Biology subjects

Nieters, P.

Publications and source records attributed to Nieters, P..

4 recordsLinked to original sources

bioRxiv2025

When Firing Rate Falls Short: Spike Synchrony to Efficiently Disentangle Stimulus Saliency and Familiarity

Whether neural computation relies on firing rate or spike timing has been debated for decades, with no definitive resolution. Here, using a recurrent spiking network simulation and realistically varying stimulus saliency, we demonstrate that the two mechanisms are complementary: rate reliably detects stimulus class, spike synchrony reliably detects stimulus familiarity. This division of labor is necessary: rate coding fails to distinguish novel high-saliency from familiar low-saliency stimuli, while synchrony succeeds. We validate this complementary coding across a biologically realistic V1 model and an abstract associative memory network, demonstrating robustness across connectivity regimes and memory loads. Crucially, when inputs are already temporally coordinated through familiarity in a previous network layer, spike synchrony alone can encode both stimulus identity and familiarity. Our findings reconcile the rate-timing debate: familiar stimuli exploit efficient synchrony-based codes reinforced by recurrent connectivity across the cortical hierarchy, while novel stimuli depend on rate codes.

neuroscience↗

bioRxiv2025

Continual familiarity decoding from recurrent connections in spiking networks

Familiarity memory enables recognition of previously encountered inputs as familiar without recalling detailed stimuli information, which supports adaptive behavior across various timescales. We present a spiking neural network model with lateral connectivity shaped by unsupervised spike-timing-dependent plasticity (STDP) that encodes familiarity via local plasticity events. We show that familiarity can be decoded from network activity using both frequency (spike count) and temporal (spike synchrony) characteristics of spike trains. Temporal coding demonstrates enhanced performance under sparse input conditions, consistent with the principles of sparse coding observed in the brain. We also show how connectivity structure supports each decoding strategy, revealing different plasticity regimes. Our approach outperforms LSTM in temporal generalizability on the continual familiarity detection task, with input stimuli being naturally encoded in the recurrent connectivity without a separate training stage.

neuroscience↗

bioRxiv2023

Spike synchrony as a measure of Gestalt structure

The function of spike synchrony has long been debated in the neuroscience community. Some researchers view synchrony as a byproduct of brain activity, while others argue that it serves as a mechanism for binding perceptual features. We, however, argue for an alternative view on the computational role of synchrony: it can serve as a mechanism for estimating the prior probability of incoming stimuli. In V1, this probability can be estimated by comparing the input stimulus with previously acquired visual experience. Such experience is encoded in horizontal intracortical connections, as they are subject to change, learn, and adapt to incoming visual stimuli throughout the course of life. Thus, the V1 connectivity structure can encode the acquired visual experience in the form of its aggregate statistics. Since the aggregate statistics of natural images tend to follow the Gestalt principles, we can assume that V1 is more often exposed to Gestalt-like stimuli, and this is manifested in its connectivity structure. At the same time, the connectivity structure has an impact on the synchrony of neuronal firings in V1. In our work, we used a spiking model with V1-like connectivity to demonstrate that spike synchrony reflects the Gestalt structure of the stimulus. We conducted simulation experiments with three Gestalt laws: proximity, similarity, and continuity, and found significant differences in firing synchrony for stimuli with varying degrees of Gestalt-likeness. This allows us to conclude that spike synchrony indeed reflects the Gestalt structure of the stimulus, which can be interpreted as a mechanism for prior probability estimation. Author SummaryNeurons in the brain fire synchronously in different contexts, in response to different stimuli. However, the function of synchrony is very debatable in the neuroscience community. We suggest that synchrony plays an important role in brain computations: it can estimate how well a network is familiar with the incoming stimulus. We tested our hypothesis on the simulation of neuronal activity in a model of primary visual cortex - a brain region which is processing simple visual features, such as lines of different angle orientation. The connections between neurons in this brain area store the acquired visual experience in the form of its statistics, such as edge cooccurrences. We built the connectivity in our model in accordance with principles of connectivity in primary visual cortex, assuming that it reflects natural images statistics. We tested our model on artificial stimuli, which represented more and less experienced visual features. Our results showed that spike synchrony is a global phenomenon which emerges across the large parts of the network, and it reflects the familiarity of the network with the stimulus. Thus, it can serve as a form of recognition memory and has a potential for usage in various applications which require estimation of input familiarity.

neuroscience↗

bioRxiv2019

Event-based pattern detection in active dendrites

AO_SCPLOWBSTRACTC_SCPLOWThe brain constantly processes information encoded in temporal sequences of spiking activity. This sequential activity emerges from sensory inputs as well as from the brains own recurrent connectivity and spans multiple dynamically changing timescales. Decoding the temporal order of spiking activity across these varying timescales is a critical function of the brain, but we do not yet understand its neural implementation. The problem is, that the passive dynamics of neural membrane potentials occur on a short millisecond timescale, whereas many cognitive tasks require the integration of information across much slower behavioral timescales. However, actively generated dendritic plateau potentials do occur on such longer timescales, and their essential role for many aspects of cognition has been firmly established by recent experiments. Here, we build on these discoveries and propose a new model of neural computation that emerges from the interaction of localized plateau potentials across a functionally compartmentalized dendritic tree. We show how this interaction offers a robust solution to the timing invariant detection and processing of sequential spike patterns in single neurons. Stochastic synaptic transmission complements the deterministic all-or-none plateau process and improves information transmission by allowing ensembles of neurons to produce graded responses to continuous combinations of features. We found that networks of such neurons can solve highly complex sequence detection tasks by breaking down long inputs into sequences of shorter, random features that can be classified reliably. These results suggest that active dendritic processes are fundamental to neural computation.

neuroscience↗