Thibaud Taillefumier - US grants

Project Summary Intracellular recording from sensory cortex provides a window into the synaptic inputs that shape spiking responses of individual cortical neurons, but until recently, this powerful technique has been limited to anesthetized animals. By combining the unique expertise from our laboratories, we have developed a novel technique that allow us to conduct, on a routine basis, reliable, whole-cell intracellular recording in primary visual cortex (V1) of awake, behaving macaque monkeys. We combine intracellular recording with an array of concomitant measurements that provide access to the state of the local network in which the neuron is embedded as well as to the internal state of the animal. Using these techniques, we have access to both subthreshold (membrane potentials, representing input) and suprathreshold (spikes, representing output) responses of individual cortical neurons, while also utilizing the precise control of visual stimulation and the subject?s behavioral state afforded by behaving primates. Our ability to perform intracellular recording in awake, behaving primates opens the door to addressing three fundamental questions with respect to the circuit-level mechanisms that mediate visual perception: (1) what are the nature, sources, and behavioral consequences of the large neural variability of sensory cortical neurons, (2) what is the contribution of internal state fluctuations to this variability, (3) what circuit models can account for the observed neural variability during spontaneous and evoked responses? To address these questions, in Aim 1 we will study the quantitative relationship between sub- and suprathreshold activity during spontaneous and stimulus-evoked responses in V1 of fixating monkeys. This will allow us to test the generality of previous findings from anesthetized animals. In Aim 2, we will examine the relationship between the activity of single V1 neurons and perceptual decisions in monkeys that are engaged in a demanding visual detection task. Specifically, we will examine how sub- and suprathreshold responses are altered by changing the attentional and motivational states under which the stimulus is presented. Finally, in Aim 3 we will test a novel set of circuit models that can account for our observed results and guide future experiments.

Artificial intelligence can now rival human performance in tasks such as speech or object recognition, language translation, and autonomous navigation. However, by contrast with artificial computations supported by fragile, hardwired circuits, biological computations appear to robustly emerge in noisy, disordered neural networks. Understanding how meaningful computations emerge from the seemingly random interactions of neural constituents remains a challenge. To solve this, one can hope to mine biological networks for their design principles. Unfortunately, such a task is hindered by the sheer complexity of neural circuits. Deciphering neural computations will only be achieved through the simplifying lens of a biophysically relevant theory. To date, neural computations have been studied theoretically in idealized models whereby an infinite number of neurons communicate via vanishingly small interactions. Such an approach neglects that neural computations are carried out by a finite number of cells interacting via a finite number of synapses. This approach precludes understanding how neural circuits reliably process information in spite of neural variability, which depends on these finite numbers. To remedy this point, the PIs will develop a novel theoretical framework allowing for the analysis of neural computations in neural circuits with finite-size structure. The PIs will leverage ideas from the theory of communication networks to understand how biophysically relevant neural network models can reliably process information via noisy, disordered circuits. This approach will provide the basis for categorizing distinct brain operating regimen based on their stability and will help designing strategies to stabilize neural systems in their healthy regime.

In contrast to “divide and conquer” approaches, which equate a system with the mere sum of its parts, the PIs will decipher the activity of neural networks via a “multiply and conquer” approach. This approach considers limit networks made of infinitely many replicas with the same basic neural structure. The key point is that these so-called replica-mean-field networks are in fact simplified, tractable versions of neural networks that retain important features of the finite network structure of interest. The finite size of neuronal populations and synaptic interactions is a core determinant of neural activity, being responsible for non-zero correlation in the spiking activity and for finite transition rates between metastable neural states. Accounting for these finite-size phenomena is the core motivation for the development of the replica approach. The expected outcome is a mechanistic understanding of the constraints bearing on computations in finite-size neural circuits, especially in terms of their reliability, speed, and cost. This will involve characterizing the finite-structure dependence of: (i) the regime of spiking correlations, which is a fundamental determinant of the neural code and (ii) the transition rates between metastable neural states, which are thought to control the processing and gating of information. In both cases, the methodology will be based on the comparison between solutions to reduced functional equations, discrete-event simulations, and neural data sets. The ultimate goal will be to analyze biophysically detailed models in order to produce a fitting framework that is restrictive enough to formulate and validate experimental predictions.

This award is being co-funded by the MPS Division of Mathematical Sciences and the CISE Information and Intelligent Systems (IIS) through the CRCNA and BRAIN Programs.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.