Models that contain the Model Concept : Spatial Navigation

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    Models   Description
1.  Acetylcholine-modulated plasticity in reward-driven navigation (Zannone et al 2018)
"Neuromodulation plays a fundamental role in the acquisition of new behaviours. In previous experimental work, we showed that acetylcholine biases hippocampal synaptic plasticity towards depression, and the subsequent application of dopamine can retroactively convert depression into potentiation. We also demonstrated that incorporating this sequentially neuromodulated Spike- Timing-Dependent Plasticity (STDP) rule in a network model of navigation yields effective learning of changing reward locations. Here, we employ computational modelling to further characterize the effects of cholinergic depression on behaviour. We find that acetylcholine, by allowing learning from negative outcomes, enhances exploration over the action space. We show that this results in a variety of effects, depending on the structure of the model, the environment and the task. Interestingly, sequentially neuromodulated STDP also yields flexible learning, surpassing the performance of other reward-modulated plasticity rules."
2.  Active dendritic integration in robust and precise grid cell firing (Schmidt-Hieber et al 2017)
"... Whether active dendrites contribute to the generation of the dual temporal and rate codes characteristic of grid cell output is unknown. We show that dendrites of medial entorhinal cortex neurons are highly excitable and exhibit a supralinear input–output function in vitro, while in vivo recordings reveal membrane potential signatures consistent with recruitment of active dendritic conductances. By incorporating these nonlinear dynamics into grid cell models, we show that they can sharpen the precision of the temporal code and enhance the robustness of the rate code, thereby supporting a stable, accurate representation of space under varying environmental conditions. Our results suggest that active dendrites may therefore constitute a key cellular mechanism for ensuring reliable spatial navigation."
3.  Biologically-plausible models for spatial navigation (Cannon et al 2003)
Hypotheses about how parahippocampal and hippocampal structures may be involved in spatial navigation tasks are implemented in a model of a virtual rat navigating through a virtual environment in search of a food reward. The model incorporates theta oscillations to separate encoding from retrieval and yields testable predictions about the phase relations of spiking activity to theta oscillations in different parts of the hippocampal formation at various stages of the behavioral task. See paper for more and details.
4.  CA1 network model: interneuron contributions to epileptic deficits (Shuman et al 2019)
Temporal lobe epilepsy causes significant cognitive deficits in both humans and rodents, yet the specific circuit mechanisms underlying these deficits remain unknown. There are profound and selective interneuron death and axonal reorganization within the hippocampus of both humans and animal models of temporal lobe epilepsy. To assess the specific contribution of these mechanisms on spatial coding, we developed a biophysically constrained network model of the CA1 region that consists of different subtypes of interneurons. More specifically, our network consists of 150 cells, 130 excitatory pyramidal cells and 20 interneurons (Fig. 1A). To simulate place cell formation in the network model, we generated grid cell and place cell inputs from the Entorhinal Cortex (ECLIII) and CA3 regions, respectively, activated in a realistic manner as observed when an animal transverses a linear track. Realistic place fields emerged in a subpopulation of pyramidal cells (40-50%), in which similar EC and CA3 grid cell inputs converged onto distal/proximal apical and basal dendrites. The tuning properties of these cells are very similar to the ones observed experimentally in awake, behaving animals To examine the role of interneuron death and axonal reorganization in the formation and/or tuning properties of place fields we selectively varied the contribution of each interneuron type and desynchronized the two excitatory inputs. We found that desynchronized inputs were critical in reproducing the experimental data, namely the profound reduction in place cell numbers, stability and information content. These results demonstrate that the desynchronized firing of hippocampal neuronal populations contributes to poor spatial processing in epileptic mice, during behavior. Given the lack of experimental data on the selective contributions of interneuron death and axonal reorganization in spatial memory, our model findings predict the mechanistic effects of these alterations at the cellular and network levels.
5.  CA1 pyr cell: Inhibitory modulation of spatial selectivity+phase precession (Grienberger et al 2017)
Spatially uniform synaptic inhibition enhances spatial selectivity and temporal coding in CA1 place cells by suppressing broad out-of-field excitation.
6.  Decoding movement trajectory from simulated grid cell population activity (Bush & Burgess 2019)
Matlab code to simulate a population of grid cells that exhibit both a rate and phase code for location in 1D or 2D environments, and are modulated by a human hippocampal LFP signal with highly variable frequency; then subsequently decode location, running speed, movement direction and an arbitrary fourth variable from population firing rates and phases in each oscillatory cycle.
7.  Generation of stable heading representations in diverse visual scenes (Kim et al 2019)
"Many animals rely on an internal heading representation when navigating in varied environments. How this representation is linked to the sensory cues that define different surroundings is unclear. In the fly brain, heading is represented by ‘compass’ neurons that innervate a ring-shaped structure known as the ellipsoid body. Each compass neuron receives inputs from ‘ring’ neurons that are selective for particular visual features; this combination provides an ideal substrate for the extraction of directional information from a visual scene. Here we combine two-photon calcium imaging and optogenetics in tethered flying flies with circuit modelling, and show how the correlated activity of compass and visual neurons drives plasticity, which flexibly transforms two-dimensional visual cues into a stable heading representation. ... " See the supplementary information for model details.
8.  Grid cell spatial firing models (Zilli 2012)
This package contains MATLAB implementations of most models (published from 2005 to 2011) of the hexagonal firing field arrangement of grid cells.
9.  Hierarchical anti-Hebbian network model for the formation of spatial cells in 3D (Soman et al 2019)
This model shows how spatial representations in 3D space could emerge using unsupervised neural networks. Model is a hierarchical one which means that it has multiple layers, where each layer has got a specific function to achieve. This architecture is more of a generalised one i.e. it gives rise to different kinds of spatial representations after training.
10.  Hippocampus CA1 Interneuron Specific 3 (IS3) in vivo-like virtual NN simulations (Luo et al 2020)
"Disinhibition is a widespread circuit mechanism for information selection and transfer. In the hippocampus, disinhibition of principal cells is provided by the interneuron-specific interneurons that express the vasoactive intestinal polypeptide (VIP-IS) and innervate selectively inhibitory interneurons. By combining optophysiological experiments with computational models, we determined the impact of synaptic inputs onto the network state-dependent recruitment of VIP-IS cells. We found that VIP-IS cells fire spikes in response to both the Schaffer collateral and the temporoammonic pathway activation. Moreover, by integrating their intrinsic and synaptic properties into computational models, we predicted recruitment of these cells between the rising phase and peak of theta oscillation and during ripples. Two-photon Ca2+-imaging in awake mice supported in part the theoretical predictions, revealing a significant speed modulation of VIP-IS cells and their preferential albeit delayed recruitment during theta-run epochs, with estimated firing at the rising phase and peak of the theta cycle. However, it also uncovered that VIP-IS cells are not activated during ripples. Thus, given the preferential theta-modulated firing of VIP-IS cells in awake hippocampus, we postulate that these cells may be important for information gating during spatial navigation and memory encoding."
11.  MEC layer II stellate cell: Synaptic mechanisms of grid cells (Schmidt-Hieber & Hausser 2013)
This study investigates the cellular mechanisms of grid field generation in Medial Entorhinal Cortex (MEC) layer II stellate cells.
12.  Models of Vector Navigation with Grid Cells (Bush et al., 2015)
Four models of vector navigation in large scale 2D space using grid cell representations of location are included: (1) The 'Distance Cell' model, which directly decodes absolute start and goal locations in allocentric space from rate-coded grid cell representations before computing the displacement between them; (2) The 'Rate-coded Vector Cell' model, which directly decodes the displacement between start and goal locations from rate-coded grid cell representations; (3) The 'Phase-coded Vector Cell' model, which directly decodes the displacement between start and goal locations from the temporally-coded grid cell representations provided by phase precession; (4) The 'Linear Look-ahead' model, which uses a directed search through grid cell representations, initiated at the start location and then moving along a specific axis at a constant speed, to compute the displacement between start and goal locations.
13.  Odor supported place cell model and goal navigation in rodents (Kulvicius et al. 2008)
" ... Here we model odor supported place cells by using a simple feed-forward network and analyze the impact of olfactory cues on place cell formation and spatial navigation. The obtained place cells are used to solve a goal navigation task by a novel mechanism based on self-marking by odor patches combined with a Q-learning algorithm. We also analyze the impact of place cell remapping on goal directed behavior when switching between two environments. ..."
14.  Spikes,synchrony,and attentive learning by laminar thalamocort. circuits (Grossberg & Versace 2007)
"... The model hereby clarifies, for the first time, how the following levels of brain organization coexist to realize cognitive processing properties that regulate fast learning and stable memory of brain representations: single cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and acetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions; aggregate cell recordings, such as current-source densities and local field potentials; and single cell and large-scale inter-areal oscillations in the gamma and beta frequency domains. ..."

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