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Hamker, F. H.

Publications and source records attributed to Hamker, F. H..

6 recordsLinked to original sources

A systematic analysis of the joint effects of ganglion cells, lagged LGN cells, and intercortical inhibition on spatiotemporal processing and direction selectivity

Simple cells in the visual cortex process spatial as well as temporal information of the visual stream and enable the perception of motion information. Previous work suggests that the direction selectivity of V1 simple cells is associated with a temporal offset in the thalamocortical input stream through lagged and non-lagged cells of the lateral geniculate nucleus (LGN), but alternatively, it may also result from intercortical inhibition. While there exists a large corpus of models for spatiotemporal receptive fields, the majority of them built-in the spatiotemporal dynamics by utilizing a combination of spatial and temporal functions and thus, do not explain the emergence of spatiotemporal dynamics on basis of network dynamics emerging in the retina and the LGN. In order to better comprehend the emergence of spatiotemporal processing and direction selectivity, we used a spiking neural network to implement the visual pathway from the retina to the primary visual cortex. By varying different functional parts in our network, we demonstrate how the direction selectivity of simple cells emerges through the interplay between two components: tuned intercortical inhibition and a temporal offset in the feedforward path through lagged LGN cells. We observe that direction-selective simple cells are linked to a particular spiking pattern in a local excitatory-inhibitory circuit: If the stimulus moves in the non-preferred direction of a simple cell, inhibitory neurons with a different spatial position or tuning spike earlier, preventing the simple cell to spike. However, in the preferred direction, these inhibitory cells spike later, enabling the simple cell to spike.

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Virtual deep brain stimulation: Multiscale co-simulation of a spiking basal ganglia model and a whole-brain mean-field model with The Virtual Brain

Deep brain stimulation (DBS) has been successfully applied in various neurodegenerative diseases as an effective symptomatic treatment. However, its mechanisms of action within the brain network are still poorly understood. Many virtual DBS models analyze a subnetwork around the basal ganglia and its dynamics as a spiking network with their details validated by experimental data. However, connectomic evidence shows widespread effects of DBS affecting many different cortical and subcortical areas. From a clinical perspective, various effects of DBS besides the motoric impact have been demonstrated. The neuroinformatics platform The Virtual Brain (TVB) offers a modeling framework allowing us to virtually perform stimulation, including DBS, and forecast the outcome from a dynamic systems perspective prior to invasive surgery with DBS lead placement. For an accurate prediction of the effects of DBS, we implement a detailed spiking model of the basal ganglia, which we combine with TVB via our previously developed co-simulation environment. This multiscale co-simulation approach builds on the extensive previous literature of spiking models of the basal ganglia while simultaneously offering a whole-brain perspective on widespread effects of the stimulation going beyond the motor circuit. In the first demonstration of our model, we show that virtual DBS can move the firing rates of a Parkinsons disease patients thalamus - basal ganglia network towards the healthy regime while, at the same time, altering the activity in distributed cortical regions with a pronounced effect in frontal regions. Thus, we provide proof of concept for virtual DBS in a co-simulation environment with TVB. The developed modeling approach has the potential to optimize DBS lead placement and configuration and forecast the success of DBS treatment for individual patients. Highlights- We implement and validate a co-simulation approach of a spiking network model for subcortical regions in and around the basal ganglia and interface it with mean-field network models for each cortical region. - Our simulations are based on a normative connectome including detailed tracts between the cortex and the basal ganglia regions combined with subject-specific optimized weights for a healthy control and a patient with Parkinsons disease. - We provide proof of concept by demonstrating that the implemented model shows biologically plausible dynamics during resting state including decreased thalamic activity in the virtual patient and during virtual deep brain stimulation including normalized thalamic activity and distributed altered cortical activity predominantly in frontal regions. - The presented co-simulation model can be used to tailor deep brain stimulation for individual patients.

neuroscience↗

Enhanced habit formation in Tourette syndrome : dopamine release and striatal disinhibition modulate shortcut connections in a hierarchical model of cortico-basal ganglia loops

In the Gilles de la Tourette syndrome, tics are often considered as habitual responses towards unwanted premonitory urges. Support for the relationship between tics and habits comes from devaluation protocols, which reveal that unmedicated Tourette patients show an increased tendency towards responses to devalued outcomes. We use a neuro-computational model of hierarchically organized cortico-basal ganglia-thalamo-cortical loops to shed more light on enhanced habit formation of Tourette patients. In our model, habitual behavior emerges from cortico-thalamic shortcut connections, where enhanced habit formation can be linked to faster plasticity in the shortcut or to a stronger feedback from the shortcut to the basal ganglia. Irregular activity in such shortcut connections may have different pathophysiological origins. Based on our model, we explore decreased local striatal inhibition, which may correspond to a loss of inhibitory interneurons, and increased dopaminergic modulation of striatal medium spiny neurons as causes for irregular shortcut plasticity or activation. Both lead to higher rates of response towards devalued outcomes in our model, similar to what is observed in Tourette patients. Our results support the view of tics in Tourette syndrome as maladaptive habits. We suggest to reveal more shortcuts between cortico-basal ganglia-thalamo-cortical loops in the human brain and study their potential role in the development of the Tourette syndrome.

neuroscience↗

The control of plastic inhibition over excitatory synaptic plasticity leads to the joint emergence of sensory coding and contrast invariance.

Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results suggest that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. We show that inhibitory feedback (random and specific) increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight that (1) interneuron plasticity is key to the development of tuning diversity and (2) that efficient sensory representations are an emergent property of the resulting network.

neuroscience↗

Spatial updating of attention across eye movements: A neuro-computational approach

While scanning our environment, the retinal image changes with every saccade. Nevertheless, the visual system anticipates where an attended target will be next and attention is updated to the new location. Recently, two different types of perisaccadic attentional updates were discovered: Predictive remapping of attention before saccade onset (Rolfs, Jonikaitis, Deubel, & Cavanagh, 2011) as well as lingering of attention after saccade (Golomb, Chun, & Mazer, 2008; Golomb, Pulido, Albrecht, Chun, & Mazer, 2010). We here propose a neurocomputational model located in LIP based on a previous model of perisaccadic space perception (Ziesche & Hamker, 2011, 2014). Our model can account for both types of updating of attention at a neural systems level. The lingering effect originates from the late updating of the proprioceptive eye position signal and the remapping from the early corollary discharge signal. We put these results in relationship to predictive remapping of receptive fields and show that both phenomena arise from the same simple, recurrent neural circuit. Thus, together with the previously published results, the model provides a comprehensive framework to discuss multiple experimental observations that occur around saccades.

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A statistical note on the detection of autoassociative dynamics in hippocampal place-cell sequences

The sequential activity of hippocampal place cells observed during sleep and awake resting is widely viewed as a neural correlate of memory processes. While recent work has advanced our understanding of the content represented during rest-related place-cell sequences, the nature of hippocampal population dynamics during sequential activity remains poorly understood. A recent experimental study has reported that place-cell sequences show a pattern of step-like movement, reminiscent of transitions between discrete attractor states (Pfeiffer and Foster, 2015). By contrast, previous theoretical models predict that the spatiotemporal structure of place-cell sequences should reflect the disribution of place fields, typically observed to be spatially smooth.\n\nMotivated by this discrepancy between models and experimental data, we performed a quantitative comparison between these results and the spike trains generated by a network model for the generation of place-cell sequences (Gonner et al., 2017). Although the model is based on continuous attractor network dynamics, we observed that the movement of sequential place representations was phase-locked to the population oscillation, highly similar to the experimental data interpreted as evidence for discrete attractor dynamics. To resolve this potential contradiction, we performed a detailed analysis of the methodology used to identify discrete attractor dynamics. Our results show that a previous approach to step size decoding is prone to a decoding artefact. We propose a modified approach to estimate step sizes which may help to characterize the underlying circuit dynamics in vivo.

neuroscience↗