MPI für Hirnforschung
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Entorhinal-retrosplenial circuits for allocentric-egocentric transformation of boundary coding
(2020)
Spatial navigation requires landmark coding from two perspectives, relying on viewpoint-invariant and self-referenced representations. The brain encodes information within each reference frame but their interactions and functional dependency remains unclear. Here we investigate the relationship between neurons in the rat's retrosplenial cortex (RSC) and entorhinal cortex (MEC) that increase firing near boundaries of space. Border cells in RSC specifically encode walls, but not objects, and are sensitive to the animal’s direction to nearby borders. These egocentric representations are generated independent of visual or whisker sensation but are affected by inputs from MEC that contains allocentric spatial cells. Pharmaco- and optogenetic inhibition of MEC led to a disruption of border coding in RSC, but not vice versa, indicating allocentric-to-egocentric transformation. Finally, RSC border cells fire prospective to the animal’s next motion, unlike those in MEC, revealing the MEC-RSC pathway as an extended border coding circuit that implements coordinate transformation to guide navigation behavior.
Background: Cognitive dysfunctions represent a core feature of schizophrenia and a predictor for clinical outcomes. One possible mechanism for cognitive impairments could involve an impairment in the experience-dependent modifications of cortical networks.
Methods: To address this issue, we employed magnetoencephalography (MEG) during a visual priming paradigm in a sample of chronic patients with schizophrenia (n = 14), and in a group of healthy controls (n = 14). We obtained MEG-recordings during the presentation of visual stimuli that were presented three times either consecutively or with intervening stimuli. MEG-data were analyzed for event-related fields as well as spectral power in the 1–200 Hz range to examine repetition suppression and repetition enhancement. We defined regions of interest in occipital and thalamic regions and obtained virtual-channel data.
Results: Behavioral priming did not differ between groups. However, patients with schizophrenia showed prominently reduced oscillatory response to novel stimuli in the gamma-frequency band as well as significantly reduced repetition suppression of gamma-band activity and reduced repetition enhancement of beta-band power in occipital cortex to both consecutive repetitions as well as repetitions with intervening stimuli. Moreover, schizophrenia patients were characterized by a significant deficit in suppression of the C1m component in occipital cortex and thalamus as well as of the late positive component (LPC) in occipital cortex.
Conclusions: These data provide novel evidence for impaired repetition suppression in cortical and subcortical circuits in schizophrenia. Although behavioral priming was preserved, patients with schizophrenia showed deficits in repetition suppression as well as repetition enhancement in thalamic and occipital regions, suggesting that experience-dependent modification of neural circuits is impaired in the disorder.
How is semantic information stored in the human mind and brain? Some philosophers and cognitive scientists argue for vectorial representations of concepts, where the meaning of a word is represented as its position in a high-dimensional neural state space. At the intersection of natural language processing and artificial intelligence, a class of very successful distributional word vector models has developed that can account for classic EEG findings of language, that is, the ease versus difficulty of integrating a word with its sentence context. However, models of semantics have to account not only for context-based word processing, but should also describe how word meaning is represented. Here, we investigate whether distributional vector representations of word meaning can model brain activity induced by words presented without context. Using EEG activity (event-related brain potentials) collected while participants in two experiments (English and German) read isolated words, we encoded and decoded word vectors taken from the family of prediction-based Word2vec algorithms. We found that, first, the position of a word in vector space allows the prediction of the pattern of corresponding neural activity over time, in particular during a time window of 300 to 500 ms after word onset. Second, distributional models perform better than a human-created taxonomic baseline model (WordNet), and this holds for several distinct vector-based models. Third, multiple latent semantic dimensions of word meaning can be decoded from brain activity. Combined, these results suggest that empiricist, prediction-based vectorial representations of meaning are a viable candidate for the representational architecture of human semantic knowledge.
When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here we show in awake macaque area V1 that both, repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects showed some persistence on the timescale of minutes. Further, gamma increases were specific to the presented stimulus location. Importantly, repetition effects on gamma and on firing rates generalized to natural images. These findings suggest that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters, both for generating efficient stimulus responses and possibly for memory formation.
The firing pattern of ventral midbrain dopamine neurons is controlled by afferent and intrinsic activity to generate prediction error signals that are essential for reward-based learning. Given the absence of intracellular in vivo recordings in the last three decades, the subthreshold membrane potential events that cause changes in dopamine neuron firing patterns remain unknown. By establishing stable in vivo whole-cell recordings of >100 spontaneously active midbrain dopamine neurons in anaesthetized mice, we identified the repertoire of subthreshold membrane potential signatures associated with distinct in vivo firing patterns. We demonstrate that dopamine neuron in vivo activity deviates from a single spike pacemaker pattern by eliciting transient increases in firing rate generated by at least two diametrically opposing biophysical mechanisms: a transient depolarization resulting in high frequency plateau bursts associated with a reactive, depolarizing shift in action potential threshold; and a prolonged hyperpolarization preceding slower rebound bursts characterized by a predictive, hyperpolarizing shift in action potential threshold. Our findings therefore illustrate a framework for the biophysical implementation of prediction error and sensory cue coding in dopamine neurons by tuning action potential threshold dynamics.
Glia, the helper cells of the brain, are essential in maintaining neural resilience across time and varying challenges: By reacting to changes in neuronal health glia carefully balance repair or disposal of injured neurons. Malfunction of these interactions is implicated in many neurodegenerative diseases. We present a reductionist model that mimics repair-or-dispose decisions to generate a hypothesis for the cause of disease onset. The model assumes four tissue states: healthy and challenged tissue, primed tissue at risk of acute damage propagation, and chronic neurodegeneration. We discuss analogies to progression stages observed in the most common neurodegenerative conditions and to experimental observations of cellular signaling pathways of glia-neuron crosstalk. The model suggests that the onset of neurodegeneration can result as a compromise between two conflicting goals: short-term resilience to stressors versus long-term prevention of tissue damage.
Type IV pili are flexible filaments on the surface of bacteria, consisting of a helical assembly of pilin proteins. They are involved in bacterial motility (twitching), surface adhesion, biofilm formation and DNA uptake (natural transformation). Here, we use cryo-electron microscopy and mass spectrometry to show that the bacterium Thermus thermophilus produces two forms of type IV pilus ("wide" and "narrow"), differing in structure and protein composition. Wide pili are composed of the major pilin PilA4, while narrow pili are composed of a so-far uncharacterized pilin which we name PilA5. Functional experiments indicate that PilA4 is required for natural transformation, while PilA5 is important for twitching motility.
Non-random connectivity can emerge without structured external input driven by activity-dependent mechanisms of synaptic plasticity based on precise spiking patterns. Here we analyze the emergence of global structures in recurrent networks based on a triplet model of spike timing dependent plasticity (STDP) which depends on the interactions of three precisely-timed spikes and can describe plasticity experiments with varying spike frequency better than the classical pair-based STDP rule. We derive synaptic changes arising from correlations up to third-order and describe them as the sum of structural motifs which determine how any spike in the network influences a given synaptic connection through possible connectivity paths. This motif expansion framework reveals novel structural motifs under the triplet STDP rule, which support the formation of bidirectional connections and ultimately the spontaneous emergence of global network structure in the form of self-connected groups of neurons, or assemblies. We propose that under triplet STDP assembly structure can emerge without the need for externally patterned inputs or assuming a symmetric pair-based STDP rule common in previous studies. The emergence of non-random network structure under triplet STDP occurs through internally-generated higher-order correlations, which are ubiquitous in natural stimuli and neuronal spiking activity, and important for coding. We further demonstrate how neuromodulatory mechanisms that modulate the shape of the triplet STDP rule or the synaptic transmission function differentially promote structural motifs underlying the emergence of assemblies, and quantify the differences using graph theoretic measures.
Signal transfer of visual stimuli to V4 occurs in gamma-rhythmic, pulsed information packages
(2020)
Summary Selective visual attention allows the brain to focus on behaviorally relevant information while ignoring irrelevant signals. As a possible mechanism, routing by synchronization was proposed: neural populations sending attended signals align their gamma-rhythmic activities with receiving populations, such that spikes from the senders arrive at excitability peaks of the receivers, enhancing signal transfer. Conversely, the non-attended signals arrive unaligned to the receiver’s oscillation, reducing signal transfer. Therefore, visual signals should be transferred through periodically pulsed information packages, resulting in a modulation of the stimulus content within the receiver’s activity by its gamma phase and amplitude. To test this prediction, we quantified gamma phase-specific stimulus content within neural activity from area V4 of macaques performing a visual attention task. For the attended stimulus we find enhanced stimulus content reaching its maximum near excitability peaks, with effect magnitude increasing with oscillation amplitude, establishing a functional link between selective processing and gamma activity.
Synchronization has been implicated in neuronal communication, but causal evidence remains indirect. We used optogenetics to generate depolarizing currents in pyramidal neurons of cat visual cortex, emulating excitatory synaptic inputs under precise temporal control, while measuring spike output. Cortex transformed constant excitation into strong gamma-band synchronization, revealing the well-known cortical resonance. Increasing excitation with ramps increased the strength and frequency of synchronization. Slow, symmetric excitation profiles revealed hysteresis of power and frequency. Crucially, white-noise input sequences enabled causal analysis of network transmission, establishing that cortical resonance selectively transmits coherent input components. Models composed of recurrently coupled excitatory and inhibitory units uncovered a crucial role of feedback inhibition and suggest that hysteresis can arise through spike-frequency adaptation. The presented approach provides a powerful means to investigate the resonance properties of local circuits and probe how these properties transform input and shape transmission.