V1

The mapping from the visual field to V1 can be approximated by a log-polar transform. In this domain, scale is a left-right shift, and rotation is an up-down shift. When fed into a standard shift-invariant convolutional network (CNN), this provides scale and rotation invariance. Remarkably, despite the property of rotation invariance, this model provides a novel explanation for the inverted face effect. The effect comes about because, while the scale shift maps directly onto the flat cortex, rotation, as a circle group, does not. When the face is inverted, the features simply shift up and dow

Cognition arises from the coordinated interaction of multiple circuits with individual computational roles. While our ability to extract the dynamics underlying circuit computation from population activity recorded in individual areas, understanding how multiple areas jointly support distributed computation remains a challenge. Here we propose a novel multi-region statistical model which reflects a process level description of distributed neural computation. Specifically, the model includes local within-region nonlinear dynamics, potentially driven by task-specific inputs, and communication c

…recordings from hundreds of neurons in V1 and V2 in anesthetized macaque monkeys. Using a latent variable model, we identified the feedforward, feedback, and within-area signals that contributed to the measured responses. The identified feedforward and feedback signals contributed equally to explaining activity in the two areas, consistent with their equal anatomical

Many behaviors demand a balance between exploitation, which requires reliability, and exploration, which involves flexibility. In cortical circuits, much work has emphasized how attractor dynamics generate reliable behavior through the convergence of a network to specialized activity patterns for specific decisions and actions1. Other work has emphasized how cortical activity exhibits variability across trials of a behavior, which could in principle support flexibility in actions2. Here, through multi-area calcium imaging and recurrent neural network models, we propose that cortical activity f

Spontaneous activity in young ferrets (p22) is organised into modular, low-dimensional patterns across multiple cortical areas [1]. In the following two weeks (p22-43), these activity patterns undergo a transition similar in all areas, and show a gradual increase in dimensionality in most areas, suggesting an expansion of representational capacity [2]. However, it is unclear what the functional role of having low-dimensional activity patterns at the initial stage of development is, since one would expect the limited dimensionality to restrict representational learning. In this work, we investi

…changes in the primary visual cortex (V1) are potentially induced by cortico-cortical signals, such as those from V4 to V1, known to mediate visual attention in macaques [5]. However, relationships between such top-down signals and the aforementioned state-dependent changes have not been demonstrated directly. To investig

…high gamma rhythm (~60Hz) in both V1 and higher visual areas and a context-dependent low gamma rhythm (~30Hz) in V1. Our analysis of the open-source A

…features in the primary visual cortex (V1) in lit environments and under various luminance levels has been well-studied in many mammalian species. However, how the functionally distinct superficial and deep cortical layers of the V1 encode abrupt and sustained changes in light conditions in freely exploring animals remains unclear. To address this question, we recorded neuro

Selective processing of relevant aspects of our environment is crucial for goal-directed behavior. In the visual cortex, neurons encoding attended stimuli show synchronized activity within the γ-frequency band (30 – 100 Hz). Given this rhythmic nature of neuronal activity, with alternating phases of enhanced and diminished excitability, effective information transmission should not only necessitate synchronization of oscillatory activity but should critically depend on the phase difference between sender- and receiver neurons’ γ-activity. To test this, we simultaneously recorded neuronal activ

…which activity can be modulated in V1 and simultaneously, by the discriminability of evoked activity in higher visual areas (i.e. V2 and V4), for different stimulation parameters. However, the high-dimensionality of the stimulation parameter space makes impractical to exten

The steady-state visual evoked potential (SSVEP) is prevalent in cognitive neuroscience research yet much remains to be learned about how it is generated, including the relative contributions from different visual areas and the temporal delays among them. To address these questions, we exploited the idiosyncratic cortical folding patterns of retinotopically mapped visual areas to generate predictable variations in SSVEP topography across the visual field. Specifically, we regressed empirical topography variations against forward-model predictions generated by the Benson retinotopy atlas for di

The primary visual cortex (V1) of higher mammals such as cats and monkeys shows columns for stimulus features like orientation preference and ocular dominance, but such columns are not found in all groups of mammals. Models suggest that the afferent sampling density in V1 could be a key factor determining whether a columnar organization is established or not. Accordingly, in rodent V1, which – in comparison to other animals – has a low sampling density, there appear to be no orientation columns. However, the situation is less clear for ocular dominance. So far, in vivo imaging studies in mice

Flexible routing of visual input signals has been linked to changing patterns of synchrony between gamma rhythms of distant neural groups. It has been proposed that attention increases distant gamma-band synchrony by increasing the gamma frequency of the sender group processing the attended stimulus. This stimulus is subsequently preferentially processed. However, the frequency of gamma activity fluctuates over time, varies between areas, and depends on stimulus characteristics, which may complicate entrainment between oscillators. Here, we investigate the relationship between the frequency of

…Existing V1 prostheses stimulation strategies primarily focus on retinotopic stimulation, overlooking other important encoding properties and thereby failing to replicate the complex neural activity patterns of natural vision. Our work introduces a novel stimulation protocol that targets cortical tissue according to bot

…in the frequency range of 70-150 Hz in V1 and V4 of two macaque monkeys implanted with 16 Utah electrode arrays each [1]. The activity was recorded during resting state with occasional periods of drowsing. We observed prominent events with hi

…(LFP) in the primary visual cortex (V1) of the anesthetized mouse in response to contrast rate change – a complex, time dependent stimulus parameter. We investigate the population activity responsible for this surprise signal and identify neuronal subgroups

…plasticity in the adult visual cortex (V1) (Venturino et al, 2021). Microglia enable the PNN dismantling and their depletion prevented this effect. In a side investigation, we observed that under acute ketamine exposure, microglia adapt in a sex-dependent manner. Thus, is there a sex-specific effect of how microglia modulate neuronal activity? Here, we show a sex-specific adaptation of the neu

…studies using invasive recordings from V1 have shown that the simultaneous presentation of full-screen gratings at different TFs leads to more attenuation when the TFs are nearb

…between early visual cortical areas (V1 and V2) occur through a low-dimensional bottleneck, termed a communication subspace. In this talk, I will focus on the statistical methods we have developed for studying interactions between brain areas. First, I will describe Delayed Latents Across Groups (DLAG), designed to disentangle concurrent, bi-

The mapping from the visual field to V1 can be approximated by a log-polar transform. In this domain, scale is a left-right shift, and rotation is an up-down shift. When fed into a standard shift-invariant convolutional network, this provides scale and rotation invariance. However, translation invariance is lost. In our model, this is compensated for by multiple fixations on an object. Due to the high concentration of cones in the fovea with the dropoff of resolution in the periphery, fully 10 degrees of visual angle take up about half of V1, with the remaining 170 degrees (or so) taking up t

The proliferation of deep artificial neural network models has given rise to widespread interest in comparing such models to biological sensory systems. Model metamers – stimuli that produce the same responses in a model as a natural stimulus – have been proposed as a powerful test of the extent to which a model replicates the invariances of a biological sensory system. However, it has remained unclear whether discrepancies evident from metamers are the same as those identified by other model comparison benchmarks, and whether modifications that have been introduced to address other well-known

Working memory is a critical component of cognition as it allows for the flexible coupling of sensory input to motor output. However, imprecise maintenance of sensory information in working memory and the influence of intervening sensory inputs may lead to history-dependent biases in behaviour, such as recency or contraction bias. Recent studies have proposed that approximate (Lange et al., 2021) or imbalanced (Deneve & Jardri, 2016) hierarchical Bayesian inference may account for such biases, and have suggested a mapping of latent sensory representations onto the hierarchical organization

The functional visual hierarchy has been widely studied in primates but was only recently revealed in mice. It is thus unclear whether different areas along the mouse visual hierarchy also represent distinct levels of visual abstraction. To address this question, we carefully designed a visual stimulus set consisting of natural images of diverse objects, synthetic textures, and corresponding noise from a range of families. We acquired and analyzed a novel electrophysiology dataset with ~13,000 neurons recorded across six visual cortical regions along the visual hierarchy in awake mice using Ne

The size of a neuron’s receptive field increases along the visual hierarchy. Neurons in higher-order visual areas integrate information through a canonical mechanism called normalization, where neurons respond sublinearly to multiple stimuli in the receptive field (Carandini & Heeger, 2012). Neurons in visual cortex exhibit highly heterogeneous degrees of normalization, which can be measured by normalization index, defined as the sum of a neuron’s firing rate to each one of two stimuli divided by its firing rate when both stimuli are presented. Interestingly, it has been demonstrated that