A model of topological mapping of space in bat hippocampus

Abstract

The mammalian hippocampus plays a key role in spatial learning and memory, but the exact nature of the hippocampal representation of space is still being explored. Recently, there has been a fair amount of success in modeling hippocampal spatial maps in rats, assuming a topological perspective on spatial information processing. In this article, we use the topological approach to study the formation of a 3D spatial map in bats, which produces several insights into neurophysiological mechanisms of the hippocampal spatial leaning. First, we demonstrate that, in order to produce accurate maps of the environment, place cell should be organized into functional groups, which can be interpreted as cell assemblies. Second, the model suggests that the readout neurons in these cell assemblies should function as integrators of synaptic inputs, rather than detectors of place cells' coactivity, which allows estimating the integration time window. Lastly, the model suggests that, in contrast with relatively slow moving rats, suppressing θ-precession in bats improves the place cells capacity to encode spatial maps, which is consistent with the experimental observations. © 2016 Wiley Periodicals, Inc.

Transient cell assembly networks encode stable spatial memories

“…2A).The specific algorithm of constructing the complex T CA may also reflect how neuronal coactivity is processed by the readout neurons. If these neurons function as "coincidence detectors," i.e., if they react to the spikes received within a short coactivity detection period w (typically, w ≈ 200 − 250 milliseconds [39,47]), then the maximal simplexes in the corresponding coincidence detection coactivity complex (denoted T * ) will appear instantaneously at the moments of the cell assemblies' ignitions [46,48]. Alternatively, if the readout neurons integrate the coactivity inputs from smaller parts of their respective assemblies over an extended coactivity integration period [49,50], then the appearance of the maximal simplexes in the corresponding input integration coactivity complex (denoted as T + ) will extend over time, reflecting the dynamics of synaptic integration.…”

Through synapses to spatial memory maps via a topological model

“…Phenomenological description of the synaptic parameters. In the previous studies, we demonstrated that such complexes can acquire a correct topological shape in a biologically plausible period of time, in both planar and in voluminous environments, provided that the simulated spiking parameters values fall into the biological range [14][15][16][17][18]. However, the organization and the dynamics of these complexes did not reflect the parameters of synaptic connectivity, e.g., the mechanisms of transferring, detecting and interpreting neuronal (co)activity in the hippocampus and in the downstream networks.…”

Topological Schemas of Cognitive Maps and Spatial Learning

“…Another alternative was suggested to us by our recent studies of hippocampal mapping of 3D spaces in bats, using two types of simplicial schemas. The results suggest that in the 3D case, the readout neurons in the place cell assemblies should operate by integrating synaptic inputs over working memory periods, rather than detecting coactivities on synaptic plasticity timescale (Hoffman et al, 2016 ). Of course, until these predictions are proved or disproved experimentally, their value is discussable; meanwhile, the schema approach allows theoretical reasoning and generates predictions about hippocampal neurophysiology.…”