Spatial navigation in geometric mazes

Navigation is defined as the capability of planning and performing a path from the current position towards a desired location. Different types, or strategies, of navigation are used by animals depending on the task they are trying to solve. Visible goals can be approached directly, while navigation to a hidden goal usually requires a memorized representation of relative positions of the goal and surrounding landmarks. Neurophysiological and behavioral experiments on rodents suggest that different brain areas are responsible for the expression of different navigation strategies. Specifically, dorsal striatum has been related to storage and recall of stimulus-response associations underlying simple goal-approaching behaviors, whereas hippocampus is thought to store the spatial representation of the environment. Such a representation is built during an unrewarded spatial exploration and appears to be employed in cases when simple stimulus-response strategies fail. Discovery of neurons with spatially correlated activity, i.e. place cells and grid cells, in the hippocampal formation complements behavioral and lesion data suggesting its role for spatial orientation. The overall objective of this work is to study the neurophysiological mechanisms underlying rodent spatial behavior, in particular those that are responsible for the implementation of different navigational strategies. Special attention is devoted to the question of how various types of sensory cues influence goal-oriented behavior. The model of a navigating rat described in this work is based on functional and anatomical properties of brain regions involved in encoding and storage of space representation and action generation. In particular, place and grid cells are modeled by two interconnected populations of artificial neurons. Together, they form a network for spatial learning, capable of combining different types of sensory inputs to produce a distributed representation of location. Goal-directed actions can be generated in the model via two different neural pathways: the first one drives stimulus-response behavior and associates visual input directly to motor responses; the second one associates motor actions with places and hence depends on the representation of location. The visual input is represented by responses of a large number of orientation-sensitive filters to visual images generated according to the position and orientation of the simulated rat in a virtual three-dimensional world. The model was tested in a large array of tasks designed by analogy to experimental studies on animal behavior. Results of several experimental studies, behavioral as wells as neurophysiological, were reproduced. Based on these results we formulated a hypothesis about the influence that the rat's perception of surrounding environment exerts on goal-oriented behavior. This hypothesis may provide an insight into several issues in animal behavior research that were not addressed by theoretical models until now.

HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data

Shailesh Appukuttan, Luca Leonardo Bologna, Szabolcs Kali, Carmen Alina Lupascu, Rosanna Migliore, Eilif Benjamin Muller, Armando Romani, Sàra Sàray, Werner Alfons Hilda Van Geit

Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match specific experimental observations. This limits the evaluation, re-use and further development of the existing models. Further, the development of new models could also be significantly facilitated by the ability to rapidly test the behavior of model candidates against the relevant collection of experimental data. We address these problems for the representative case of the CA1 pyramidal cell of the rat hippocampus by developing an open-source Python test suite, which makes it possible to automatically and systematically test multiple properties of models by making quantitative comparisons between the models and electrophysiological data. The tests cover various aspects of somatic behavior, and signal propagation and integration in apical dendrites. To demonstrate the utility of our approach, we applied our tests to compare the behavior of several different rat hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and evaluated how well these models match experimental observations in different domains. We also show how we employed the test suite to aid the development of models within the European Human Brain Project (HBP), and describe the integration of the tests into the validation framework developed in the HBP, with the aim of facilitating more reproducible and transparent model building in the neuroscience community. Author summary Anatomically and biophysically detailed neuronal models are useful tools in neuroscience because they allow the prediction of the behavior and the function of the studied cell type under circumstances that are hard to investigate experimentally. However, most detailed biophysical models have been built to capture a few selected properties of the real neuron, and it is often unknown how they would behave under different circumstances, or whether they can be used to successfully answer different scientific questions. To help the modeling community develop better neural models, and to make the process of model building more reproducible and transparent, we developed a test suite that enables the comparison of the behavior of models of neurons in the rat hippocampus and their evaluation against experimental data. Applying our tests to several models available in the literature enabled us to assess and compare how precisely each of these models is able to match various electrophysiological properties of the real neurons. We also use the test suite in the model development workflow of the European Human Brain Project to aid the construction of better models of hippocampal neurons and networks.

PUBLIC LIBRARY SCIENCE2021

Is there a geometric module for spatial orientation? Insights from a rodent navigation model

Angelo Arleo, Ricardo Andres Chavarriaga Lozano, Wulfram Gerstner, Thomas Strösslin

Modern psychological theories of spatial cognition postulate the existence of a geometric module for reorientation. This concept is derived from experimental data showing that in rectangular arenas with distinct landmarks in the corners, disoriented rats often make diagonal errors, suggesting their preference for the geometric (arena shape) over the nongeometric (landmarks) cues. Moreover, sensitivity of hippocampal cell firing to changes in the environment layout was taken in support of the geometric module hypothesis. Using a computational model of rat navigation, the authors proposed and tested the alternative hypothesis that the influence of spatial geometry on both behavioral and neuronal levels can be explained by the properties of visual features that constitute local views of the environment. Their modeling results suggest that the pattern of diagonal errors observed in reorientation tasks can be understood by the analysis of sensory information processing that underlies the navigation strategy employed to solve the task. In particular, 2 navigation strategies were considered: (a) a place-based locale strategy that relies on a model of grid and place cells and (b) a stimulus–response taxon strategy that involves direct association of local views with action choices. The authors showed that the application of the 2 strategies in the reorientation tasks results in different patterns of diagonal errors, consistent with behavioral data. These results argue against the geometric module hypothesis by providing a simpler and biologically more plausible explanation for the related experimental data. Moreover, the same model also describes behavioral results in different types of water-maze tasks. (PsycINFO Database Record (c) 2009 APA, all rights reserved)

American Psychological Association2009

Simple Neuron Models and Adaptation

Skander Mensi

Predicting activity of single neuron is an important part of the computational neuroscience and a great challenge. Several mathematical models exist, from the simple (one compartment and few parameters, like the SRM or the IF-type models), to the more complex (biophysical model like the Hodgkin-Huxley model). All these models have their own advantages and limitations, but no one is able to reproduce the exact behavior of real neurons. Multiple projects try to simulate complex neural networks, or even the whole brain (i.e. the blue brain project or other big network simulation). To achieve this goal it is very important that simple neuron models simulate, for a low computational cost, the precise activities of all neuron classes. It is well-known that neurons exhibit a lot of different activity patterns (from an electro-physiological point of view). Here we have focused on pyramidal neurons from the layer 5 of the neocortex. They are classified as regular spiking-cells. This cell type shows adaptation and like other neurons, refractoriness. Adaptation and refractoriness are very common neuronal activity in the brain, and so it is important to have a simple model which can reproduce this kind of activity. This project deals with two classical simple neuron models: the adaptive exponential integrate-and-fire model (AdEx) and the spike response model (SRM). We deter- mined the parameters of these two models using data generated with a detailed model, the Destexhe's model which is a HH-like model for cortical pyramidal cells, stimulated with different current injection scenarios. In a second time the model parameters have been set using data from in-vitro recordings of 4 layer 5 pyramidal rat neurons, stimulated with a sinusoid in vivo-like protocol, injected somatically in current-clamp configuration. Then we show that this type of model can capture adaptation and can reproduce the activity of neuron with a high reliability

2008