Biology subjects

Krstulovic, M.

Publications and source records attributed to Krstulovic, M..

4 recordsLinked to original sources

bioRxiv2023

Simultaneous representation of multiple time horizons by entorhinal grid cells and CA1 place cells

Grid cells and place cells constitute the basic building blocks of the medial entorhinal-hippocampal spatial cognitive map by representing the spatiotemporal continuum of an animals past, present and future locations. However, the spatiotemporal relationship between these different cell types is unclear. Here we co-recorded grid and place cells in freely foraging rats. We show that average time shifts in grid cells tend to be prospective and are proportional to their spatial scale, providing a nearly instantaneous readout of a spectrum of progressively increasing time horizons ranging hundreds of milliseconds. Average time shifts of place cells are generally larger compared to grid cells and also increase with place field sizes. Moreover, time shifts displayed nonlinear modulation by the animals trajectories in relation to the local boundaries and locomotion cues. Finally, long and short time shifts occurred at different parts of the theta cycle, which may facilitate their readout. Together, these findings suggest that progressively increasing time horizons of grid and place cells may provide a basis for calculating animal trajectories essential for goal-directed navigation and planning.

neuroscience↗

bioRxiv2023

Variational Log-Gaussian Point-Process Methods for Grid Cells

We present practical solutions to applying Gaussian-process methods to calculate spatial statistics for grid cells in large environments. Gaussian processes are a data efficient approach to inferring neural tuning as a function of time, space, and other variables. We discuss how to design appropriate kernels for grid cells, and show that a variational Bayesian approach to log-Gaussian Poisson models can be calculated quickly. This class of models has closed-form expressions for the evidence lower-bound, and can be estimated rapidly for certain parameterizations of the posterior covariance. We provide an implementation that operates in a low-rank spatial frequency subspace for further acceleration, and demonstrate these methods on experimental data.

neuroscience↗

bioRxiv2022

Unsupervised, frequent and remote: a novel platform for personalised digital phenotyping of spatial working memory and image recognition in humans

Spatial working memory and image recognition tests are commonly used to facilitate the diagnosis of hippocampal-related neurological disorders such as Alzheimers disease due to their relatively high specificity and sensitivity to damage to the medial temporal lobes compared to standard commonly used clinical tests. Pathological changes in Alzheimers disease start years before the formal diagnosis is made, partially due to testing too late. To address this challenge, we developed a novel digital platform, hAge ( healthy Age), which integrates double spatial alternation, image recognition and visuospatial tasks for frequent remote unsupervised assessment of spatial and non-spatial working memory. 191 healthy adults (67% females, 18-81 years old) participated in the study. In line with findings using standard laboratory tests, we showed that performance on the spatial alternation task negatively correlated with inter-trial periods and performance levels on image recognition and visuospatial tasks can be controlled by varying image similarity. Importantly, we demonstrated that frequent engagement with the double spatial alternation task leads to a strong practice effect, previously identified as a potential measure of cognitive decline in MCI patients. Finally, we discuss how lifestyle and motivation confounds may present a serious challenge for cognitive assessment in real-world uncontrolled environments.

neuroscience↗

bioRxiv2021

Scaling up to meet new challenges of brain and behaviour: Deep brain electrophysiology in freely moving sheep

While rodents are arguably the easiest animals to use for studying brain function, relying on them as model species for translational research comes with its own sets of limitations. Here, we propose sheep as a practical large animal species for in vivo brain function studies performed in naturalistic settings. To demonstrate their experimental usefulness, we performed proof-of-principle deep brain electrophysiological recording experiments from unrestrained sheep. Recordings were made from cortex and hippocampus both whilst sheep performed goal-directed behaviours (two-choice discrimination tasks), and across states of vigilance that included natural sleep. Hippocampal and cortical oscillatory rhythms were consistent with those seen in rodents and non-human primates, and included cortical alpha oscillations during immobility, hippocampal theta oscillations (5-6Hz) during locomotion and hippocampal sharp wave ripple oscillations ([~]150 Hz) during immobility. Moreover, we found clear examples of neurons whose activity was modulated by task, speed of locomotion, spatial position, reward and vigilance states. Recordings were conducted over a period of many months. Due to the exceptional stability of individual electrodes we were able to record from some neurons repeatedly across the span of a month. Together these experiments demonstrate that sheep are an excellent experimental animal model to use in longitudinal electrophysiological and imaging studies, particularly those requiring a large brained mammal, large scale recordings across distributed neuronal networks, experimentation outside the confounds of the traditional laboratory, or all the above concomitantly.

neuroscience↗