Topic: layer 2/3

Distinguishing near and far visual cues is an essential computation that animals must carry out to guide behavior using vision. When animals move, self-motion creates motion parallax — an important but poorly understood source of depth information — whereby the speed of optic flow generated by self-motion depends on the depth of visual cues. This enables animals to estimate depth by comparing visual motion and self-motion speeds. As neurons in the mouse primary visual cortex (V1) are broadly modulated by locomotion, we hypothesized that they may integrate visual- and locomotion-related signals

Although most synapses within the neocortex are recurrent, the functional impact of this recurrence remains unclear. Prior experimental and theoretical work in the primary visual cortex (V1) suggests that recurrent excitation amplifies responses when signals are weak in order to optimize detection, while recurrent inhibition suppresses responses when signals are strong to optimize discrimination. Deepening our understanding of the logic for when recurrent activity facilitates cooperation among cortical ensembles and when it mediates competition beyond these initial results is fundamental to un

Inferring the mappings between actions and sensory feedback is a fundamental aspect of brain function. These mappings are described by two types of internal models – one that transforms motor signals into predictions of sensory feedback (forward model), and one that transforms sensory goals onto the motor commands necessary to achieve those goals (inverse model). One promising theoretical framework that provides computational ground for learning and maintaining these mappings, is that of predictive processing. The functional implementation of this algorithm in cortex implies two computational

Stringer et al. used two-photon calcium imaging to record activity from tens of thousands of V1 cells in head-fixed mice during a visual orientation discrimination task, and showed that the neural response encodes stimulus discrimination thresholds nearly 100 times more precise than the corresponding behavioral thresholds. Animal arousal state was monitored by measuring the locomotion speed during task execution, but could not explain this large discrepancy in discrimination threshold. Here, we take advantage of the fact that the behavioral data was acquired in that experiment, and ask whethe

Predictive learning hypotheses posit that the neocortex learns a hierarchical model of the structure of features in the environment. Under these hypotheses, expected or predictable features are differentiated from unexpected ones by comparing bottom-up and top-down streams of data, with unexpected features then driving changes in the representation of incoming stimuli. This is supported by numerous studies in early sensory cortices showing that pyramidal neurons respond particularly strongly to unexpected stimulus events. However, it remains unknown how their responses govern subsequent change

Biological circuits adapt their behaviour through experience and ongoing learning in a non-trivial manner. Experimental evidence shows differing neuronal population responses to familiar and novel stimuli [1, 2], indicating that neural circuits operate in distinct processing modes depending on stimulus familiarity. In layer 2/3 of mouse primary visual cortex (V1) specifically, populations of pyramidal and vasoactive-intestinal-peptide-expressing (VIP) cells show pronounced responses to novel visual stimuli (novelty responses) and weaker responses to familiar ones. Although experimental and the

At eye-opening, layer 2/3 (L2/3) of ferret primary visual cortex (V1) exhibits orientation-selective responses, modular (~periodic) patterns of visually-evoked and spontaneous activity, and spatially periodic orientation maps. L4 activity has weaker orientation selectivity (OS) than, and is spatially uncorrelated with, L2/3 responses at eye-opening, suggesting L2/3 may generate modular activity patterns independently of L4 map or activity organization. Here we characterize L4 and L2/3 receptive fields (RFs) and the L4 orientation map at eye-opening. We then model the connectivity by which L4 i

Activation of group I metabotropic glutamate receptors (mGluR) in the brain mediates changes in neuronal excitability, synaptic transmission, and network activity of cortical circuits and has been linked to learning-related plasticity, brain state-modulation as well as various human neurological disorders but studies investigating their effect in human brain are scarce and mostly focused on excitatory neurons. We studied the effects of group I mGluR activation on monosynaptic glutamatergic synapses arriving onto interneurons and disynaptic GABAergic synapses using dual whole-cell patch clamp r

Sounds are represented in the mouse auditory cortex in the form of a representational map, in which the similarity of global activity patterns elicited by a pair of sounds encodes their perceptual relationship. Interestingly, sensory processing in cortex is generally robust despite loss of neurons during aging, and even the accelerated loss during prodromal phases of neurodegeneration. Here, we probed the robustness of the representational map against the permanent removal of specific neurons and tested in how far its structure is stabilized by homeostatic network mechanisms. We combined longi

The cerebral cortex, consisting of six layers, plays a key role in higher brain functions such as motor planning, sensory processing, emotions, and consciousness. Classification of neuronal subtypes based on morphological, physiological, and genetic features is an indispensable process in understanding the mechanism of higher-order cerebral function.Neocortical pyramidal neurons in layer 2/3 (L2/3) integrate information from numerous brain regions and provide the majority of input to layer five neurons, facilitating information exchange with other cortices. Despite recent advances in genetic t

The spontaneous activity in the primary visual cortex (V1) of higher mammals shows spatially modular events of higher activity, where co-active regions are correlated to the underlying structure - orientation maps [1]. This activity travels in sparse waves, in line with the propagation speed of unmyelinated lateral connections [2]. Currently there is a lack of understanding of how these phenomena interact with each other and with evoked activity – so far, they have been only studied experimentally [1,4] and computationally [5,2] in isolation.We present a single large-scale spiking model of cat