Bernstein Conference 2024 ePosters

Odor naming is considered as a particularly challenging task. In free odor naming scenarios people often fail to respond with any linguistic label to certain odors they smell, resulting in what is called an omission (i.e., a lack of response). The cognitive demands and the nature of interactions between olfaction and…

Dopamine plays a crucial role in the neural mechanisms underlying behavior, yet quantifying its impact through genetic modifications presents significant challenges, particularly due to the high-dimensional nature of the resulting data. In this study, we address these challenges by proposing a novel methodological…

In modern deep neural networks, the intricate learning dynamics of individual neurons often remain obscured due to global optimization methods. This contrasts with biological neural networks, which achieve robustness and efficiency through self-organized, local learning processes using limited global information. To…

Activity-dependent structural plasticity plays a pivotal role in the self-organization of neuronal networks. Emerging activity continuously feeds back into the growth process, regulating connectivity and activity homeostatically towards an optimal functional dynamic range. Transferred into cell culture, neurons follow…

Spiking Neural Networks (SNNs) promise energy efficiency and offer a more brain-like alternative to artificial neural networks. A major challenge to unleashing their potential, however, is to make them trainable on challenging tasks that require credit assignment across long time horizons. Gradient-based learning,…

Neural events are characteristic transient, coordinated neural activities observable in the brain’s aggregate signals, such as local field potentials (LFPs). It was shown that certain neural events, like sharp wave-ripples (SWR) [1], play a crucial role in various cognitive functions, including memory consolidation…

Spontaneous activity of neuron populations is often characterized by unique network events, ranging from sharp-wave ripples to slow propagating waves. One of the prominent examples of such network events is population bursting (Fig. 1a), robustly emerging in networks of primary neurons in vitro [1]. Population…

Exploring the origins of animal and human behavior within cortical circuits remains a puzzle awaiting resolution. It is established that when animals and humans compare two stimuli (d1 and d2) separated by a delay interval (Fig. 1a), their perception is influenced by a perceptual bias, characterized by a contraction…

A major question in computational neuroscience is what computational strategies the brain employs to represent the external world, the internal states, and its decisions. An open debate is whether neurons are organized in specialized sub-populations to encode behaviorally relevant variables ("categorical" selectivity)…

We present a comprehensive dataset of neural activity, compiled from 11 neuroimaging experiments in the nematode worm C. elegans. These source datasets, while collected under different experimental protocols, all measure neural activity via changes in calcium fluorescence in labeled subsets of the hermaphrodite worm’s…

It is appealing to disclose the state of an animal as well as the functional connectivity of its neuronal circuit by calculating pairwise and higher-order interactions (HOI) among neuronal activity. Here, we present a systematic approach to achieve these goals by analysing the spiking activity of simultaneously…

AMPA receptors (AMPARs) mediate fast excitatory neurotransmission and participate in memory formation. Synaptic strength is proportional to the number of AMPARs in the postsynaptic membrane. Previous models of AMPA receptor trafficking can explain their fast, brief incorporation in dendritic spines after LTP induction…

Recognizing the causes of ischemic stroke is crucial for defining secondary preventive strategies. Etiology-based classification systems integrate clinical, imaging, and laboratory findings to assign stroke etiology[1-4]. However, this requires significant time and resources, and these systems still fail to assign…

Neural simulations using NEST are typically executed by a Python script that configures the simulator kernel, builds the network, and runs the simulation. The result is a series of files containing the simulated network activity, which can then be analyzed to provide insights into the neural activity. Despite the…

Deep Brain Stimulation (DBS) involves implanting electrodes in the brain to modulate neural activity, treating conditions like Epilepsy [1]. Using synthetic micro-electrode array (MEA) signals as stimuli in a DBS system enables electrical impulses that mimic natural brain activity, improving precision and…

Most mammalian species present topological maps of preference for the ori- entation of edges in their early visual cortex. However, despite the evidence that evolution has favoured topological map organisation in most species, a clear explanation for the functional role of such maps is lacking. Traditional approaches…

Unlike conventional deep neural networks, the human brain has a myriad of direct connections from subcortical nuclei to all cortical areas [1,2]. These enable fast information transfer and facilitate hierarchical compositionality but have not yet been explored in artificial systems [3]. In this work, we present the…

Advancements in machine learning have drastically improved our ability to fit biophysical models of neural systems. We present here a fully-differentiable, biophysical model of the mouse retina that is primed for gradient-based optimization. Our model is based on the Jaxley software library (Deistler et al. 2024,…

Interactions between spiking units grouped in distinct excitatory (E) and inhibitory (I) populations can result in a complex dynamical landscape. Part of this complexity, namely dynamics that depend on undelayed connectivities and external inputs, is retained by a simplified model, the threshold-linear network (TLN),…

Neural activity in the cortex exhibits a wide range of timescales, allowing animals to respond to rapidly changing sensory stimuli while maintaining information in working memory. The timescales of neural activity are not only affected by bottom-up sensory inputs, but also by top-down inputs originating from…

Precise timing of action potentials is crucial for processing sensory information. Axonal length and propagation speed largely determine the time necessary for action potentials to reach postsynaptic neurons. Within the retina, retinal ganglion cell (RGC) axons form the retinal nerve fiber layer (RNFL), a highly…

The hippocampus plays a central role in memory processing. In order to avoid deleterious interference between stored and ongoing experience, theoretical considerations require a separation between phases of memory encoding and recall within the same neural circuit, putatively controlled by hippocampal theta…

Transcranial direct current stimulation (tDCS) has proven to be effective in reducing the seizure frequency in patients with epilepsy. This therapy greatly benefits from designing personalized electrode montages (electrode positions and currents) that target the appropriate areas of the brain. In the state-of-the-art…

Various working memory (WM) models based on a diverse set of biologically plausible synaptic and neural plasticity mechanisms have been proposed. We show that these proposed short-term plasticity mechanisms may not necessarily be competing explanations, but instead yield interesting functional interactions on a wide…