Biology subjects

Reyes, A. D.

Publications and source records attributed to Reyes, A. D..

5 recordsLinked to original sources

bioRxiv2025

Interleaving asynchronous and synchronous activity in balanced cortical networks with short-term synaptic depression

Cortical populations are in a broadly asynchronous state that is sporadically interrupted by brief epochs of coordinated population activity. Cortical models are at a loss to explain this combination of states. At one extreme are network models where recurrent inhibition dynamically stabilizes an asynchronous low activity state. While these networks are widely used, they cannot produce the coherent population-wide activity that is reported in a variety of datasets. At the other extreme are models in which strong recurrent excitation that is quickly tamed by short term synaptic depression between excitatory neurons leads to short epochs of population- wide activity. However, in these networks, inhibition plays only a perfunctory role in network stability, which is at odds with many reports across cortex. In this study we analyze spontaneously active in vitro preparations of primary auditory cortex that show dynamics that are emblematic of this mixture of states. To capture this complex population activity we use firing rate-based networks as well as biologically realistic networks of spiking neuron models where large excitation is balanced by recurrent inhibition, yet we include short-term synaptic depression dynamics of the excitatory connections. These models give very rich nonlinear behavior that mimics the core features of the in vitro data, including the possibility of low frequency (2-12 Hz) rhythmic dynamics within population events. In these networks, synaptic depression enables activity fluctuations to induce a weakening of inhibitory recruitment, which in turn triggers population events. In sum, our study extends balanced network models to account for nonlinear, population-wide correlated activity, thereby providing a critical step in a mechanistic theory of realistic cortical activity.

neuroscience↗

bioRxiv2025

Predicting effects of E-I balance on the input-output properties of neurons

In sensory systems, stimuli are represented through the diverse firing responses and receptive fields of neurons. These features emerge from the interaction between excitatory (E) and inhibitory (I) neuron populations within the network. Changes in sensory inputs alter this balance, leading to shifts in firing patterns and the input-output properties of individual neurons and the network. While these phenomena have been studied extensively with experiments and theory, the underlying principles for combining E and I inputs are still unclear. Here, the rules for probabilistically combining E and I inputs are derived that describe how neurons in a feedforward inhibitory circuit respond to stimuli. This simple model is broadly applicable, capturing a wide range of response features that would otherwise require multiple separate models and offers insights into the cellular and network mechanisms influencing the input-output properties of neurons, gain modulation, and the emergence of diverse temporal firing patterns. Author SummarySensory stimuli activate a broad network of excitatory and inhibitory neurons. The response of individual neurons is often complex and influenced by the animals state--such as whether it is resting, moving, or attending to a specific environmental cue. To understand how stimulus features are encoded and modulated, it is essential to examine how synaptic inputs are integrated within individual neurons and across neural networks. In this manuscript, I propose a set of rules for combining excitatory and inhibitory synaptic inputs in neural circuits. This simple, general model captures key features of sensory-driven responses.

neuroscience↗

bioRxiv2021

Topological conditions for propagation of spatially-distributed neural activity

An important task of the nervous system is to transmit information faithfully and reliably across brain regions, a process that involves the coordinated activity of a relatively large population of neurons. In topographically organized networks, where the entering and exiting axons of neurons terminate in confined areas, successful propagation depends on the spatial patterns of activity: the firing neurons in a presynaptic or source layer must be located sufficiently close to each other to ensure that cells in the postsynaptic or target layer receive the requisite number of convergent inputs to fire. Here, we use principles of topology to define the conditions for transmitting information across layers. We show that simplicial complexes formed by source neurons can be used to: 1) determine whether target neurons receive suprathreshold inputs; 2) identify neurons within the active population that contribute to firing; and 3) discriminate between single and multiple active clusters of neurons.

neuroscience↗

bioRxiv2020

Mathematical framework for a place code in the auditory system

In the auditory system, tonotopy is postulated to be the substrate for a place code, where sound frequency is encoded by the location of the neurons that fire during the stimulus. Though conceptually simple, the computations that allow for the representation of intensity and complex sounds are poorly understood. Here, a mathematical framework is developed in order to define clearly the conditions that support a place code. To accommodate both frequency and intensity information, the neural network is described as a topological space with elements that represent individual neurons and clusters of neurons. A bijective mapping is then constructed from acoustic space to neural space so that frequency and intensity are encoded, respectively, by the location and size of the clusters. Algebraic operations -addition and multiplication- are derived to elucidate the rules for representing, assembling, and modulating multi-frequency sound in networks. The predicted outcomes of these operations are consistent with network simulations as well as with electrophysiological and psychophysical data. The analyses show how both frequency and intensity can be encoded with a purely place code, without the need for rate or temporal coding schemes. The algebraic operations are used to describe loudness summation and suggest a mechanism for the critical band. The mathematical approach complements experimental and computational approaches and provides a foundation for interpreting data and constructing models. Author SummaryOne way of encoding sensory information in the brain is with a so-called place code. In the auditory system, tones of increasing frequencies activate sets of neurons at progressively different locations along an axis. The goal of this study is to elucidate the mathematical principles for representing tone frequency and intensity in neural networks. The rigorous, formal process ensures that the conditions for a place code and the associated computations are defined precisely. This mathematical approach offers new insights into experimental data and a framework for constructing network models.

neuroscience↗

bioRxiv2017

Propagation of spike timing and firing rate in feedforward networks reconstituted in vitro

The manner in which information is transferred and transformed across brain regions is yet unclear. Theoretical analyses of idealized feedforward networks suggest that several conditions have to be satisfied in order for activity to propagate faithfully across layers. Verifying these concepts experimentally in networks has not been possible owing to the vast number of variables that must be controlled. Here, we culture cortical neurons in a chamber with sequentially connected compartments, optogenetically stimulate individual neurons in the first layer with high spatiotemporal resolution, and monitor the subthreshold and suprathreshold potentials in subsequent layers. In the first layer, a brief stimulus with different temporal precisions resulted in the modulation of the firing rate. This temporal to rate transformation was propagated to other layers as a sustained response, thereby preserving rate information. This novel mode of propagation occurred in the balanced excitatory-inhibitory regime and is mediated by NMDA-mediated synapses activated by recurrent activity.

neuroscience↗