Expectation violations produce error signals in mouse V1

Repeated exposure to visual sequences changes the form of evoked activity in the primary visual cortex (V1). Predictive coding theory provides a potential explanation for this, namely that plasticity shapes cortical circuits to encode spatiotemporal predictions and that subsequent responses are modulated by the degree to which actual inputs match these expectations. Here we use a recently developed statistical modeling technique called Model-Based Targeted Dimensionality Reduction (MbTDR) to study visually-evoked dynamics in mouse V1 in context of a previously described experimental paradigm called "sequence learning". We report that evoked spiking activity changed significantly with training, in a manner generally consistent with the predictive coding framework. Neural responses to expected stimuli were suppressed in a late window (100-150ms) after stimulus onset following training, while responses to novel stimuli were not. Omitting predictable stimuli led to increased firing at the expected time of stimulus onset, but only in trained mice. Substituting a novel stimulus for a familiar one led to changes in firing that persisted for at least 300ms. In addition, we show that spiking data can be used to accurately decode time within the sequence. Our findings are consistent with the idea that plasticity in early visual circuits is involved in coding spatiotemporal information.

Spatially distributed computation in cortical circuits

The traditional view of neural computation in the cerebral cortex holds that sensory neurons are specialized, i.e., selective for certain dimensions of sensory stimuli. This view was challenged by evidence of contextual interactions between stimulus dimensions in which a neuron's response to one dimension strongly depends on other dimensions. Here we use methods of mathematical modeling, psychophysics, and electrophysiology to address shortcomings of the traditional view. Using a model of a generic cortical circuit, we begin with the simple demonstration that cortical responses are always distributed among neurons, forming characteristic waveforms, which we call neural waves. When stimulated by patterned stimuli, circuit responses arise by interference of neural waves. Resulting patterns of interference depend on interaction between stimulus dimensions. Comparison of these modeled responses with responses of biological vision makes it clear that the framework of neural wave interference provides a useful alternative to the standard concept of neural computation.

Neuronal Activity Patterns Regulate Brain-Derived Neurotrophic Factor Expression in Cortical Cells via Neuronal Circuits

During development, cortical circuits are remodeled by spontaneous and sensory-evoked activity via alteration of the expression of wiring molecules. An intriguing question is how physiological neuronal activity modifies the expression of these molecules in developing cortical networks. Here, we addressed this issue, focusing on brain-derived neurotrophic factor (BDNF), one of the factors underlying cortical wiring. Real-time imaging of BDNF promoter activity in organotypic slice cultures revealed that patterned stimuli differentially regulated the increase and the time course of the promoter activity in upper layer neurons. Calcium imaging further demonstrated that stimulus-dependent increases in the promoter activity were roughly proportional to the increase in intracellular Ca2+ concentration per unit time. Finally, optogenetic stimulation showed that the promoter activity was increased efficiently by patterned stimulation in defined cortical circuits. These results suggest that physiological stimulation patterns differentially tune activity-dependent gene expression in developing cortical neurons via cortical circuits, synaptic responses, and alteration of intracellular calcium signaling.

Simultaneous two-photon imaging of action potentials and subthreshold inputs in vivo

To better understand the input-output computations of neuronal populations, we developed ArcLight-ST, a genetically-encoded voltage indicator, to specifically measure subthreshold membrane potentials. We combined two-photon imaging of voltage and calcium, and successfully discriminated subthreshold inputs and spikes with cellular resolution in vivo. We demonstrate the utility of the method by mapping epileptic seizures progression through cortical circuits, revealing divergent sub- and suprathreshold dynamics within compartmentalized epileptic micronetworks. Two-photon, two-color imaging of calcium and voltage enables mapping of inputs and outputs in neuronal populations in living animals.

CA1 pyramidal cell diversity is rootedin the time of neurogenesis

Cellular diversity supports the computational capacity and flexibility of cortical circuits. Accordingly, principal neurons at the CA1 output node of the murine hippocampus are increasingly recognized as a heterogeneous population. Their genes, molecular content, intrinsic morphophysiology, connectivity, and function seem to segregate along the main anatomical axes of the hippocampus. Since these axes reflect the temporal order of principal cell neurogenesis, we directly examined the relationship between birthdate and CA1 pyramidal neuron diversity, focusing on the ventral hippocampus. We used a genetic fate-mapping approach that allowed tagging three groups of age-matched principal neurons: pioneer, early- and late-born. Using a combination of neuroanatomy, slice physiology, connectivity tracing and cFos staining in mice, we show that birthdate is a strong predictor of CA1 principal cell diversity. We unravel a subpopulation of pioneer neurons recruited in familiar environments with remarkable positioning, morpho-physiological features, and connectivity. Therefore, despite the expected plasticity of hippocampal circuits, given their role in learning and memory, the diversity of their main components is also partly determined at the earliest steps of development.

Respiration-Driven Brain Oscillations in Emotional Cognition

Respiration paces brain oscillations and the firing of individual neurons, revealing a profound impact of rhythmic breathing on brain activity. Intriguingly, respiration-driven entrainment of neural activity occurs in a variety of cortical areas, including those involved in higher cognitive functions such as associative neocortical regions and the hippocampus. Here we review recent findings of respiration-entrained brain activity with a particular focus on emotional cognition. We summarize studies from different brain areas involved in emotional behavior such as fear, despair, and motivation, and compile findings of respiration-driven activities across species. Furthermore, we discuss the proposed cellular and network mechanisms by which cortical circuits are entrained by respiration. The emerging synthesis from a large body of literature suggests that the impact of respiration on brain function is widespread across the brain and highly relevant for distinct cognitive functions. These intricate links between respiration and cognitive processes call for mechanistic studies of the role of rhythmic breathing as a timing signal for brain activity.

A reservoir of timescales in random neural networks

The temporal activity of many biological systems, including neural circuits, exhibits fluctuations simultaneously varying over a large range of timescales. The mechanisms leading to this temporal heterogeneity are yet unknown. Here we show that random neural networks endowed with a distribution of self-couplings, representing functional neural clusters of different sizes, generate multiple timescales activity spanning several orders of magnitude. When driven by a time-dependent broadband input, slow and fast neural clusters preferentially entrain slow and fast spectral components of the input, respectively, suggesting a potential mechanism for spectral demixing in cortical circuits.

Experience-dependent weakening of callosal synaptic connections in the absence of postsynaptic FMRP

Reduced structural and functional interhemispheric connectivity correlates with the severity of Autism Spectrum Disorder (ASD) behaviors in humans. Little is known of how ASD-risk genes regulate callosal connectivity. Here we show that Fmr1, whose loss-of-function leads to Fragile X Syndrome (FXS), cell autonomously promotes maturation of callosal excitatory synapses between somatosensory barrel cortices in mice. Postnatal, cell-autonomous deletion of Fmr1 in postsynaptic Layer (L) 2/3 or L5 neurons results in a selective weakening of AMPA receptor- (R), but not NMDA receptor-, mediated callosal synaptic function, indicative of immature synapses. Sensory deprivation by contralateral whisker trimming normalizes callosal input strength, suggesting that experience-driven activity of postsynaptic Fmr1 KO L2/3 neurons weakens callosal synapses. In contrast to callosal inputs, synapses originating from local L4 and L2/3 circuits are normal, revealing an input-specific role for postsynaptic Fmr1 in regulation of synaptic connectivity within local and callosal neocortical circuits. These results suggest direct cell autonomous and postnatal roles for FMRP in development of specific cortical circuits and suggest a synaptic basis for long-range functional underconnectivity observed in FXS patients.