Neural Comput - Computing with a canonical neural circuits model with pool normalization and modulating feedback.

Tópicos

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Resumo

Evidence suggests that the brain uses an operational set of canonical computations like normalization, input filtering, and response gain enhancement via reentrant feedback. Here, we propose a three-stage columnar architecture of cascaded model neurons to describe a core circuit combining signal pathways of feedforward and feedback processing and the inhibitory pooling of neurons to normalize the activity. We present an analytical investigation of such a circuit by first reducing its detail through the lumping of initial feedforward response filtering and reentrant modulating signal amplification. The resulting excitatory-inhibitory pair of neurons is analyzed in a 2D phase-space. The inhibitory pool activation is treated as a separate mechanism exhibiting different effects. We analyze subtractive as well as divisive (shunting) interaction to implement center-surround mechanisms that include normalization effects in the characteristics of real neurons. Different variants of a core model architecture are derived and analyzed--in particular, individual excitatory neurons (without pool inhibition), the interaction with an inhibitory subtractive or divisive (i.e., shunting) pool, and the dynamics of recurrent self-excitation combined with divisive inhibition. The stability and existence properties of these model instances are characterized, which serve as guidelines to adjust these properties through proper model parameterization. The significance of the derived results is demonstrated by theoretical predictions of response behaviors in the case of multiple interacting hypercolumns in a single and in multiple feature dimensions. In numerical simulations, we confirm these predictions and provide some explanations for different neural computational properties. Among those, we consider orientation contrast-dependent response behavior, different forms of attentional modulation, contrast element grouping, and the dynamic adaptation of the silent surround in extraclassical receptive field configurations, using only slight variations of the same core reference model.

Resumo Limpo

evid suggest brain use oper set canon comput like normal input filter respons gain enhanc via reentrant feedback propos threestag columnar architectur cascad model neuron describ core circuit combin signal pathway feedforward feedback process inhibitori pool neuron normal activ present analyt investig circuit first reduc detail lump initi feedforward respons filter reentrant modul signal amplif result excitatoryinhibitori pair neuron analyz d phasespac inhibitori pool activ treat separ mechan exhibit differ effect analyz subtract well divis shunt interact implement centersurround mechan includ normal effect characterist real neuron differ variant core model architectur deriv analyzedin particular individu excitatori neuron without pool inhibit interact inhibitori subtract divis ie shunt pool dynam recurr selfexcit combin divis inhibit stabil exist properti model instanc character serv guidelin adjust properti proper model parameter signific deriv result demonstr theoret predict respons behavior case multipl interact hypercolumn singl multipl featur dimens numer simul confirm predict provid explan differ neural comput properti among consid orient contrastdepend respons behavior differ form attent modul contrast element group dynam adapt silent surround extraclass recept field configur use slight variat core refer model

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