Rapid Long-Range Disynaptic Inhibition Explains the Formation of Cortical Orientation Maps

Abstract

Competitive interactions are believed to underlie many types of cortical processing, ranging from memory formation, attention and development of cortical functional organization (e.g., development of orientation maps in primary visual cortex). In the latter case, the competitive interactions happen along the cortical surface, with local populations of neurons reinforcing each other, while competing with those displaced more distally. This specific configuration of lateral interactions is however in stark contrast with the known properties of the anatomical substrate, i.e., excitatory connections (mediating reinforcement) having longer reach than inhibitory ones (mediating competition). No satisfactory biologically plausible resolution of this conflict between anatomical measures, and assumed cortical function has been proposed. Recently a specific pattern of delays between different types of neurons in cat cortex has been discovered, where direct mono-synaptic excitation has approximately the same delay, as the combined delays of the disynaptic inhibitory interactions between excitatory neurons (i.e., the sum of delays from excitatory to inhibitory and from inhibitory to excitatory neurons). Here we show that this specific pattern of delays represents a biologically plausible explanation for how short-range inhibition can support competitive interactions that underlie the development of orientation maps in primary visual cortex. We demonstrate this statement analytically under simplifying conditions, and subsequently show using network simulations that development of orientation maps is preserved when long-range excitation, direct inhibitory to inhibitory interactions, and moderate inequality in the delays between excitatory and inhibitory pathways is added.

Computational Modeling of Information Propagation during the Sleep–Waking Cycle

“…The extrinsic input comes from non-explicitly modeled distant cortical columns that impinges on population p and i only through excitatory synapses. This is in agreement with the morphology of cortical neurons, whereby pyramidal neurons have larger projecting axons with respect to the more local and constrained range of inhibitory axons [36][37][38]. This has also been reflected in numerous computational studies [30,[39][40][41][42].…”

“…The rest of parameters are the same for the perturbed and unperturbed cortical columns. We consider that the connectivity between the two cortical columns is symmetric and excitatory due to the longer axons of pyramidal neurons as compared to the short-range axons of inhibitory neurons [30,[36][37][38][39][40][41]. The average AMPAergic conductance of excitatory synapses between the two cortical columns will be referred here as inter-conductance, as opposed to the intra-conductance of each single cortical column.…”

Shaping the collision selectivity in a looming sensitive neuron model with parallel ON and OFF pathways and spike frequency adaptation

“…The ON and OFF pathways have been explored in the visual systems of many animals such as flies [28,29,30,31,32,33] and vertebrates [34]; these include mammals like rabbits [35], mice [36], cats [37] and monkeys [38]. Such a structure reveals an important theory of encoding visual information separately in an animal's preliminary motion-detecting circuitry: luminance increments and decrements flow into the ON and OFF pathways, respectively [30,35,31,36].…”

An anatomy-based V1 model: Extraction of Low-level Features, Reduction of distortion and a V1-inspired SOM

“…However long-range lateral influence by pyramidal neurons is still possible by an indirect 2-step process, where a pyramidal neuron first excites an interneuron, which in turn inhibits another neuron further away. Such disynaptic inhibition can give rise to Mexican hat-shaped lateral influences [30]. Based on these above-mentioned findings we consider an approximate scenario in our model.…”