Person: Meister, Markus
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Meister
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Markus
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Meister, Markus
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Publication A Wireless Multi-Channel Neural Amplifier for Freely Moving Animals(Nature Publishing Group, 2011) Szuts, Tobi A; Fadeyev, Vitaliy; Kachiguine, Sergei; Sher, Alexander; Grivich, Matthew V; Agrochão, Margarida; Hottowy, Pawel; Dabrowski, Wladyslaw; Lubenov, Evgueniy V; Siapas, Athanassios G; Uchida, Naoshige; Litke, Alan M; Meister, MarkusConventional neural recording systems restrict behavioral experiments to a flat indoor environment compatible with the cable that tethers the subject to recording instruments. To overcome these constraints, we developed a wireless multi-channel system for recording neural signals from rats. The device takes up to 64 voltage signals from implanted electrodes, samples each at 20 kHz, time-division multiplexes them into one signal and transmits that output by radio frequency to a receiver up to 60 m away. The system introduces <4 μV of electrode-referred noise, comparable to wired recording systems, and outperforms existing rodent telemetry systems in channel count, weight and transmission range. This allows effective recording of brain signals in freely behaving animals. We report measurements of neural population activity taken outdoors and in tunnels. Neural firing in the visual cortex was relatively sparse, correlated even across large distances and was strongly influenced by locomotor activity.Publication Dynamical Adaptation in Photoreceptors(Public Library of Science, 2013) Clark, Damon A.; Benichou, Raphael; Meister, Markus; Azeredo da Silveira, RavaAdaptation is at the heart of sensation and nowhere is it more salient than in early visual processing. Light adaptation in photoreceptors is doubly dynamical: it depends upon the temporal structure of the input and it affects the temporal structure of the response. We introduce a non-linear dynamical adaptation model of photoreceptors. It is simple enough that it can be solved exactly and simulated with ease; analytical and numerical approaches combined provide both intuition on the behavior of dynamical adaptation and quantitative results to be compared with data. Yet the model is rich enough to capture intricate phenomenology. First, we show that it reproduces the known phenomenology of light response and short-term adaptation. Second, we present new recordings and demonstrate that the model reproduces cone response with great precision. Third, we derive a number of predictions on the response of photoreceptors to sophisticated stimuli such as periodic inputs, various forms of flickering inputs, and natural inputs. In particular, we demonstrate that photoreceptors undergo rapid adaptation of response gain and time scale, over ∼ 300 ms—i. e., over the time scale of the response itself—and we confirm this prediction with data. For natural inputs, this fast adaptation can modulate the response gain more than tenfold and is hence physiologically relevant.Publication Precision and diversity in an odor map on the olfactory bulb(Springer Nature, 2009) Soucy, Edward; Albeanu, Dinu F.; Fantana, Antoniu L.; Murthy, Venkatesh; Meister, MarkusWe explored the map of odor space created by glomeruli on the olfactory bulb of both rat and mouse. Identified glomeruli could be matched across animals by their response profile to hundreds of odors. Their layout in different individuals varied by only ~1 glomerular spacing, corresponding to a precision of 1 part in 1,000. Across species, mouse and rat share many glomeruli with apparently identical odor tuning, arranged in a similar layout. In mapping the position of a glomerulus to its odor tuning, we found only a coarse relationship with a precision of ~5 spacings. No chemotopic order was apparent on a finer scale and nearby glomeruli were almost as diverse in their odor sensitivity as distant ones. This local diversity of sensory tuning stands in marked distinction from other brain maps. Given the reliable placement of the glomeruli, it represents a feature, not a flaw, of the olfactory bulb.Publication Laminar Restriction of Retinal Ganglion Cell Dendrites and Axons: Subtype-Specific Developmental Patterns Revealed with Transgenic Markers(Society for Neuroscience, 2010) Kim, In-Jung; Zhang, Yifeng; Meister, Markus; Sanes, JoshuaRetinal ganglion cells (RGCs), which transfer information from the eye to the brain, are heterogeneous in structure and function, but developmental studies have generally treated them as a single group. Here, we investigate the development of RGC axonal and dendritic arbors using four mouse transgenic lines in which nonoverlapping subsets of RGCs are indelibly labeled with a fluorescent protein. Each subset has a distinct functional signature, size, and morphology. Dendrites of each subset are restricted to specific sublaminae within the inner plexiform layer in adulthood, but acquire their restriction in different ways: one subset has lamina-restricted dendrites from an early postnatal stage, a second remodels an initially diffuse pattern, and two others develop stepwise. Axons of each subset arborize in discrete laminar zones within the lateral geniculate nucleus or superior colliculus, demonstrating previously unrecognized subdivisions of retinorecipient layers. As is the case for dendrites, lamina-restricted axonal projections of RGC subsets develop in different ways. For example, while axons of two RGC subsets arborize in definite zones of the superior colliculus from an early postnatal stage, axons of another subset initially occupy a deep layer, then translocate to a narrow subpial zone. Together, these results show that RGC subsets use a variety of strategies to construct lamina-restricted dendritic and axonal arbors. Taking account of these subtype-specific features will facilitate identification of the molecules and cells that regulate arbor formation.Publication Bayesian Model of Dynamic Image Stabilization in the Visual System(Proceedings of the National Academy of Sciences, 2010) Burak, Yoram; Rokni, Uri; Meister, Markus; Sompolinsky, HaimHumans can resolve the fine details of visual stimuli although the image projected on the retina is constantly drifting relative to the photoreceptor array. Here we demonstrate that the brain must take this drift into account when performing high acuity visual tasks. Further, we propose a decoding strategy for interpreting the spikes emitted by the retina, which takes into account the ambiguity caused by retinal noise and the unknown trajectory of the projected image on the retina. A main difficulty, addressed in our proposal, is the exponentially large number of possible stimuli, which renders the ideal Bayesian solution to the problem computationally intractable. In contrast, the strategy that we propose suggests a realistic implementation in the visual cortex. The implementation involves two populations of cells, one that tracks the position of the image and another that represents a stabilized estimate of the image itself. Spikes from the retina are dynamically routed to the two populations and are interpreted in a probabilistic manner. We consider the architecture of neural circuitry that could implement this strategy and its performance under measured statistics of human fixational eye motion. A salient prediction is that in high acuity tasks, fixed features within the visual scene are beneficial because they provide information about the drifting position of the image. Therefore, complete elimination of peripheral features in the visual scene should degrade performance on high acuity tasks involving very small stimuli.Publication Computing Complex Visual Features with Retinal Spike Times(Public Library of Science, 2013) Gütig, Robert; Gollisch, Tim; Sompolinsky, Haim; Meister, MarkusNeurons in sensory systems can represent information not only by their firing rate, but also by the precise timing of individual spikes. For example, certain retinal ganglion cells, first identified in the salamander, encode the spatial structure of a new image by their first-spike latencies. Here we explore how this temporal code can be used by downstream neural circuits for computing complex features of the image that are not available from the signals of individual ganglion cells. To this end, we feed the experimentally observed spike trains from a population of retinal ganglion cells to an integrate-and-fire model of post-synaptic integration. The synaptic weights of this integration are tuned according to the recently introduced tempotron learning rule. We find that this model neuron can perform complex visual detection tasks in a single synaptic stage that would require multiple stages for neurons operating instead on neural spike counts. Furthermore, the model computes rapidly, using only a single spike per afferent, and can signal its decision in turn by just a single spike. Extending these analyses to large ensembles of simulated retinal signals, we show that the model can detect the orientation of a visual pattern independent of its phase, an operation thought to be one of the primitives in early visual processing. We analyze how these computations work and compare the performance of this model to other schemes for reading out spike-timing information. These results demonstrate that the retina formats spatial information into temporal spike sequences in a way that favors computation in the time domain. Moreover, complex image analysis can be achieved already by a simple integrate-and-fire model neuron, emphasizing the power and plausibility of rapid neural computing with spike times.Publication Modeling Convergent ON and OFF Pathways in the Early Visual System(Springer-Verlag, 2008) Gollisch, Tim; Meister, MarkusFor understanding the computation and function of single neurons in sensory systems, one needs to investigate how sensory stimuli are related to a neuron’s response and which biological mechanisms underlie this relationship. Mathematical models of the stimulus–response relationship have proved very useful in approaching these issues in a systematic, quantitative way. A starting point for many such analyses has been provided by phenomenological “linear–nonlinear” (LN) models, which comprise a linear filter followed by a static nonlinear transformation. The linear filter is often associated with the neuron’s receptive field. However, the structure of the receptive field is generally a result of inputs from many presynaptic neurons, which may form parallel signal processing pathways. In the retina, for example, certain ganglion cells receive excitatory inputs from ON-type as well as OFF-type bipolar cells. Recent experiments have shown that the convergence of these pathways leads to intriguing response characteristics that cannot be captured by a single linear filter. One approach to adjust the LN model to the biological circuit structure is to use multiple parallel filters that capture ON and OFF bipolar inputs. Here, we review these new developments in modeling neuronal responses in the early visual system and provide details about one particular technique for obtaining the required sets of parallel filters from experimental data.Publication Rat Olfactory Bulb Mitral Cells Receive Sparse Glomerular Inputs(Elsevier, 2008) Fantana, Antoniu L.; Soucy, Edward R.; Meister, MarkusCenter-surround receptive fields are a fundamental unit of brain organization. It has been proposed that olfactory bulb mitral cells exhibit this functional circuitry, with excitation from one glomerulus and inhibition from a broad field of glomeruli within reach of the lateral dendrites. We investigated this hypothesis using a combination of in vivo intrinsic imaging, single-unit recording, and a large panel of odors. Assuming a broad inhibitory field, a mitral cell would be influenced by >100 contiguous glomeruli and should respond to many odors. Instead, the observed response rate was an order of magnitude lower. A quantitative model indicates that mitral cell responses can be explained by just a handful of glomeruli. These glomeruli are spatially dispersed on the bulb and represent a broad range of odor sensitivities. We conclude that mitral cells do not have center-surround receptive fields. Instead, each mitral cell performs a specific computation combining a small and diverse set of glomerular inputs.Publication Retinal Ganglion Cells Can Rapidly Change Polarity from Off to On(Public Library of Science, 2007) Geffen, Maria Neimark; de Vries, Saskia E. J.; Meister, MarkusRetinal ganglion cells are commonly classified as On-center or Off-center depending on whether they are excited predominantly by brightening or dimming within the receptive field. Here we report that many ganglion cells in the salamander retina can switch from one response type to the other, depending on stimulus events far from the receptive field. Specifically, a shift of the peripheral image—as produced by a rapid eye movement—causes a brief transition in visual sensitivity from Off-type to On-type for approximately 100 ms. We show that these ganglion cells receive inputs from both On and Off bipolar cells, and the Off inputs are normally dominant. The peripheral shift strongly modulates the strength of these two inputs in opposite directions, facilitating the On pathway and suppressing the Off pathway. Furthermore, we identify certain wide-field amacrine cells that contribute to this modulation. Depolarizing such an amacrine cell affects nearby ganglion cells in the same way as the peripheral image shift, facilitating the On inputs and suppressing the Off inputs. This study illustrates how inhibitory interneurons can rapidly gate the flow of information within a circuit, dramatically altering the behavior of the principal neurons in the course of a computation.Publication A Neural Computation for Visual Acuity in the Presence of Eye Movements(Public Library of Science, 2007) Pitkow, Xaq; Sompolinsky, Haim; Meister, MarkusHumans can distinguish visual stimuli that differ by features the size of only a few photoreceptors. This is possible despite the incessant image motion due to fixational eye movements, which can be many times larger than the features to be distinguished. To perform well, the brain must identify the retinal firing patterns induced by the stimulus while discounting similar patterns caused by spontaneous retinal activity. This is a challenge since the trajectory of the eye movements, and consequently, the stimulus position, are unknown. We derive a decision rule for using retinal spike trains to discriminate between two stimuli, given that their retinal image moves with an unknown random walk trajectory. This algorithm dynamically estimates the probability of the stimulus at different retinal locations, and uses this to modulate the influence of retinal spikes acquired later. Applied to a simple orientation-discrimination task, the algorithm performance is consistent with human acuity, whereas naive strategies that neglect eye movements perform much worse. We then show how a simple, biologically plausible neural network could implement this algorithm using a local, activity-dependent gain and lateral interactions approximately matched to the statistics of eye movements. Finally, we discuss evidence that such a network could be operating in the primary visual cortex.