Cortical circuits

Takaki Komiyama will discuss how cortical circuits maintain information about the value of behavioral options across trials and transform past experience into future choices. The talk draws on longitudinal imaging and circuit manipulation in mice to examine distinct neuronal populations and local and long-range interactions in history-dependent decision-making.

Dean Buonomano discusses timing as a computational property of cortical circuits, including experiments training organotypic slices to predict temporal stimuli and replay sequences. The conversation also addresses the relationship between AI and neuroscience, agreements and tensions between physics and neuroscience about time, and his objections to integrated information theory as an account of consciousness.

Cortical circuits are built from diverse collections of neurons. Recent large-scale surveys have characterized this striking variability in terms of molecular, morphological, and electrophysiological features, identifying hundreds to thousands of distinct neuronal cell types. However, in computational neuroscience, we often stick our heads in the sand and ignore these details, instead basing theory on homogeneous populations of simple ‘point neurons'. The problem with this approach is that it's currently unknown what is lost with such simplifying assumptions. The simplification may be entirely

Understanding how cortical circuits encode sensory information remains a key challenge. Given a stimulus, the central question from the prespecitive of probablistic theories is how posterior probablities are neurally encoded. In recent years, standard approaches like sampling-based and variational approximations have been extended by deep neural networks (DNNs). For variational approaches, DNNs are frequently used to define encoders in variational autoencoders (VAE) settings. It remains unclear, however, what aspects of posterior approximations are important to explain neural encoding . Unlik

Recent years have witnessed a growing body of experimental evidence highlighting the active role of glial cells in modulating neuronal dynamics. Glial cells, including astrocytes and microglia, have been found to influence various aspects of neuronal function, such as regulating neurotransmitter concentrations and ion buffering. Yet, the mechanisms through which glial cells communicate with and affect neighboring neurons remain unclear. In this study, we seek to develop a computational model to explore recent experimental findings which demonstrated that glial cells physically wrap around, o

…In cortical circuits, much work has emphasized how attractor dynamics generate reliable behavior through the convergence of a network to specialized activity patterns for specific decisions and actions1. Other work has emphasized how cortical activity exhibits variability across trials of a behavior, which could in principle support flexibility in actions2. Here, through multi-area calcium imaging and recurrent neural network models, we propose that cortical activity f

Cortical circuits are widely thought to operate in the so-called ``inhibition-stabilized network (ISN) regime’‘, whereby the excitatory (E) subcircuit is unstable in isolation but dynamically stabilized by feedback inhibition (I). So far, this regime has been studied both theoretically and experimentally in its one-dimensional, ``mean-field’‘ form, whereby the overall E population activity constitutes the main mode of instability, and stability is maintained by uniform, blanket inhibition. Here, we investigate the hypothesis that cortical circuits may in fact be operating in a richer, multi-d

…this framework to communication across cortical circuits and distinguishes theoretical explanations from computational modelling, building on questions raised by active predictive coding.

To successfully inform behavior, cortical circuits must produce consistent patterns of neuronal activity within and across trials that allow for reliable stimulus discrimination. Under this perspective, any response variability appears detrimental to sensory systems as it severely limits stimulus discriminability. Here, we propose a beneficial role of response variability: to facilitate faster learning in feedforward-recurrent networks through a self-stabilizing feedback mechanism. We explore this hypothesis in the framework of learning novel stimulus representations in pre-structured recurren

…myelination may stabilize neuronal circuits and temper neuronal plasticity as animals age. We tested this theory in the visual cortex, which has a well-defined critical period for experience-dependent neuronal plasticity. During adolescence, visual experience modulated oligodendrocyte maturation in visual cortex. To determine whether oligodendrocyte maturation in turn regulates neuronal plasticity, we genetically blocked oligodendrocyte differentiation and

Dravet syndrome (DS) is a rare form of epilepsy caused by loss-of-function mutations of the voltage-gated sodium channel NaV1.1. The proposed disease mechanism suggests that these mutations compromise the ability of inhibitory interneurons to regulate network activity, leading to seizure events. However, standard treatments aimed at restoring inhibition have very limited efficacy, suggesting a potential role for excitatory neurons in DS pathogenesis. To explore this, we generated excitatory and inhibitory neurons from control and Dravet hiPSCs and combined them at physiological ratios into i

Understanding the dynamic interplay between neuromodulatory systems and neuronal circuit dynamics is crucial for deciphering the underpinnings of complex behaviors. Noradrenergic signaling is widespread throughout the body and the brain and plays a pivotal role in the implementation of physiological processes and brain network configuration across arousal states. Yet, unraveling the causal relationship between norepinephrine release and concomitant neuronal dynamics poses a formidable challenge due to the intrinsic coupling of noradrenergic activity with behavioral changes.To resolve this conu

How do cortical circuits acquire new dynamics that drive learned movements? This webinar will focus on mouse premotor cortex in relation to learned lick-timing and explore high-density electrophysiology using our silicon neural probes alongside region and cell-type-specific acute genetic manipulations of proteins required for synaptic plasticity.

Cortical circuits embody remarkably reliable neural representations of sensory stimuli that are critical for perception and action. The fundamental structure of these network representations is thought to arise early in development prior to the onset of sensory experience. However, how these endogenously generated networks respond to the onset of sensory experience, and the extent to which they reorganize with experience remains unclear. Here we examine this ‘nature-nurture transform’ using chronic in vivo calcium imaging to probe the developmental emergence of the representation of orientatio

Network models are often designed to capture selective aspects of cortical circuits. On one end, mechanistic models such as balanced spiking networks resemble activity regimes observed in data, but are often limited to simple computations. On the other end, functional models like trained deep networks can show comparable performance and dynamical motifs, but are far removed from experimental physiology. Here, we put forth a new framework for excitatory-inhibitory spiking networks which retains key properties of both mechanistic and functional models. Based on previous studies of the geometry o

The cholinergic system has been associated with learning, but also with cognitive decline in dementia, aging and injury. Yet, to date, no computational models have been put forward to explain how the cholinergic system contributes to both learning and cognitive decline. Here we introduce a model that combines a recently proposed model of cortex-wide credit assignment with a cholinergic adaptive module based on adaptive deep learning rules. According to this model, the cholinergic system opens a cortex-wide gate for learning, but its end effect is controlled by local adaptive processes. Using a

A signature feature of cortical circuits is the irregularity of neuronal firing, which manifests itself in the high temporal variability of spiking and the broad distribution of rates. Theoretical works have shown that this feature emerges dynamically in network models if coupling between cells is strong, i.e. if the mean number of synapses per neuron K is large and synaptic efficacy is of order 1/\sqrt{K}. However, the degree to which these models capture the mechanisms underlying neuronal firing in cortical circuits is not fully understood. Results have been derived using neuron models with

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 dynamics, i.e., the suppression of the internally-generated chaotic variability, strongly depends on correlations in the input: One might expect that driving all neurons with a common input helps to control network dynamics. Surprisingly, we find that the network is far easier to co

…about how it may be implemented in cortical circuits. We set out to develop and characterise a biological model system with layer 5 pyramidal cells in the centre. We aim to gain access to prediction and internal model generating processes by controlling, understanding or monitoring everything else: the sensory environment, feed-forward and feed-back inputs, integrative properties, their spiking activity and output. I’ll show recent work from the lab establishing such a model system both in term

Topographic maps are a prominent feature of brain organization, reflecting local and large-scale representation of the sensory surface. ​​Traditionally, such representations in early visual areas are conceived as retinotopic maps preserving ego-centric retinal spatial location while ensuring that other features of visual input are uniformly represented for every location in space. I will discuss our recent findings of a striking departure from this simple mapping in the secondary visual area (V2) of the tree shrew that is best described as a sinusoidal transformation of the visual field. This

Cortical circuits process information by rich recurrent interactions between excitatory neurons and inhibitory interneurons. One of the prime functions of interneurons is to stabilize the circuit by feedback inhibition, but the level of specificity on which inhibitory feedback operates is not fully resolved. We hypothesized that inhibitory circuits could enable separate feedback control loops for different synaptic input streams, by means of specific feedback inhibition to different neuronal compartments. To investigate this hypothesis, we adopted an optimization approach. Leveraging recent ad

…A pervasive yet puzzling feature of cortical circuits is that despite their complex wiring, population-wide shared spiking variability is low dimensional with all neurons fluctuating en masse. We show that the spatiotemporal dynamics in a spatially structured network produce large population-wide shared variability. When the spatial and temporal scales of inhibitory coupling match known physiology, model spiking

Spontaneous and sensory-evoked cortical activity is highly state-dependent, promoting the functional flexibility of cortical circuits underlying perception and cognition. Using neural recordings in combination with behavioral state monitoring, we find that arousal and motor activity have complementary roles in regulating local cortical operations, providing dynamic control of sensory encoding. These changes in encoding are linked to altered performance on perceptual tasks. Neuromodulators, such as acetylcholine, may regulate this state-dependent flexibility of cortical network function. We

…and neural populations organized into circuits. We have developed methods to measure, manipulate, and analyze neural circuits across various spatial and temporal scales, including technology for virtual reality, optical imaging, optogenetics, intracellular electrophysiology, molecular sensors, and computational modeling. I will present recent wor