Dr. Rich and Dr. Richard Gast have organized a workshop at CNS*2026 Halifax, entitled “Neuronal heterogeneity’s role in network dynamics and computation.” The workshop takes place on Tuesday, July 14 from 9:00 AM-12:30 PM local time. The agenda and abstracts for this session are below.

Abstracts:

Dr. Scott Rich: Neuronal heterogeneity mechanistically distinguishes physiological and pathological oscillations

Oscillatory population activity arising from neuronal microcircuits, particularly at gamma (30-80 Hz) frequencies, serves a vital role in brain functions including learning, memory, and attention. Paradoxically, oscillatory activity also hallmarks epilepsy via seizure activity. However, there are key nuances distinguishing these physiological and pathological rhythms: seizures are typified by hyper-active neuronal spiking and long-lasting oscillatory activity, while physiological gamma activity consists of sparse spiking and lasts on the order of hundreds of milliseconds. From this perspective, it is notable that many computational explanations for oscillatory dynamics in neuronal networks, including the seminal Pyramidal Interneuron Network Gamma (PING) mechanism, produce dynamics more closely reproducing features of pathological oscillations. Here, we show that networks retaining the key elements of PING—namely, strong reciprocal connectivity between excitatory and inhibitory neurons—can produce more physiologically-realistic gamma activity when neuronal heterogeneity is accounted for rather than idealized away. We argue that such heterogeneities serve to promote oscillations driven by a damped oscillator rather than a stable limit cycle, accounting for key features distinguishing physiological and pathological rhythmicity.

Dr. Jeremie Lefebvre: Structured by Disorder: Heterogeneity, Noise and the Resilience of Brain Dynamics
Neural circuits operate in constantly changing environments yet typically maintain stable and flexible activity over long timescales. Despite widespread variability in both intrinsic neuronal properties and neural activity, the contribution of this biological diversity to circuit resilience remains unclear. Here, we combine large-scale network simulations, modulatory perturbations, and stability analysis to investigate how intrinsic heterogeneity and noise shape the robustness of neural circuits. Using nonlinear random networks, we show that intrinsic diversity enhances resilience by reducing network gain and simplifying the underlying dynamical landscape. Specifically, heterogeneity decreases the number of stable fixed points, limits multistability, and reduces susceptibility to instability. Variability in neural activity, modeled as noise, similarly promotes robustness by disrupting pathological attractors, linearizing network dynamics, and supporting gain control. Together, these results identify spatial and temporal variability as complementary mechanisms that stabilize neural circuits and provide a unifying framework for understanding how diversity, modulation, and connectivity interact to preserve healthy network function.

Sanjna Kumari: Heterogeneities and dorsoventral gradients in intrinsic excitability of hippocampal granule and mossy cells in the rat hippocampus

The distinctions between the dorsal, intermediate, and ventral hippocampus are well characterized in terms of their behavioural roles, anatomical features, circuit connectivity, and gene expression profiles. However, physiological heterogeneity of neurons along the dorsoventral axis of the dentate gyrus (DG), the principal gateway to the hippocampus, remains poorly understood. Here, we used patch-clamp electrophysiology to systematically examine the intrinsic electrophysiological properties of the two excitatory DG neuron types, granule cells (GCs) and mossy cells (MCs), along the dorsoventral axis of the rat hippocampus. Both GCs and MCs exhibited pronounced dorsoventral gradients in intrinsic excitability, with dorsal neurons displaying lower input resistance, impedance amplitude, and firing rates than their ventral counterparts. Within GCs, blade-specific heterogeneity emerged where infrapyramidal blade neurons manifested higher firing rates than suprapyramidal blade neurons only in the ventral DG. GCs exhibited integrator-like dynamics characterized by low-pass impedance profiles, whereas MCs displayed sag, robust delta-frequency (0.5–4 Hz) resonance, and late firing. Pharmacological blockade of HCN channels abolished sag and resonance, while blockade of D-type potassium channels significantly reduced firing latency and enhanced firing rates, identifying key conductances mediating MC-specific intrinsic dynamics. Together, these findings reveal multi-regional intrinsic heterogeneity across DG excitatory circuits and underscore the functional complexity of hippocampus along its dorsoventral axis.

Dr. Mohammadreza Soltanipour: Inferring Cortical Dynamics from Neural Response Diversity

Cortical circuits are often found to exhibit highly diverse neuronal responses, with firing rates and stimulus tuning displaying broad, highly skewed distributions even within apparently homogeneous neuronal populations. While theoretical studies have been able to reproduce such heterogeneity, they often come with a lack of mathematical tractability, preventing a deeper understanding of how circuit parameters shape emergent response diversity. At the same time, emerging data-driven approaches increasingly exploit this heterogeneity to infer effective models of cortical computation, while its use for inferring mechanistic circuit models remains largely unexplored. In this talk, using the Gauss-Rice neuron model, I present a balanced-state cortical circuit model for which the distributions of firing rates and tuning curves can be calculated exactly. It offers self-consistent solutions to recurrent neuronal networks and allows the combination of multiple neuronal populations, arbitrary postsynaptic current dynamics, multiple synaptic receptors (e.g. AMPA and NMDA), and heterogeneous neuronal excitability. By enabling the exact calculation of the likelihood of observed response heterogeneity given circuit parameters, our framework provides a mathematically tractable foundation for mechanistic neural circuit inference.

Marco Zenari: Topological Origins of Timescale Diversity in Recurrent Neural Circuits

Structural and functional heterogeneities are defining features of cortical circuits, ranging from broad connectivity distributions in neural networks to diverse intrinsic timescales across neurons. However, the mechanisms by which heterogeneity in connectivity gives rise to functional diversity remain unclear. A theoretical framework is presented that links network topology to the emergence of heterogeneous neural dynamics. Recurrent networks with heterogeneous connectivity and partially symmetric synaptic interactions are considered and a heterogeneous dynamical mean-field theory is derived to characterize neuronal activity as a function of connectivity. The theory reveals that the interplay between degree heterogeneity and partial symmetry generates effective self-couplings whose strength increases with node degree, producing a broad distribution of neuronal timescales. Highly connected neurons are therefore predicted to exhibit slower dynamics, shaping network stability and supporting multiscale computation. Finally, by instantiating the model with the topology of the MICrONS mouse cortical connectome, it is shown that realistic connectivity structure reproduces experimentally observed relationships between neuronal connectivity and intrinsic timescales. These results provide a mechanistic link between connectome topology, neural dynamics and computation.

Dr. Richard Gast: The Critical Role of Neural Heterogeneity for Population Coding

Recent advances in transcriptomics, projection tracing, and multi-neuron recordings reveal that neural heterogeneity — the continuous diversity of cellular properties — is a pervasive feature of brains. But what is the functional relevance of this heterogeneity? In the traditional view, differences between cell types are considered as the functionally relevant aspect of neural heterogeneity, whereas the contribution of additional heterogeneity within cell types is considered negligible. In this talk, I will argue that the continuously distributed physiological properties existing within cell types play an important functional role that is complementary to the role of discrete cell types. In the first part of the talk, I will discuss results obtained in models of spiking neural populations that reveal how neural heterogeneity controls the capacity of the population to . In the second part, I will present recent, unpublished results on a new mean-field method that allows (a) to apply these concepts to empirical data, and (b) to study the interaction of neural heterogeneity and synaptic plasticity.