UP-DOWN cortical dynamics reflect state transitions in a bistable network.

In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. Here we analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate. Fluctuations triggered state transitions, while adaptation in E cells paradoxically caused a marginal decay of E-rate but a marked decay of I-rate in UP periods, a prediction that we validated experimentally. A spiking network implementation further predicted that DOWN-to-UP transitions must be caused by synchronous high-amplitude events. Our findings provide evidence of bistable cortical networks that exhibit non-rhythmic state transitions when the brain rests

Heading direction with respect to a reference point modulates place-cell activity.

The tuning of neurons in area CA1 of the hippocampus emerges through a combination of non-spatial input from different sensory modalities and spatial information about the animal's position and heading direction relative to the spatial enclosure being navigated. The positional modulation of CA1 neuronal responses has been widely studied (e.g. place tuning), but less is known about the modulation of these neurons by heading direction. Here, utilizing electrophysiological recordings from CA1 pyramidal cells in freely moving mice, we report that a majority of neural responses are modulated by the heading-direction of the animal relative to a point within or outside their enclosure that we call a reference point. The finding of heading-direction modulation relative to reference points identifies a novel representation encoded in the neuronal responses of the dorsal hippocampus

High-Throughput Task to Study Memory Recall During Spatial Navigation in Rodents

© Copyright © 2020 Morales, Tomàs, Dalmau, de la Rocha and Jercog. Spatial navigation is one of the most frequently used behavioral paradigms to study memory formation in rodents. Commonly used tasks to study memory are labor-intensive, preventing the simultaneous testing of multiple animals with the tendency to yield a low number of trials, curtailing the statistical power. Moreover, they are not tailored to be combined with neurophysiology recordings because they are not based on overt stereotyped behavioral responses that can be precisely timed. Here we present a novel task to study long-term memory formation and recall during spatial navigation. The task consists of learning sessions during which mice need to find the rewarding port that changes from day to day. Hours after learning, there is a recall session during which mice search for the location of the memorized rewarding port. During the recall sessions, the animals repeatedly poke the remembered port over many trials (up to ∼20) without receiving a reward (i.e., no positive feedback) as a readout of memory. In this task, mice show memory of port locations learned on up to three previous days. This eight-port maze task requires minimal human intervention, allowing for simultaneous and unsupervised testing of several mice in parallel, yielding a high number of recall trials per session over many days, and compatible with recordings of neural activity

UP-DOWN cortical dynamics reflect state transitions in a bistable network

In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. Here we analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate. Fluctuations triggered state transitions, while adaptation in E cells paradoxically caused a marginal decay of E-rate but a marked decay of I-rate in UP periods, a prediction that we validated experimentally. A spiking network implementation further predicted that DOWN-to-UP transitions must be caused by synchronous high-amplitude events. Our findings provide evidence of bistable cortical networks that exhibit non-rhythmic state transitions when the brain rests

Asymmetric Excitatory Synaptic Dynamics Underlie Interaural Time Difference Processing in the Auditory System

In order to localize sounds in the environment, the auditory system detects and encodes differences in signals between each ear. The exquisite sensitivity of auditory brain stem neurons to the differences in rise time of the excitation signals from the two ears allows for neuronal encoding of microsecond interaural time differences

Frequency-Invariant Representation of Interaural Time Differences in Mammals

Interaural time differences (ITDs) are the major cue for localizing low-frequency sounds. The activity of neuronal populations in the brainstem encodes ITDs with an exquisite temporal acuity of about . The response of single neurons, however, also changes with other stimulus properties like the spectral composition of sound. The influence of stimulus frequency is very different across neurons and thus it is unclear how ITDs are encoded independently of stimulus frequency by populations of neurons. Here we fitted a statistical model to single-cell rate responses of the dorsal nucleus of the lateral lemniscus. The model was used to evaluate the impact of single-cell response characteristics on the frequency-invariant mutual information between rate response and ITD. We found a rough correspondence between the measured cell characteristics and those predicted by computing mutual information. Furthermore, we studied two readout mechanisms, a linear classifier and a two-channel rate difference decoder. The latter turned out to be better suited to decode the population patterns obtained from the fitted model

Author response

Spontaneous slow oscillation-associated slow wave activity represents an internally generated state which is characterized by alternations of network quiescence and stereotypical episodes of neuronal activity - slow wave events. However, it remains unclear which macroscopic signal is related to these active periods of the slow wave rhythm. We used optic fiber-based calcium recordings of local neural populations in cortex and thalamus to detect neurophysiologically defined slow calcium waves in isoflurane anesthetized rats. The individual slow wave events were used for an event-related analysis of simultaneously acquired whole-brain BOLD fMRI. We identified BOLD responses directly related to onsets of slow calcium waves, revealing a cortex-wide BOLD correlate: the entire cortex was engaged in this specific type of slow wave activity. These findings demonstrate a direct relation of defined neurophysiological events to a specific BOLD activity pattern and were confirmed for ongoing slow wave activity by independent component and seed-based analyses

Dynamics of spontaneous activity in the cerebral cortex across brain states

Spontaneous activity in the cerebral cortex changes in different brain states. During desynchronized brain states (e.g. wakefulness, REM sleep), populations of neurons in the cerebral cortex fire action potentials in a stochastic and uncorrelated manner. In contrast, during synchronized states (e.g. slowwave sleep, anesthesia) cortical neurons display the alternation between quiescent periods (DOWN) and periods of spiking activity (UP) coherently across cortical layers. In recent years, the view has emerged that brain states are not defined in discrete categories, but rather form a continuum of possible states. In this thesis, we address three main questions regarding this phenomenon: How is the oscillatory activity at high frequencies (10-100 Hz) distributed across the cortical laminae during UP states? What are the mechanisms underlying UP and DOWN dynamics in neocortex in vivo? How do brain states shape the statistics of cortical spontaneous activity? First, we analyzed laminar local field potential recordings of spontaneous activity in the visual cortex in vivo and characterized the laminar profile of fast oscillatory activity present during UP states, which showed overall similar spectral properties across layers but were generated independently in two different compartments determined by superficial and deep cortical layers. In order to explore whether this laminar profile of fast oscillatory activity was generated intrinsically by cortical circuitry or by external inputs, we performed simultaneous local field potential recordings in spontaneously active cortical slices in vitro. By manipulating the slices pharmacologically, we concluded that neural excitability can control inter-laminar couplings and oscillatory dynamics in cortical circuits. Second, we made a quantitative analysis of UP and DOWN dynamics in vivo by analyzing multiple single-unit cortical recordings during synchronized states. The classic view about what causes cortical UP and DOWN dynamics during synchronized states implicates a "fatigue" or adaptation process (such as spike frequency adaptation or synaptic depression), but our results were inconsistent with a dominant role for this mechanism. Using a low dimensional modeling approach, we proposed a rate model that displays UP and DOWN dynamics, in which bistability can be obtained at arbitrarily low rates. With this model we explored the role and interplay of adaptation and external fluctuations in shaping the statistics of UP and DOWN state dynamics, and determined that a regime of weak adaptation and strong fluctuations is necessary to qualitatively reproduce the statistical features of the experimental data. Finally, we studied the impact of brain states in cortical dynamics. Under urethane anesthesia, spontaneous transitions between synchronized states (with UP and DOWN state dynamics) and desynchronized states (with sporadic or absent DOWN states) are observable, and these transitions resemble those observed from slow-wave to REM sleep states. Investigating multiple single-unit recordings during these transitions, we found that the statistics of UP and DOWN states are largely determined by the brain state in a continuous manner, consistently across experiments and cortical areas. This constrains the possible cortical mechanisms modulated during transitions between desynchronized and synchronized states, as revealed in a low-dimensional firing rate network model.La actividad espontánea en la corteza cerebral cambia en diferentes estados cerebrales. Durante estados desincronizados (e.g. estado de vigilia, sueño MOR), las poblaciones de neuronas en los potenciales de acción en una manera aparentemente estocástica y no correlacionada. Por el contrario, durante estados sincronizados (e.g. sueño de ondas lentas, anestesia) las neuronas corticales muestran la alternancia entre periodos de reposo (DOWN) y los períodos de actividad (UP) de manera coherente a través de las capas corticales. En los últimos años, ha emergido la visión de que los estados cerebrales no están definidos en categorías discretas, sino que forman un continuo de estados posibles. En esta tesis, nos dirigimos a tres preguntas principales con respecto a este fenómeno: ¿Cómo es la actividad oscilatoria a altas frecuencias (10-100 Hz) distribuída a través de las capas de la neocorteza durante los períodos UP? ¿Cuáles son los mecanismos que subyacen a la dinámica UP y DOWN en el neocórtex in vivo? ¿Cómo se determinan los estados cerebrales estadísticas de actividad espontánea cortical? En primer lugar, analizamos registros de potencial de campo local en la corteza visual in vivo y caracterizamos el perfil laminar de actividad oscilatoria rápida durante los estados UP, que mostraron en general propiedades espectrales similares a través de las distintas capas, pero generadas de forma independiente en dos compartimentos distintos determinados por capas corticales superficiales y profundas. Con el fin de explorar si este perfil laminar de la actividad oscilatoria rápida es generada intrínsecamente por los circuitos corticales o por inputs externos, se realizaron registros de potencial de campo local en rebanadas corticales espontáneamente activas in vitro. Mediante la manipulación farmacológica in vitro, se concluyó que la excitabilidad neuronal puede controlar los acoplamientos entre capas y dinámica oscilatoria en los circuitos corticales. En segundo lugar, realizamos un análisis cuantitativo de la dinámica UP y DOWN in vivo mediante el análisis de registros corticales múltiples de una sola unidad durante estados sincronizados. El punto de vista clásico sobre las causas de esta dinámica durante los estados sincronizados implica un mecanismo de "fatiga" o proceso de adaptación (e.g. adaptación de frecuencia disparo o depresión sináptica), pero los resultados que observamos resultan inconsistentes con un papel dominante de este mecanismo. Utilizando un enfoque de modelado de baja dimensión, propusimos un modelo que muestra dinámica UP y DOWN, en la que biestabilidad se puede conseguir a tasas de descarga arbitrariamente bajas. Con este modelo hemos explorado el papel y la interacción de la adaptación y las fluctuaciones externas en la conformación de las estadísticas de la dinámica del estado UP y DOWN, y determinamos un régimen de adaptación débil pero con fluctuaciones fuertes es necesario para reproducir cualitativamente la estadística de los datos experimentales. Por último, transiciones espontáneas entre estados sincronizados (con la dinámica del estado UP y DOWN) y estados desincronizados (con los períodos DOWN esporádicos o ausentes) son observables bajo la influencia del anestésico uretano, y estas transiciones se asemejan a las observadas a partir de ondas lentas de los estados de sueño REM. Investigando registros múltiples de una sola unidad durante estas transiciones, encontramos que la estadísticas de la dinámica UP y DOWN está determinada en gran medida por el estado del cerebro de forma continua, de manera consistente a través de experimentos y áreas corticales. Esto limita los posibles mecanismos corticales modulados durante las transiciones entre estados desincronizados y sincronizados, tal como lo revela el uso de un modelo de baja dimensión