Topic: spiking neurons

Current machine learning systems consume vastly more energy than biological brains. Neuromorphic systems aim to overcome this difference by mimicking the brain’s information coding via discrete voltage spikes. However, it remains unclear how both 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

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

Understanding the neural code for vision remains a major challenge for neuroscience, with recent efforts influenced by the success of convolutional neural networks. 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

Recently, the field of computational neuroscience has seen an explosion of the use of trained recurrent network models (RNNs) to model patterns of neural activity. These RNN models are typically characterized by tuned recurrent interactions between rate 'units' whose dynamics are governed by smooth, continuous differential equations. However, 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

The hippocampal formation plays a key role in learning and memory, with CA3 thought to be the substrate of pattern completion [1]. However, its underlying computational and synaptic mechanisms are not yet understood. Here we analyze the mechanisms behind 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

Energy efficiency and robustness to noise for long-distance communication are often cited as reasons to the question of why the brain uses 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

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 in the perception of the first stimulus towards its distribution’s mean (highlighted in red in Fig. 1a). To shed light on this effect, recurrent neural networks (RNNs) of spiking neurons were trained, and their behavior and firing activity were examined in comparison to those obser