Spiking neurons

…artificial and natural networks of spiking neurons can learn energy-efficient information processing strategies. Here we propose Predictive Coding Light (PCL), a recurrent hierarchical spiking neural network for unsupervised representation learning. In contrast to previous predictive coding approaches, PCL does not transmit prediction errors to higher

To realize goal-directed behavior an executive control mechanism needs to coordinate when and which pieces of working memory (WM) information are operated on in the specific task context. For that, fundamental WM flexibility is desirable so that a given task learned using a specific set of WM items can be immediately performed on novel items too without any extra training (generalization in the spirit of zero-shot learning). Here we apply the computational paradigm of Spatial computing as a candidate mechanism for such generalization in a Delayed Match-To-Sample Task (DMTS), using an attracto

…sequence replay through simulations of spiking neurons and a time-discrete model of moving membrane potential distributions. First, we simulated spiking networks of excitatory leaky integrate-and-fire cells forming a sequence of assemblies. The sequence contains both recurrent and feedforward connections (within and across assemblies, respect

We consider two spiking neurons, with one neuron driving the other. From a statistical point of view, the relationship between the two neurons can be quantified by (i) the cross-correlation $C(t,t^{\prime})$ of the two spike trains, a measure of similarity of spontaneous fluctuations, and (ii) the linear response $K(t,t^{\prime})$ of the driven neuron's rate to a rate perturbation of the driving neuron. Connecting the two statistics, we analytically derive a simple cross-correlation–response relation (CRR), $C(t,t^{\prime})=K(t,t^{\prime})\lambda(t^{\prime})$, for an arbitrary model of the dr

Neurons in the brain exhibit significant heterogeneity in their dynamics. Whether this diversity plays any computational role and if it is exploited to perform efficient computation is not well understood. We used the reservoir computing paradigm to answer this question. Balanced networks with different levels of heterogeneity in their neuronal time constants were set up and benchmarked against various working-memory-related tasks, including stimulus recall, forecasting, and nonlinear transformation. Our results suggest that neuronal heterogeneity, even without any recurrent communication, si

…digital communication in the form of spiking neurons [1,2]. However, a quantitative and systematic analysis of the advantages and disadvantages of spikes in terms of communication, computation or control is still missing. Here, we approach the question from a control perspective and systematically compare event-based control with analog control. In particular, we view motor neurons as controllers that act on muscle fibers through their twitch response kern

…recurrent neural networks (RNNs) of spiking neurons were trained, and their behavior and firing activity were examined in comparison to those obser

…from biophysically detailed models of spiking neurons containing specific neural mechanisms, such as M-current and adaptation currents (Dalla Porta et al. Plos Comp. Bio., 2023). We incorporated these features in a mean-field model and, using human tractography data, we constructed a virtual whole-brain model consisting of 68 cortical nodes (Goldman et al. Front. Comput. Neurosci., 2023). To enhance realism, we introduced heterogene

…the real-time behavior of individual neurons within a complex circuitry, such as the ones executing neurosensory functions. This study proposes a computational model to study how the mammalian brainstem implements sound localization: the ability to identify an acoustic source in the surrounding space. The main actors in sound localization are two brainstem nuclei: the medial and the lateral superior olive. We have reconstructed a model made of thousands of spiking

It is widely accepted that human cognition is the product of spiking neurons. Yet even for basic cognitive functions, such as the ability to make decisions or prepare and execute a voluntary movement, the gap between spikes and computation is vast. Only for very simple circuits and reflexes can one explain computations neuron-by-neuron and spike-by-spike. This approach becomes infeasible when neurons are numerous the flow of information is recurrent. To understand computation, one thus requires appropriate abstractions. An increasingly common abstraction is the neural ‘factor’. Factors are ce

Neural information processing in dynamic, natural environments requires the brain to flexibly allocate limited computational resources to varying task demands. One example is selective attention in the visual system, which allows to preferentially route signals from behaviourally relevant stimuli to downstream visual areas, while suppressing irrelevant visual information. It is an open question which neural mechanisms realize selective routing, and how this process is controlled. Here we propose that spontaneous synchronization in recurrent networks is the key mechanism for selective processin

…explanation for how dopamine (DA) neurons in the brain can exhibit reward prediction error (RPE), usually through direct analogy to temporal difference (TD) learning. However, there are two key hurdles in imagining how TD could plausibly be implemented in the brain. First, TD models of DA learning frequently require arbitrarily constructed and unrealistic components, such as a temporal chain of feature-specific neurons that uniformly tile the time from stimulus onset to reward arrival. Secondly, various p

…studies of individual neurons to high-level studies of cognitive computation. Still, after twenty years of research, important questions remain open: Experimental evidence for error units, which are central to the theory, is inconclusive, and little theoretical work ex

Neurons in the brain communicate through action potentials (spikes) that are transmitted through chemical synapses. Throughout the last decades, the question how networks of spiking neurons represent and process information has remained an important challenge. Some progress has resulted from a recent family of supervised learning rules (tempotrons) for models of spiking neurons. However, these studies have viewed synaptic transmission as static and characterized synaptic efficacies as scalar quantities that change only on slow time scales of learning across trials but remain fixed on the fast

…However, spiking neurons make biological vision fundamentally different from the firing rate model implicit in these artificial neural networks. In region V1 of the primate visual cortex, a flashed image is processed in tens of milliseconds, allowing time for only a few action potentials, typically across a small subset of neurons. Moreover, persistent eye motion (ocular drift) changes the location of retinal responses, and thus

Neurons in the brain communicate through action potentials (spikes) that are transmitted through chemical synapses. Throughout the last decades, the question how networks of spiking neurons represent and process information has remained an important challenge. Some progress has resulted from a recent family of supervised learning rules (tempotrons) for models of spiking neurons. However, these studies have viewed synaptic transmission as static and characterized synaptic efficacies as scalar quantities that change only on slow time scales of learning across trials but remain fixed on the fast

Recent chronic imaging experiments in mice have revealed that the hippocampal code exhibits non-trivial turnover dynamics over long time scales. Specifically, the subset of cells which are active on any given session in a familiar environment changes over the course of days and weeks. While some cells transition into or out of the code after a few sessions, others are stable over the entire experiment. The mechanisms underlying this turnover are unknown. Here we show that the statistics of turnover are consistent with a model in which non-spatial inputs to CA1 pyramidal cells readily undergo p

…the response of biological single neurons is better described by all-or-none events - spikes - that are triggered in response to the processing of their synaptic input by the complex dynamics of their membrane. One line of research has attempted to resolv

Recent advances in optogenetics allow for stimulation of neurons with sub-millisecond spike jitter and single neuron selectivity. Already this precision has revealed new levels of cortical sensitivity: stimulating tens of neurons can yield changes in the mean firing rate of thousands of similarly tuned neurons. This extreme sensitivity suggests that cortical dynamics are near criticality. Criticality is often studied in neural systems as a non-equilibrium thermodynamic process in which scale-free patterns of activity, called avalanches, emerge between distinct states of spontaneous activity. W

To prevail in a dynamic and noisy environment, the brain must create reliable and meaningful representations from sensory inputs that are often ambiguous or corrupt. Since only information that permeates the cortical hierarchy can influence sensory perception and decision-making, it is critical that noisy external stimuli are encoded and propagated through different processing stages with minimal signal degradation. Here we hypothesize that stimulus-specific pathways akin to cortical topographic maps may provide the structural scaffold for such signal routing. We investigate whether the featur