Topic: lateral inhibition

The internal and external world is thought to be represented by distributed patterns of cortical activity. The emergence of these cortical representations over the course of development remains an unresolved question. In this talk, I share results from a series of recent studies combining theory and experiments in the cortex of the ferret, a species with a well-defined columnar organization and modular network of orientation-selective responses in visual cortex. I show that prior to the onset of structured sensory experience, endogenous mechanisms set up a highly organized cortical network str

Decisions about where to move the eyes depend on neurons in frontal eye field (FEF). Movement neurons in FEF accumulate salience evidence derived from FEF visual neurons to select the location of a saccade target among distractors. How visual neurons achieve this salience representation is unknown. We present a neuro-computational model of target selection called Salience by Competitive and Recurrent Interactions (SCRI), based on the competitive interaction model of attentional selection and decision-making (Smith & Sewell, 2013). SCRI selects targets by synthesizing localization and identific

The role of granule cells in olfactory processing is surrounded by several enigmatic observations, such as the existence of reciprocal spines and the mechanisms for GABA release from them, the missing evidence for functional reciprocal connectivity, and the apparently low inhibitory drive of granule cells, both with respect to recurrent and lateral inhibition. Here, I summarize recent results with regard to GABA release, leading to a novel hypothesis on granule cell function that has the potential to resolve most of these enigmas. I predict that granule cells provide dynamically switched later

The brain computes internal representations by applying highly recurrent dynamics to feed-forward input. Such dynamics may be viewed as analogous to the inference step in latent variables models, where one usually follows the gradient of the latent posterior distribution until a stable state is reached. At the stable state, when this gradient has approximately zero norm, a parameter optimization step can be applied to slightly increase the likelihood of the current sample under the model. Here we suggest this procedure, which closely matches the expectation-maximization (EM) algorithm, can be

For the olfactory associative learning to be specific, stimulus representation in the Kenyon cell (KC) population should be accurate with minimal activity overlap between different stimuli, which is achieved by the sparse and highly odor-specific KC response patterns. While a multitude of cellular and network mechanisms substantially increase the KC population sparsity, the proportion of KCs responding unreliably to a given odor over multiple trials is roughly three times larger than that of reliable responders [1, 3], leading to substantial response correlations between different odors. Consi

Interactions between spiking units grouped in distinct excitatory (E) and inhibitory (I) populations can result in a complex dynamical landscape. Part of this complexity, namely dynamics that depend on undelayed connectivities and external inputs, is retained by a simplified model, the threshold-linear network (TLN), which allows to derive fully analytical conditions for the emergence of multistability [1], paradoxical perturbation effects [2,3], and some oscillations [4,5]. These results, however, have only been applied to specific architectures, such as canonical circuits or all-inhibitory n

A large part of mammalian brains is dedicated to sensory processing. Sensory receptors extract distinct but partly correlated features of the environment that vary at different timescales, and then transmit them through subcortical structures and a succession of recurrently connected cortical areas. A general theory on how sensory pathways encode, propagate, and transform sensory features is still missing. To understand how biophysically realistic neural networks support these fundamental brain functions, we developed an analytical framework based on the principle of efficient coding (Barlow,