tuning curves
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COCHLEAR SIGNALING MEDIATED BY HENSEN’S CELLS
PROJECT SUMMARY/ABSTRACT The organ of Corti has two types of auditory sensory cells (inner and outer hair cells) surrounded by nearly a dozen different types of supporting cells organized in a very meticulous pattern. Hair cells mediate the mechano-electrical transduction process of the organ of Corti and thus most cochlear auditory research has focused on these sensory cells. In contrast, much less is known about the different types of cochlear supporting cells, even though they likely impact hair cell function. Hensen’s cells are located laterally to the outer hair cell rows and appear to be the only cell type in the cochlear epithelium that expresses TRPA1 channels. These channels are widely known for their role as sensors of tissue damage and inflammation in nociceptive neurons. Not surprisingly, we recently found that Hensen’s cells are main sensors of tissue damage in the cochlear epithelium via the activation of TRPA1 channels (Velez-Ortega et al., Nat Commn, 2023). Additionally, our preliminary data also supports the role of Hensen’s cells in signaling pathways important for the proper innervation of the organ of Corti (aim 1), for the transmission of cochlear damage signals to the brain (aim 2), and for the regulation of hearing sensitivity after acoustic trauma (aim 3). Thus, here we will explore the hypothesis that TRPA1- mediated signaling pathways in the Hensen’s cells are required for the proper innervation and auditory function of the organ of Corti. In Aim 1 we will perform a detailed comparison of the morphology and synapses of afferent cochlear neurons of wild-type and Trpa1-/- mice at several developmental stages (using immunolabeling, confocal microscopy, STED microscopy, and electron microscopy) to assess the role of TRPA1 activity on the postnatal refinement of the cochlear innervation. Aim 2 will evaluate whether the afferent type II spiral ganglion neurons (SGN) can be activated downstream of TRPA1 channel gating in Hensen’s cells by testing responses of neonate and adult type II SGN to TRPA1 agonists (via live-cell time-lapse calcium imaging and patch clamp recordings of type II SGN dendrites). Aim 3 will test the impact of TRPA1 signaling in Hensen’s cells to the operating point of the cochlear transducer (via the recording of cochlear microphonics) and to cochlear tuning (via the recording of ABR tuning curves). This study is significant because it will contribute to our understanding of the cellular (Hensen’s cells plus type II SGN) and molecular (TRPA1 channels) mechanisms of the elusive cochlear nociceptive pathway. In addition, given that the loss of TRPA1 channels does not affect hearing thresholds in mice, we believe that undiagnosed deficits in TRPA1-dependent responses in the human population could represent a hidden susceptibility for cochlear damage after noise exposure or other insults.
Efficient Random Codes in a Shallow Neural Network
Efficient coding has served as a guiding principle in understanding the neural code. To date, however, it has been explored mainly in the context of peripheral sensory cells with simple tuning curves. By contrast, ‘deeper’ neurons such as grid cells come with more complex tuning properties which imply a different, yet highly efficient, strategy for representing information. I will show that a highly efficient code is not specific to a population of neurons with finely tuned response properties: it emerges robustly in a shallow network with random synapses. Here, the geometry of population responses implies that optimality obtains from a tradeoff between two qualitatively different types of error: ‘local’ errors (common to classical neural population codes) and ‘global’ (or ‘catastrophic’) errors. This tradeoff leads to efficient compression of information from a high-dimensional representation to a low-dimensional one. After describing the theoretical framework, I will use it to re-interpret recordings of motor cortex in behaving monkey. Our framework addresses the encoding of (sensory) information; if time allows, I will comment on ongoing work that focuses on decoding from the perspective of efficient coding.
Self-organized formation of discrete grid cell modules from smooth gradients
Modular structures in myriad forms — genetic, structural, functional — are ubiquitous in the brain. While modularization may be shaped by genetic instruction or extensive learning, the mechanisms of module emergence are poorly understood. Here, we explore complementary mechanisms in the form of bottom-up dynamics that push systems spontaneously toward modularization. As a paradigmatic example of modularity in the brain, we focus on the grid cell system. Grid cells of the mammalian medial entorhinal cortex (mEC) exhibit periodic lattice-like tuning curves in their encoding of space as animals navigate the world. Nearby grid cells have identical lattice periods, but at larger separations along the long axis of mEC the period jumps in discrete steps so that the full set of periods cluster into 5-7 discrete modules. These modules endow the grid code with many striking properties such as an exponential capacity to represent space and unprecedented robustness to noise. However, the formation of discrete modules is puzzling given that biophysical properties of mEC stellate cells (including inhibitory inputs from PV interneurons, time constants of EPSPs, intrinsic resonance frequency and differences in gene expression) vary smoothly in continuous topographic gradients along the mEC. How does discreteness in grid modules arise from continuous gradients? We propose a novel mechanism involving two simple types of lateral interaction that leads a continuous network to robustly decompose into discrete functional modules. We show analytically that this mechanism is a generic multi-scale linear instability that converts smooth gradients into discrete modules via a topological “peak selection” process. Further, this model generates detailed predictions about the sequence of adjacent period ratios, and explains existing grid cell data better than existing models. Thus, we contribute a robust new principle for bottom-up module formation in biology, and show that it might be leveraged by grid cells in the brain.
The emergence of contrast invariance in cortical circuits
Neurons in the primary visual cortex (V1) encode the orientation and contrast of visual stimuli through changes in firing rate (Hubel and Wiesel, 1962). Their activity typically peaks at a preferred orientation and decays to zero at the orientations that are orthogonal to the preferred. This activity pattern is re-scaled by contrast but its shape is preserved, a phenomenon known as contrast invariance. Contrast-invariant selectivity is also observed at the population level in V1 (Carandini and Sengpiel, 2004). The mechanisms supporting the emergence of contrast-invariance at the population level remain unclear. How does the activity of different neurons with diverse orientation selectivity and non-linear contrast sensitivity combine to give rise to contrast-invariant population selectivity? Theoretical studies have shown that in the balance limit, the properties of single-neurons do not determine the population activity (van Vreeswijk and Sompolinsky, 1996). Instead, the synaptic dynamics (Mongillo et al., 2012) as well as the intracortical connectivity (Rosenbaum and Doiron, 2014) shape the population activity in balanced networks. We report that short-term plasticity can change the synaptic strength between neurons as a function of the presynaptic activity, which in turns modifies the population response to a stimulus. Thus, the same circuit can process a stimulus in different ways –linearly, sublinearly, supralinearly – depending on the properties of the synapses. We found that balanced networks with excitatory to excitatory short-term synaptic plasticity cannot be contrast-invariant. Instead, short-term plasticity modifies the network selectivity such that the tuning curves are narrower (broader) for increasing contrast if synapses are facilitating (depressing). Based on these results, we wondered whether balanced networks with plastic synapses (other than short-term) can support the emergence of contrast-invariant selectivity. Mathematically, we found that the only synaptic transformation that supports perfect contrast invariance in balanced networks is a power-law release of neurotransmitter as a function of the presynaptic firing rate (in the excitatory to excitatory and in the excitatory to inhibitory neurons). We validate this finding using spiking network simulations, where we report contrast-invariant tuning curves when synapses release the neurotransmitter following a power- law function of the presynaptic firing rate. In summary, we show that synaptic plasticity controls the type of non-linear network response to stimulus contrast and that it can be a potential mechanism mediating the emergence of contrast invariance in balanced networks with orientation-dependent connectivity. Our results therefore connect the physiology of individual synapses to the network level and may help understand the establishment of contrast-invariant selectivity.
Changes in tuning curves, not neural population covariance, improve category separability in the primate ventral visual pathway
COSYNE 2025
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