Multi-objective Evolutionary Algorithms to Investigate Neurocomputational Issues: The Case Study of Basal Ganglia Models

Liénard, Jean‐Sylvain; Guillot, Agnès; Girard, Benoît

doi:10.1007/978-3-642-15193-4_56

Cited by 4 publications

(6 citation statements)

References 23 publications

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“…Most real world optimization problems involve the simultaneous minimization of several objectives [ 69 ]. Thus when comparing different model architectures, it may be helpful to consider trade-offs separately along different dimensions [ 36 , 70 , 71 ]. The current study focused in particular on the trade-off between model prediction accuracy and parameter count.…”

Section: Discussionmentioning

confidence: 99%

The Essential Complexity of Auditory Receptive Fields

2015

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Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models.

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Section: Discussionmentioning

confidence: 99%

The Essential Complexity of Auditory Receptive Fields

2015

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“…To position our methodology among previous studies, it is also relevant to study how other works have used stochastic optimization methods to constrain the parameter choice of BG models. In Wang et al (2007); Liénard et al (2010) the aim of the optimization was to obtain a given functionality, namely selection. But the neuron models used were very abstract leaky integrators, and their main parameters were synaptic connection strengths coded as a numerical value in an arbitrary range.…”

Section: Related Studiesmentioning

confidence: 99%

A biologically constrained model of the whole basal ganglia addressing the paradoxes of connections and selection

Liénard

Girard

2013

J Comput Neurosci

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The basal ganglia nuclei form a complex network of nuclei often assumed to perform selection, yet their individual roles and how they influence each other is still largely unclear. In particular, the ties between the external and internal parts of the globus pallidus are paradoxical, as anatomical data suggest a potent inhibitory projection between them while electrophysiological recordings indicate that they have similar activities. Here we introduce a theoretical study that reconciles both views on the intra-pallidal projection, by providing a plausible characterization of the relationship between the external and internal globus pallidus. Specifically, we developed a mean-field model of the whole basal ganglia, whose parameterization is optimized to respect best a collection of numerous anatomical and electrophysiological data. We first obtained models respecting all our constraints, hence anatomical and electrophysiological data on the intrapallidal projection are globally consistent. This model furthermore predicts that both aforementioned views about the intra-pallidal projection may be reconciled when this projection is weakly inhibitory, thus making it possible to support similar neural activity in both nuclei and for the entire basal ganglia to select between actions. Second, we predicts that afferent projections are substantially unbalanced towards the external segment, as it receives the strongest excitation from STN and the weakest inhibition from the striatum. Finally, our study strongly suggest that the intrapallidal connection pattern is not focused but diffuse, as this latter pattern is more efficient for the overall selection performed in the basal ganglia.

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“…In order to gain a better understanding on the mecanisms governing it, various neuro-computational models have been proposed (for a review, see [72]). Among them are the recent CBG model [1] and the more classical GPR model [2]; the parameters of both of them have been evolved with MOEA in a previous work [73].…”

Section: Discussionmentioning

confidence: 99%

“…Each connection parameter is to be evolved in the range [0.05, 1], as setting a connection weight to zero would be equivalent to delete this connection. For more details on the whereabouts of each models, we refer to the original articles [1,2] or to our previous work [73].…”

Section: Description Of the Modelsmentioning

confidence: 99%

Multi-objective analysis of computational models

Doncieux¹,

Liénard²,

Girard³

et al. 2015

Preprint

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Computational models are of increasing complexity and their behavior may in particular emerge from the interaction of different parts. Studying such models becomes then more and more difficult and there is a need for methods and tools supporting this process. Multi-objective evolutionary algorithms generate a set of trade-off solutions instead of a single optimal solution. The availability of a set of solutions that have the specificity to be optimal relative to carefully chosen objectives allows to perform data mining in order to better understand model features and regularities. We review the corresponding work, propose a unifying framework, and highlight its potential use. Typical questions that such a methodology allows to address are the following: what are the most critical parameters of the model? What are the relations between the parameters and the objectives? What are the typical behaviors of the model? Two examples are provided to illustrate the capabilities of the methodology. The features of a flapping-wing robot are thus evaluated to find out its speed-energy relation, together with the criticality of its parameters. A neurocomputational model of the Basal Ganglia brain nuclei is then considered and its most salient features according to this methodology are presented and discussed.

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Multi-objective Evolutionary Algorithms to Investigate Neurocomputational Issues: The Case Study of Basal Ganglia Models

Cited by 4 publications

References 23 publications

The Essential Complexity of Auditory Receptive Fields

The Essential Complexity of Auditory Receptive Fields

A biologically constrained model of the whole basal ganglia addressing the paradoxes of connections and selection

Multi-objective analysis of computational models

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