 publications

Cognitive swarming in complex environments with attractor dynamics and oscillatory computing

Cognitive swarming in complex environments with attractor dynamics and oscillatory computing

Robust spatial memory maps encoded by networks with transient connections

Topological Schemas of Memory Spaces

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