Chaos Theory ePosters

Ongoing neural activity unfolds over intrinsic timescales that vary widely across the brain. The increase in timescales along the sensory-transmodal cortical axis parallels the cortical hierarchy, while diversity of timescales within each area may reflect functional specialization of individual neurons. However,…

The synaptic connection strengths in the brain largely follow a heavy-tailed distribution, whereas most theoretical frameworks assume a Gaussian distribution due to the mathematical complexity involved. Understanding how these heavy-tailed distributions influence neural dynamics is crucial for developing more…

Despite wide recognition of the modular and hierarchical organization of neural circuits in the brain, our understanding of its influence on neural dynamics and information processing remains incomplete. To address this gap, we introduced a model of randomly connected neural populations (modules) and studied its…

Models of recurrent neural networks provide fundamental insights about the interplay between structure and dynamics in biological neural networks. Most previous work focused on networks with random or simple connectivity structures, yet experiments show that overrepresented motifs shape the patterns of network…

Networks of interconnected neurons display diverse patterns of collective spiking activity. Relating this collective activity to the network's connectivity structure is a key goal of computational neuroscience. We approach this question for clustered networks, which can form via biologically realistic learning rules…

Advances in simultaneous recordings of large numbers of neurons have driven significant interest in structures of neural population activity such as dimensionality. A key question is how these dynamic features arise mechanistically and their relationship to circuit connectivity. It was previously proposed to use the…

Forming an episodic memory requires binding together disparate elements that co-occur in a single experience. One model of this process is that neurons representing different components of a memory bind to an "index"--- a subset of neurons unique to that memory. Evidence for this model has recently been found in…

Increasing the strength of synaptic connections (synaptic upscaling) within networks of randomly connected neurons transitions the internal dynamics from stationary to a chaotic regime [1, 2]. Synaptic upscaling in neural networks improves learning speed, accuracy, and robustness [3] and enhances the dimensionality of…

Neural replay, a phenomenon where activation sequences experienced during wakefulness are replayed during rest or sleep, is thought to play a crucial role in memory consolidation and planning [1]. It is commonly modeled as a chain of activity, where links are formed based on temporal associations in the stimulus.…

Neural mass models (NMM) seek to unveil the fundamental principles governing macroscopic neural activity, drawing insights from the brain's intricate and multi-scale structure. The advent of an exact mean-field theory for networks of quadratic integrate-and-fire (QIF) neurons represents a significant leap forward in…

Coherent oscillations of neural activity are ubiquitous across brain spatial and temporal scales and have been associated with the formation of sensory or behavioral representations [1]. Refined experimental techniques report today a new complexity of this oscillatory activity, especially in the gamma (30-100 Hz)…

Recent theoretical models and experimental data have revealed that many neurons can exhibit homoclinic (HOM) spike-onset bifurcations by tuning variables like temperature, extracellular ion concentrations, or channel expression levels within the physiologically plausible range. In this HOM regime, spike trains have…

Slow brain rhythms, observed during slow-wave sleep or seizures, often coincide with oscillations in extracellular potassium levels. These rhythms have a lower frequency compared to a typical neuron’s tonic firing. Mechanisms organizing these rhythms are believed to rely on synaptic interactions within the network or…

Different stimuli elicit different transient neural responses in the brain. How is the information represented in the parallel neuronal activity of a neuronal population? We investigate this question in Neuropixel recordings of awake behaving mice by inferring the stimulus class (e.g., visual or tactile) from the…

Information encoding in neural circuits depends on how well time-varying stimuli are encoded by neural populations. Slow neuronal timescales, noise, and network chaos can compromise reliable and rapid population response to external stimuli. A dynamic balance of externally incoming currents by strong recurrent…

The specific topology of biological neural networks is believed to be one of the most important ingredients of the nervous system's computational capabilities. Furthermore, multiple experimental studies have demonstrated that neural connectivity is highly inhomogeneous, following different patterns between different…

The connectivity between neurons plays a major role in the dynamics and function of all neural systems. Here, we investigate the joint effects of two previously reported connectivity motifs, partial symmetry and self-coupling, on the dynamics of recurrent neural networks (RNNs). Both motifs can be thought of as…

Neuronal computation is mediated by spikes, yet it is unclear what the benefits of discrete spiking dynamics over continuous firing rates are. Many theoretical and computational studies treat single neurons as continuous units and their output as an effective firing rate. These works view spiking dynamics as a…

In recurrent circuits, neurons and synapses are coupled in an intricate dance: neurons influence synapses through activity-dependent plasticity, and synapses influence neurons by shaping network dynamics. While previous studies have analyzed recurrent circuits with static synapses, or synapses displaying short-term…

The temporal activity of neural circuits exhibits fluctuations simultaneously varying over a large range of timescales (Murray et al. 2014). Recent experimental evidence presents a heterogeneity of timescales across neurons within the same local cortical circuit, ranging from milliseconds to minutes (Bernacchia et al.…

Cortical circuits exhibit complex activity patterns, both spontaneously and evoked by external stimuli. Information encoding and learning in neural circuits depend on how well time-varying stimuli can control network activity. We show that in firing-rate networks in the balanced state, external control of recurrent…