Alexandre Pitti

Mathias Quoy

Catherine Lavandier

Sofiane Boucenna

Complementary Working Memories using Free-Energy Optimization for Learning Features and Structure in Sequences

come

Complementary Working Memories using Free-Energy Optimization for Learning Features and Structure in Sequences

Alexandre Pitti 1 * , Mathias Quoy 1 , Catherine Lavandier 1 and Sofiane Boucenna 1

Abstract-We propose a global framework for modeling the cortico-basal system (CX-BG) and the fronto-striatal system (PFC-BG) for the generation and recall of audio memory sequences; ie, sound perception and speech production. Our genuine model is based on the neural architecture called INFERNO standing for Iterative Free-Energy Optimization of Recurrent Neural Networks. Free-energy (FE) corresponds to the prediction error on internal or external noise. FE minimization is used for exploring, selecting and learning in PFC the optimal choices of actions to perform in the BG network (eg sound production) in order to reproduce and control the most accurately possible the spike trains representing sounds in CX. The difference between the two working memories relies in the neural coding itself, which is based on temporal ordering in the CX-BG networks (Spike Timing-Dependent Plasticity) and on the rank ordering in the sequence in the PFC-BG networks (gating or gain-modulation). We detail in this short paper the CX-BG system responsible to encode the audio primitives at few milliseconds order, and the PFC-BG system responsible for the learning of temporal structure in sequences. Two experiments done with a small and a big audio database show the capabilities of exploration, generalization and robustness to noise of the neural architecture to retrieve audio primitives as well as long-range sequences based on structure detection. Although both learning mechanisms are implemented with the same algorithm of rank-order coding, the CX-BG system realizes a model-free recurrent neural network (INFERNO) and the PFC-BG system implements a gated recurrent neural network (INFERNO GATE).

I. INTRODUCTION

In different brain areas, working memories (WMs) are hypothesized to embed neural processes with forward and inverse models that can encode, anticipate and eventually control incoming signals to be more robust and to overcome their variability [START_REF] Wolpert | A unifying computational framework for motor control and social interaction[END_REF]. Two brain areas namely the Basal Ganglia (BG) that selects actions with respect to current states [START_REF] Doya | Metalearning and neuromodulation[END_REF] and the Prefrontal Cortex (PFC) that represents forthcoming actions with respect to current contexts [START_REF] Buschman | Goal-direction and top-down control[END_REF], are important for embedding these WMs. Being part of two different loops but connected at the BG level, they realize reactive (BG) and proactive (PFC) control, processing information differently and at different speed, see Fig. 1.

II. RELATED WORKS

On the one hand, some evidences indicate that the striatum in BG has a principal function in learning-related plasticity associated with selecting one set of actions from many, resulting in the acquisition of habitual behavior [START_REF] Graybiel | The basal ganglia and chunking of action repertoires[END_REF]. On the other hand, PFC achieves behavioral planning in terms of the 1 University Paris-Seine, University of Cergy-Pontoise, ENSEA, CNRS, France surname.name@u-cergy.fr end result, rather than in terms of the movement required to perform the task [START_REF] Tanji | Concept-based behavioral planning and the lateral prefrontal cortex[END_REF].

Graybiel and Grafton suggest in [START_REF] Graybiel | The Striatum: Where Skills and Habits Meet[END_REF] that proactive control is associated with sustained and/or anticipatory activation of lateral PFC, which reflects the active maintenance of task goals. By contrast, reactive control should be reflected in transient activation, along with a wider network of additional brain regions such as the BG. Therefore, these two control mechanisms differ in terms of the involvement during learning and retrieving tasks or sequences, with the BG dynamics working at a faster pace than the PFC.

In Machine Learning, reactive and proactive control relate to what is called model-free and model-based systems in Reinforcement Learning (RL) [START_REF] Barto | Adaptive critics and the basal ganglia[END_REF], [START_REF] Schultz | A neural substrate of prediction and reward[END_REF], having one system for stimulus-response tasks doing greedy-like optimization and the other learning distinct policies for prediction, which serves for planning goal-directed behaviors.

These features are linked therefore to what is called now the Bayesian theory of the brain [START_REF] Tenenbaum | How to grow a mind statistics, structure, and abstraction[END_REF] and to the paradigm of predictive coding for cognition [START_REF] Friston | A free energy principle for the brain[END_REF]. These general theories describe how our expectations (as well as our errors) on sensory inputs are used as attention signals to adjust the prior expectations for the next events. Brain areas are hypothesized to use error prediction as a core information to control their dynamics from each others, not just for binding them mutually.

Under this framework, two or more brain networks can interact dynamically (eg Cortex CX with Basal Ganglia BG) so that we have always one network (eg the controller) that infers the reliability of another (eg the observer) with respect to a specific context. Along with Bayes theory, predictive coding has also its link with optimal control theory [START_REF] Jordan | Optimal feedback control as a theory of motor coordination[END_REF], which we think interesting in terms of perspective for modeling the corticostriatal system as it moves the problem of learning and retrieving memory sequences into a control problem.

Problem-solving tasks are good examples for understanding the involvement of the BG-PFC loops in goal-directed behaviors under uncertainty especially during infancy. These goal-directed behaviors are also called task-sets in cognitive and developmental sciences [START_REF] Harlow | The formation of learning sets[END_REF]. Task sets relate to the novel capabilities acquired by twelve-months-old babies such as tool-use, sustained attention, spatial memory, asymmetric imitation and rule-based learning and are argued to be linked to the maturation of the PFC [START_REF] Diamond | Development of the ability to use recall to guide action, as indicated by infants' performance on a-not-b[END_REF], [START_REF] Munakata | Computational cognitive neuroscience of early memory development[END_REF]. Other crucial examples during infancy are speech production and the sequential organization of actions [START_REF] Saffran | Statistical learning by 8-monthold infants[END_REF], [START_REF] Nazzi | Language discrimination by newborns: toward an understanding of the role of rhythm[END_REF], [START_REF] Marcus | Rule learning by seven-month-old infants[END_REF]. These two important cognitive tasks presumably involve the BG and PFC loops to adjust timely and orderly motor primitives [START_REF] Barone | Prefrontal cortex and spatial sequencing in macaque monkey[END_REF] and [START_REF] Botvinick | Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective[END_REF].

This neural process has been particularly studied for speech and language sequences because auditory modality is the sense especially sensitive to temporal structure. In the case of speech production, Romanski and colleagues propose that the phonotopical level requires the implementation of high-order models for encoding words or sentences as articulatory vocal tracks [START_REF] Romanski | Neural representation of vocalizations in the primate ventrolateral prefrontal cortex[END_REF]. In other experiments with 3 monthsold children [START_REF] Saffran | Statistical learning by 8-monthold infants[END_REF], [START_REF] Nazzi | Language discrimination by newborns: toward an understanding of the role of rhythm[END_REF], [START_REF] Marcus | Rule learning by seven-month-old infants[END_REF], the stronger activation of the PFC has been observed for detecting temporal dissonance in regular temporal structures of spoken sequences of totally random syllabes such as the ABA structure in "tomato" or "mifumi", independent of the syllabes pronounced for the A or B items [START_REF] Wacongne | Evidence for a hierarchy of predictions and prediction errors in human cortex[END_REF], [START_REF] Dehaene | The neural representation of sequences from transition probabilities to algebraic patterns and linguistic trees[END_REF].

A. Proposal framework for feature extraction and structure learning in sequences

In line with these finding, we propose a neural architecture to model the CX, BG and PFC systems that combines modelfree and model-based learning for retrieving and controlling long-range memory sequences hierarchically at the signal level and at the abstract level, see Figs. 1. The two working memories are developped within the same framework of predictive coding and reinforcement learning [START_REF] Friston | Predictive coding under the free-energy principle[END_REF] but each system is working differently to code information and to minimize online error and external noise. The models use spiking neural networks (SNN) in order to learn temporal delays between pre-and post-synaptic firing neurons with the mechanism of Spike Timing-Dependent Plasticity (STDP) [START_REF] Bi | Activity-induced synaptic modifications in hippocampal culture, dependence of spike timing, synaptic strength and cell type[END_REF]. In line with the framework of free-energy minimization [START_REF] Friston | A free energy principle for the brain[END_REF], we exploit also intrinsic noise within the system in order to realize stochastic descent gradient and novelty detection.

We propose that these neural mechanisms can serve for the learning of temporal delays between neurons in a selforganizing manner and makes possible the discovery of causes and effects necessary for active inference and predictive coding. This extends previous researches in which we developped several models of WMs using SNNs corresponding to different brain areas. These models exploited noise or novelty to iteratively infer a model and minimize error prediction either to control one system's dynamics (eg the hippocampus or BG-like model-free networks [START_REF] Pitti | Modeling the cholinergic innervation in the infant cortico-hippocampal system and its contribution to early memory development and attention[END_REF], [START_REF] Pitti | Iterative free-energy optimization for recurrent neural networks (inferno)[END_REF]) or to select dynamically a better controller (eg a PFC-like model-based network [START_REF] Pitti | Neural model for learning-to-learn of novel task sets in the motor domain[END_REF]).

In our BG-like network modeled in [START_REF] Pitti | Iterative free-energy optimization for recurrent neural networks (inferno)[END_REF] and [START_REF] Pitti | Feature and structural learning of memory sequences with recurrent and gated spiking neural networks using free-energy: application to speech perception and production i[END_REF], we showed that it is possible to control long-range memory sequences of spikes -, above 1000 iterations without loss,and to solve the so-called credit assignment problem by infering causes and effects, even with long-range delays. Because of its property to optimize and control dynamics iteratively using noise or free-energy, we named our network INFERNO, which stands for Iterative Free-Energy Optimization for Recurrent Neural Networks [START_REF] Pitti | Iterative free-energy optimization for recurrent neural networks (inferno)[END_REF].

Our framework will be applied to speech learning (perception and production). The global framework combines the corticostriatal and prefrontal systems for the recognition and generation of audio memory sequences, see Fig. 1. In the first part, the WM of the cortico-striatal (CX-BG) system is developped in order to better describe the process for retrieving audio primitives for a short time scale. The model named INFERNO network is then used to solve the credit assignment problem for retrieving the motor primitives that cause specific sound signal (vocal tracks). In principle, the motor primitives should be the vocal articulatory motions of the mouth and of the vocal cords, or the sound generated by a vocoder but in our case it is simply the sound vector reconstruction. In the second part, the combination with the prefrontal system (PFC) will be presented, with the use of Rank-Order neurons for learning the temporal organization within memory sequences and for predicting the next ones; the sensitivity of Rank-Order or gated neurons to the items' order within a sequence will serve for finding structure within signals. We named this gated version of the INFERNO network, INFERNO GATE and we have showed that it is possible to retrieve long range sequences through iterative optimization for long time scale. More detailed explanations about these two models are presented in extented papers [START_REF] Pitti | Feature and structural learning of memory sequences with recurrent and gated spiking neural networks using free-energy: application to speech perception and production i[END_REF], [START_REF] Pitti | Gated spiking neural network using iterative free-energy optimization and rank-order coding for structure learning in memory sequences (inferno gate)[END_REF].

B. Neural model for the cortico-striatal system and the fronto-basal system

In our comprehension of the free-energy optimization strategy proposed by Friston [START_REF] Friston | Reinforcement learning or active inference?[END_REF], [START_REF] Friston | The free-energy principle a rough guide to the brain?[END_REF], [START_REF] Friston | Predictive coding under the free-energy principle[END_REF], it is similar to a reinforcement learning process without the need of value-functions to minimize online error prediction. To us, it conveys the learning problem into the ones of optimal control and predictive coding. We can apprehend the cortico-striatal system and the fronto-basal system as two learning systems that attempt to perform an optimal control and resolve error prediction among their dynamics. In Fig. 2, we display the first network, INFERNO, with the Primary Auditory Cortex (PAC) system and the Superior Temporal Gyrus (STG) layer modeled with SNNs to encode incoming inputs, the Striatum layer that categorizes the state of the STG dynamics and the Globus Pallidus that attempt to control back the input dynamics of the PAC and STG with a reentrant loop. The error prediction is evaluated and minimized over time by supervision of the STR units (critic) and by noise generation and stochastic search done on the GP output layer (actor).

This paper is organised as follows. Two experimental setups for sound sequences are presented in section IV, respectively for a limited learning database (only one speaker, 3 minutes length) and for a larger database (several speakers of different genders, 30 minutes length). The results of these two experiments are developped and discussed in section V.

III. METHODS

The neural architecture INFERNO [START_REF] Pitti | Iterative free-energy optimization for recurrent neural networks (inferno)[END_REF] consists of two coupled learning systems arranged. The first network corresponds to one recurrent neural network of spiking neurons (SNNs) and the second network consists on one associative map. The SNN implements a forward model of the incoming signals whereas the associative map implements an inverse In order to minimize error, the second network generates intrinsic noise I noise to control the dynamics of the first one following a RL mechanism. The activity of the SNN Y out is compared to one desired goal vector Y des to compute the error E between Y des and Y out and the current input is kept for the next step I(t + 1) = I(t) + I noise , if and only if it diminishes the gradient ∆E. Over time, I converges to I opt its optimum value, and Y out converges to Y des the desired vector. This scheme is in line with actor-critic algorithms and predictive coding. Its organization is similar to novel architectures combining two or more competitive neural networks such as auto-encoders or the generative adversarial networks.

We showed in [START_REF] Pitti | Iterative free-energy optimization for recurrent neural networks (inferno)[END_REF] that this variational process is similar to a stochastic descent gradient algorithm performed iteratively and can solve the temporal credit assignment problem for delays above dizains of iterations. For instance, the convergence to the desired goal after a certain delay can be viewed as the retrieval of a memory sequence for such duration. Furthermore, the free-energy minimization is generative in the sense that it can retrieve novel solutions I for the same output Y . This can be viewed as a synchronization process toward attractor memories [START_REF] Tsuda | Chaotic itinerancy and its roles in cognitive neurodynamics[END_REF].

IV. RESULTS

A. Experiment 1 -self-supervised vocal babbling

In this experiment, we make to learn the Primary Auditory Cortex (PAC), STG and Striatum layers in an unsupervised manner so that the three structures self-organize to sparse distributions using Hebb law for the PAC and the Striatum whereas the STL learns the temporal dependencies across time using the STDP learning mechanism; the direction of the information flow is PAC→STG→STR.

The experimental setup for Experiment 1 in section IV-A consists on a small audio dataset of 2 minutes length of a native french woman speaker repeating three times five sentences. The audio .wav file is translated into MFCC vectors (dimension 12) sampled at 25ms each and tested either with a stride of 10ms and no stride. The whole sequence represents 14.000 MFCC vectors for the case with strides and 10.000 MFCC vectors for the case with no strides. The number of Striatal and GP units are chosen so that they correspond to the number of MFCC vectors, which means 14000 units (10.000 units) for each layer. We do so in order to test the reliability of our architecture to retrieve input data with an orthogonal representation. The compression rate is however low (1:1).

The learning stage of the MFCC in the different layers is as follows. While the PAC first receives at each iteration the MFCC vectors, the STG integrates with a temporal horizon of 20 iterations the different dynamics. Then, the third layer, the Striatum, categorizes the current state of the STG network in a higher dimension. In so far, the learning stage is feedforward from PAC→STG→STR and the categorization is done in an unsupervised manner.

Once several passes are done over the complete audio sequence, the neurons stabilize to certain representations. It is possible then to perform an active exploration stage in the other direction -which means STR→GP→PAC→STG→STR for retrieving the corresponding audio entries in GP through reinforcement learning.

This stage corresponds to a motor babbling in which the audio inputs are generated in GP and evaluated after a delay in STR. The prediction error in STR is used to drive the dynamics in GP using free-energy and to control the PAC layer and STG dynamics via an iterative optimization process. Over time, each audio vector is reinforced for each GP-Striatal pair whenever the GP auditory pattern makes to fire its corresponding Striatal unit. The audio pattern converges to an optimal MFCC vector for which the Striatal unit was

Vocal babbling

Free Energy Optimisation Fig. 3. Free-energy optimization. error minimization of one Striatal unit (top chart) using noise to retrieve GP vectors for which the Striatal units fire maximally (middle chart). The STG unit displays different spike trains for which a solution is found (bottom charts). The dashed lines correspond to a reset of the GP dynamics in order to show that the minimization process is always present and that different solutions can be retrieved dynamically. the most active. As proposed by several neuroscientists, the GP layer may control indirectly the Striatal layer through the cortical dynamics [START_REF] Graybiel | The basal ganglia and chunking of action repertoires[END_REF], [START_REF] Doya | Metalearning, neuromodulation, and emotion[END_REF], [START_REF] Friston | Predictive coding under the free-energy principle[END_REF]. The prediction error may drive the amount of noise within the system and the ratio between exploration and exploitation. This scheme corresponds to a predictive coding mechanism, which can solve the temporal credit assignment problem between causes (in GP) and delayed effects (in STG).

We display in Fig. 3 one example of retrieved GP dynamics (middle chart) for which the prediction error in Striatum is diminished over time (top chart) with respect to the spatio-temporal patterns of the STG layer (bottom chart). The dashed line corresponds to a reset performed on the GP dynamics in order to observe dynamically the error minimization mechanism at work. The sample corresponds to the optimization process for one Striatal unit and for one GP vector. During the free-energy descent gradient, the GP vector converges to one audio pattern for which the STG activity is the most recognized by the corresponding Striatal unit. As showed in the graphs, the optimization process does not necessarily converge to the same minima after the reset done on the GP vector but can be stacked to another one. This means that different patterns of activity in the GP layer can influence in a similar way the activity in the STG layer. Therefore, the categorization done in STR is not perfectly orthogonal (sparse) and different solutions coexist to retrieve the STG spatio-temporal dynamics.

When reconstituting the .wav file in Fig. 4 from the retrieved MFCC vectors, we can observe a gradual refining of the audio waveform from the four periods with respect to the ground truth displayed at the bottom chart. The sequence is showed for 11 seconds although the global test was performed over two minutes length of the audio database.

After four exposures of the neural architecture to the audio sequence, the retrieved signals are gradually converging to the correct waveform. At period #0, the waveform is very discrete with square-like pattern and the amplitude and the wavelength are not respected. Gradually from period #1 to #3, we can observe a refinement of the waveform matching the ground truth curve1 .

B. Experiment 2 -structure learning and serial recall of audio sequences

This section explains the processing done in the network INFERNO Gate in order to learn the temporal structure in sequences and to retrieve back original sequences from incomplete information.

The iterative optimization process is done at the dlPFC level for retrieving memory sequences at the OFC level with the error rate computed at the ACC. The information flow corresponds to dlPFC→OFC→Broca Area (B45)→lPFC→dlPFC, see Fig. 2 b).

In order to understand better the global process, we display in Fig. 5, the final retrieved sequence in OFC with respect to the one represented by the dlPFC units is displayed in a), the raster plot of the iterative search of exact sequences in OFC and Broca Area (B45) is showed resp. b). The exploration search is performed after the learning stage. Over time, a sequence in OFC is explored iteratively using noise so that the dlPFC activity is maximal activity level and that ACC reaches a minimal value.

We display in Fig 5 a) in red, the retrieved OFC sequence in the top chart and the serial order for which the two different dlPFC neurons are the most sensitive to in the bottom chart.

In the top chart, we can observe that the reconstructed OFC sequences in red follow a similar pattern to the ones in blue although the identity of the neurons is not completely preserved. Nonetheless, we can see that the ordinal information in the bottom chart is matched, which means that the proposed sequence in the top chart follows the temporal pattern encoded in the lPFC and in the dlPFC layers.

Hence, despite the indices in the sequence have been lost in the encoding process, the system is capable to retrieve the memory sequences from incomplete information (due to compressive rank) with small error.

In order to analyze the accuracy of the Inferno Gate network, we plot in Fig 6 the euclidean error normalized between [0, 1] made by the network during recall with respect to the number of items given as input vector, resp. b). The exploration stage was limited to 10.000 iterations for each experiment and we plot the retrieved sequences for 20 items given out of 50 items to retrieve, resp. in Fig. 6 b). The grey areas indicate the part of sequence given to the system to restitute the missing part.

In Fig. 6 a), the error rate computed from goal sequence encoded in lPFC/dlPFC and retrieved sequences in the OFC layer with respect to the amount of items given from 0 to 80% of the sequence given at the B45 level. The more the number of units to search are few, the more accurate is the recall. If we provide 40% of the items of the sequence we want to retrieve, the error on the neurons id is particularly small and almost error free if 80% of the neurons are given. In b), serial recall in OFC layer from incomplete information. Retrieved goal sequence when 40%, information are furnished to the system. In the top charts, the generated sequences in OFC layer with identity fo the STR neurons are displayed in red with the goal sequences to retrieve in blue. The more information is given to the system, the easier is the explorative search to retrieve the missing units identity. In the bottom chart, although the rank order in the temporal patterns of the units in lPFC is respected, this does not warranty that the units identity is retrieved correctly in the OFC sequences.

V. DISCUSSION

We have presented the neural architecture INFERNO based on free-energy minimization using recurrent spiking neural networks for modeling the CX-BG loop [START_REF] Pitti | Iterative free-energy optimization for recurrent neural networks (inferno)[END_REF]. This neural architecture is used for learning temporal sequences and for retrieving vocal 'motor' primitives by evaluating sensory feedback [START_REF] Pitti | Feature and structural learning of memory sequences with recurrent and gated spiking neural networks using free-energy: application to speech perception and production i[END_REF], although in our case, we did not model the vocal articulatory system for sound generation but regenerated the sound primitives instead. We will model this part in further works.

The INFERNO network has two features, namely generalization and robustness to temporal delays. On the one hand, the number of units in the Striatum layer imposes a dimensionality reduction depending on the number of sound primitives to be learned (eg the number of MFCC vectors). On the other hand, the temporal chains in the CX layer permits to solve the temporal credit assignment problem and to link causes and effects thanks to STDP.

In the first experiments we have designed the network with the same number of STR units as MFCCs to retrieve (14.000 units) in order to have an orthogonal representation with few overlapping. The exploration of the audio primitives in a self-organized manner is similar to a vocal babbling, testing different sounds till convergence to the correct ones.

In second part, we have presented a novel neural architecture for modeling the fronto-striatal (PFC-BG) loop and learning temporal sequences. This network, named Inferno Gate [START_REF] Pitti | Gated spiking neural network using iterative free-energy optimization and rank-order coding for structure learning in memory sequences (inferno gate)[END_REF], extends our original neural architecture Inferno presented earlier. Here, we have showed its effectiveness in the more challenging tasks of speech recognition and production based on structure learning. Although the two networks are similar in their functional organization, the encoding type is different. The first network uses the STDP mechanism for learning temporal correlations between spiking events whereas the second one uses a gating mechanism for binding the item's rank and their position within a sequence.

By discriminating content (which sound) and contextual information (when to play it in the sequence), we have showed that the two networks are capable to robustly learn the temporal structure within sequences and to retrieve the items identity in the correct order. In the first experiment, we did not compare the results of the network Inferno with standard RNN methods such as LSTM, as with 5% error reconstruction it performed advantageously. We will do the comparison in future research. In the second experiment, we compare the performances of the LSTM network with Inferno Gate in [START_REF] Pitti | Gated spiking neural network using iterative free-energy optimization and rank-order coding for structure learning in memory sequences (inferno gate)[END_REF] and showed that our network outperforms the LSTM. The rationale is that by strictly separating the structure to learn and items to locate, the problem becomes easier even with a high number of items and with longer sequences.

In future researches, we envision to extend our framework to social and developmental robotics with speech generation and with visual and motor integration [START_REF] Pitti | Gain-field modulation mechanism in multimodal networks for spatial perception[END_REF], [START_REF] Dermy | Developmental learning of audio-visual integration from facial gestures of a social robot[END_REF], [START_REF] Valentin | Robot recognizing vowels in a multimodal way[END_REF].

Fig. 1 .Fig. 2 .

 12 Fig. 1. Framework for sequence learning based on iterative optimization. Cortico-basal (CX-BG) and Fronto-striatal (PFC-BG) loops.Cortico-basal loop (Inferno network)Fronto-striatal loop (Inferno Gate network)

Fig. 4 .

 4 Fig. 4. Waveform reconstruction for the four learning periods.

  The desired sequences we want to reconstruct in OFC are presented at Fig 5 a) in the top chart. The raster plot of the reconstructed OFC/B45 dynamics are plotted in Fig 5 b).

Fig. 5 .

 5 Fig.5. Free-energy optimization for retrieving sequence in OFC layer. In a), the temporal pattern retrieved in lPFC/dlPFC layers and the temporal pattern of the goal sequence in blue are displayed in the bottom chart. In c), the OFC layer achieves to retrieve with some uncertainty a temporal sequence but the global structure of the sequence and the rank orders are mostly respected.

Fig. 6 .

 6 Fig. 6. Performance analysis of the Inferno gate architecture for retrieving sequences with respect to the amount of information given.

We provide the link of the different .wav files at https:// promethe.u-cergy.fr/alexpitt/inferno.

ACKNOWLEDGEMENTS

We would like to thank Mathieu Lagrange (LS2N, UMR 6004 CNS, Ecole Centrale de Nantes) for providing the audio database. This work was partially funded by EQUIPEX-ROBOTEX (CNRS), chaire dexcellence CNRS-UCP and project Labex MME-DII (ANR11-LBX-0023-01).


Jessica B Baroga-Barbecho

Sheryl A Yap
email: sayap3@up.edu.ph

Ming Kai Tan

Tony Robillard
email: tony.robillard@mnhn.fr

P Leytensis Robillard

P Septentria Baroga

Yap & Robillard

Bernice P Bishop

Taxonomic review of the genus Paranisitra Chopard (Orthoptera: Gryllidae: Eneopterinae: Nisitrini) with description of a new species from Mindanao

Keywords: checklist, key, new record, new species, Paranisitra, taxonomy General morphology: Tarsomere III-1, III-3: first and third segments of hind leg tarsomere. Measurements: FIIIL, length of hind femora, FIIIW, width of hind femora, OL,

The genus Paranisitra Chopard (Orthoptera: Gryllidae

Introduction

The Philippines is composed of more than 7,100 islands and it is considered as one of the mega biodiversity hotspots in the world because of its high levels of species richness and variation among adjacent regions [START_REF] Brown | Philippines, biology. Encyclopedia of Islands[END_REF]. Although the fauna of every island in the country is unique, only few taxonomists have been able to study all of them in detail [START_REF] Tan | Trends in new species discovery of Orthoptera (Insecta) from Southeast Asia[END_REF]). Most of the faunistic studies being done were concentrated in Luzon Island, and only limited works have been done in Mindanao Island, particularly on crickets [START_REF] Tan | Trends in new species discovery of Orthoptera (Insecta) from Southeast Asia[END_REF].

The genus Paranisitra Chopard, 1925 includes distinct species of wingless, long-legged crickets that can be found dwelling on low vegetation during daytime, near or within rainforest. In addition to the complete absence of wings in both sexes, the genus is also characterized by its very short abdomen and cerci, and head shape with narrow fastigium, large prominent eyes and protruding rounded ocelli (Gorochov 2009;Robillard & Yap 2015; [START_REF] Baroga | Two new species of Eneopterinae crickets (Orthoptera: Gryllidae) from Luzon, Philippines[END_REF]. Species under this genus are most often yellow brown, with specific black and yellow patterns.

The genus was established by Chopard (1925) for the species P. longipes Chopard, 1925 from the Philippines (Luzon Island: Mount Makiling). It remained monotypic until Gorochov (2009) described P. diluta Gorochov, 2009 and[START_REF] Gorochov | Philippines: Mindoro Is., Northern Coast, Environs Puerto Galera (TL) ZIN[END_REF] from Indonesia (Supiori Island, north of New Guinea Island) and the Philippines (Mindoro Island) respectively. More recently, P. leytensis Robillard, 2015 was described from Leyte Island (Robillard & Yap 2015), and P. septentria Baroga, Yap & Robillard, 2016 from Luzon Island (Mt. Pau) [START_REF] Baroga | Two new species of Eneopterinae crickets (Orthoptera: Gryllidae) from Luzon, Philippines[END_REF], both from the Philippines.

In this paper, we describe the new species of Paranisitra flavofacia n. sp. from Mindanao Island and Siargao Island and provide new records and additional descriptions for P. longipes Chopard, 1925, P. leytensis Robillard, 2015, andP. septentria Baroga, Yap & Robillard, 2016. A complete checklist, distribution map, and key to Paranisitra species are also provided. -Absence of dark longitudinal band on hind femora; male genitalia with short and rounded pseudepiphallic lophi, wider than long; endophallic sclerite with lateral arms curved posteriorly and distinctly hooked at apex .. ................................. P. diluta Gorochov, 2009 Family Gryllidae Coloration of adults dominantly yellow brown with specific black and yellow patterns variable between species. Head shape with narrow fastigium, antenna very long, large prominent eyes and protruding rounded ocelli. Pronotum not carinated; its lateral angles rounded, posterior edge slightly concave, lateral lobes almost triangular. Wings completely absent in both sexes. Legs long and thin. Fore tibia with two tympana, inner one slit-like, covered by a swollen cuticular expansion; outer tympanum oval, its membrane transversally plicate in dorsal half. Fore tibia with three apical spurs, two inner and one outer. Median tibia with four apical spurs, inner longest. Hind tibia with three inner and three outer apical spurs, median longest on each side; four inner subapical spurs and only three outer subapical spurs (four in other eneopterine genera), inner and outer spurs almost straight, their apex hook-like. Hind tibia serrulated on whole length, dorsal face slightly furrowed. Tarsomeres III-1 with two rows of spines on external edge (one dorsal and one more lateral) in addition to apical spines. Apical claws of legs slightly indented. Abdomen and cerci short. Male genitalia membranous and setose; pseudepiphallic lophi short but well individualized. Female ovipositor apex thin and not denticulate; copulatory papilla conical to diamond-shaped, with a basal sclerotized ring. Chopard, 1925 (Fig. 1) Paranisitra longipes Chopard, 1925: 318 (original Diagnosis. General shape slender, body not spotted (unlike in P. maculata); coloration usually yellow brown with more or less black and yellow patterns. Face with a wide yellow transverse band as in P. leytensis and P. maculata; differing from P. leytensis by larger size and male genitalia with pseudepiphallic lophi wider and rounded, and from P. maculata by less spotted body coloration. Gorochov, 2009 (Fig. 1) Paranisitra maculata Gorochov, 2009: 436;[START_REF] Baroga | Two new species of Eneopterinae crickets (Orthoptera: Gryllidae) from Luzon, Philippines[END_REF]: 94 (taxonomic key); [START_REF] Vicente | In and out of the Neotropics: historical biogeography of Eneopterinae crickets[END_REF]Vicente et al. : 2203 (biogeography) (biogeography).

Subfamily

Paranisitra longipes

Paranisitra maculata

Type material. Holotype male. Philippines. Mindoro Island, northern coast, environs of Puerto Galera town, 11-13.iv.2004, coll. A. Gorochov (ZIN) [examined]. Paratypes.

Philippines. Mindoro I., env. de Puerto Galera (N. coast), 1♂, 11.iii..2004-13.iii.2004 Diagnosis. Species similar to P. longipes for having the yellow transverse band in the face, differing by larger size and body heavily spotted; male genitalia with lateral endophallic sclerites rather wide, and almost not hooked at apex. Gorochov, 2009 (Fig. 1) Gorochov, 2009: 438. Vicente et al. 2017: 2203 (biogeography).

Paranisitra diluta

Paranisitra diluta
Type material. Holotype male. Indonesia. Supiori Island (not far from northern edge of western New Guinea), southern coast, environs of Korido village, 10-11 November 2004, coll. A. Gorochov [not examined]. Paratypes. Indonesia. Southern coast of Supiori I. (not far from N. coast of W. New Guinea), on hills near vill. Korido, primary forest, 10.xi.200410.xi. -11.xi.2004, Gorochov A., 1♂, molecular , Gorochov A., 1♂, molecular Diagnosis. Species close to P. longipes in terms of face coloration with a wide yellow band; differing by its smaller size and male genitalia with smaller and triangular pseudepiphallic lophi, shorter endophallic sclerite, and narrower endophallic apodemes.

Remarks. The male specimens from Siargao Island and Mindanao Island (Agusan del Sur) were identified as P. leytensis based on the features of the body, face, size and male genitalia. They slightly differ from the specimens from Leyte by the following characters: in Siargao specimens, male genitalia wider than long, short and less setaceous lophi, and short rami; in Agusan specimen, thin and long male genitalia, ectophallic fold longer and narrowing basally. Male specimen from Mindanao Island is the smallest among examined specimens of the species. Diagnosis. Species differing from other species of the genus by face and mouthparts entirely dark brown. Male genitalia with membranous lophi and ectophallic fold with strong leaf-like lateral sclerites, while rounded in P. leytensis. Female copulatory papilla elongate and with lateral striations.

Paranisitra septentria

Addition to the description. Female (Fig. 2a-b). In addition to the general morphology and characters of male presented in [START_REF] Baroga | Two new species of Eneopterinae crickets (Orthoptera: Gryllidae) from Luzon, Philippines[END_REF]. Size larger than male.

Coloration brown to dark brown. Vertex black, with brown patterns. Fastigium brown with three long apical setae; ocelli brown, protruding and outlined with black lining.

Scapes yellow, antennae brown with white rings. Face entirely black; frons with two yellow spots. Maxillary palpi white. Ovipositor slightly shorter than hind femur.

Copulatory papilla (Fig. 2c-d) elongate, longer than P. longipes and P. leytensis, with a narrowed basal sclerotized ring; its lateral margins with some striations; apex bisunated. 1. Etymology. The name of the species comes from the combination of the Latin words "flavo" meaning yellow and "faciem" meaning face.

Measurements in mm. see Table

Diagnosis. Species of average size, characterized as in P. diluta by its entirely yellow face, without transverse band; differing from P. diluta by male genitalia with pseudepiphallic lophi longer than wide and forming a triangle (shorter and rounded in P. diluta), and by lateral arms of endophallic sclerite curved anteriorly.

Description. Size average for the genus (Fig. 3). Coloration yellow to golden brown.

Head vertex (Fig. 4a) black, with some yellow to brown patterns, covered with whitish setae. Fastigium black, area posterior to ocelli brown; ocelli rounded, protruding and outlined with black. Scapes and antennae yellow to dark brown with whitish rings widely spaced out. Entire face and mouthparts yellow, without transverse band (Fig. 4b); maxillary palpi whitish to yellowsh. Frons with a dark brown marking. Head lateral side yellowish (Fig. 4c), area below eyes brown. Dorsal disc of pronotum brown, with dark brown markings. Lateral lobes of pronotum dark brown, covered with short white setae; ventral margin golden brown. Legs brown to dark brown. Fore-and median femora orange brown, covered with dark setae. Hind femora yellowish basally, darker apically, with white markings and longitudinal bands on external face; knees dark brown. Tibiae dark brown, lighter basally. Tarsomeres brown apically, lighter basally. Tarsomeres III-1 with 3-10 spines on dorso-external edge. Abdomen orange brown to golden brown, mottled with black markings. Cerci homogenously dark brown, without markings and rings, covered with hairs.

Male. Male genitalia (Fig. 5a-c). Pseudepiphallus wide and slightly conical, its anterior margin slightly indented. Lophi membranous, thin and elongated, forming short triangles slightly indented laterally. Rami long, curved and converging apically. Pseudepiphallic parameres narrow, fused to pseudepiphallus dorsally, with strong rounded lateral sclerites, narrowing apically. Ectophallic apodemes long, reaching half of size of rami, parallel. Endophallic sclerite sclerotized, its posterior apex with long divergent lateral arms and with a short triangular median expansion. Endophallic apodeme including wide and thick lateral lamellas, and a long strongly sclerotized dorsal crest.

Female. Habitus similar to males (Fig. 3), but body coloration (including fastigium vertex, legs, and abdomen) tends to be less dark. 3 rd abdominal tergite with dark median patterns. Ovipositor shorter than hind femora, apex black, fainter apically, its apex smooth and pointed. Copulatory papilla (Fig. 5d-e) wider than long laterally and longer than wide dorsally, with a basal sclerotized ring; apex slightly bent.

Juveniles. Coloration pattern similar to adults, except face, back with a vertical yellow stripe.

Measurements in mm. see Table 2.

Remarks. We observed minor difference in the features of the face of materials from Siargao. Compared with the type material, Siargao specimens are 1.2 times smaller and exhibit small differences in male genitalia, which are not sufficient to support description of a separate species. More information from other localities in Mindanao Island and near Siargao Island and molecular data will be necessary to test whether there are several closely related species. In addition, in both Mindanao and Siargao islands specimens, a few females are strikingly darker than the rest of the series, showing a face almost entirely black and wide black longitudinal band on FIII. However, despite this difference of melanization, the light/dark patterns are more or less similar.

Habitat and life history traits. Materials from Siargao were collected in an agroforestry area dominated with coconut trees. Courting and mating were observed on top of vegetation both in field and secondary herbaceous areas during night time (Fig. 7).

Discussion

The updated checklist of Paranisitra species is presented in Table 3. Today, a total of six species of Paranisitra are recorded, including P. flavofacia n. sp. described here. Five of these species are from the Philippines: three from island group of Luzon, one from Visayas, and one from Mindanao, and only one is distributed in the north of New Guinea Island (Indonesia: Supiori Island).

Most species of the genus have been discovered recently from islands where the orthopteran fauna has been little studied. In such regions, even common and abundant species of crickets often remain undescribed [START_REF] Robillard | Centuriarus, a new genus of Eneopterinae crickets from Papua (Insecta, Orthoptera, Grylloidea)[END_REF][START_REF] Vicente | Gnominthus gen. nov., a new genus of crickets endemic to Papua New with novel acoustic and behavioral diversity (Insecta, Orthoptera, Gryllidae, Eneopterinae)[END_REF][START_REF] Tan | A new species of Landrevinae (Orthoptera) from the Philippines, with notes on Endodrelanva and Endolandrevus[END_REF]. For example, P. flavofacia n. sp. seems relatively well distributed in the large island of Mindanao. It is thus very likely that other species of Paranisitra will be discovered during future field surveys in undisturbed areas in Luzon and Mindanao, or in islands from where no material of the genus exists yet in the collections, such as in Palawan, Batanes, large islands of the Visayas (i.e., Negros, Romblon, and Samar) and other parts of Mindanao (i.e., Davao, Sulu, and Basilan).

The taxonomy of Paranisitra still needs clarification. Owing to the fact that the genus is apterous and does not produce songs, we cannot infer species boundaries using wing venation and acoustics. With some species having overlapping distributions, there is a need to examine intraspecific variations more closely to ascertain whether some of the color patterns are indeed diagnostic of the species or merely geographical differences. For example, we observed how nymphs can exhibit variable colorations different from the adults (Fig. 8). Moreover, we should also account for the possibility of introgression among sympatric species to have a better understanding of Paranisistra.

A recent study of molecular phylogeny and historical biogeography about the subfamily Eneopterinae included three species of Paranisistra [START_REF] Vicente | In and out of the Neotropics: historical biogeography of Eneopterinae crickets[END_REF]. The results suggested that Paranisitra could have originated in South-east Asia from a divergence with its sister genus Nisitrus ca. 55 Ma; then it may have diversified after 12.5 Myrs in the Philippines, before colonizing more recently the north-west of New Guinea (ca. 8.5

Ma).

Interestingly, several field expeditions in Palawan Island failed collecting any specimen of Paranisistra (A. Gorochov, pers. obs. & TR, SAY, JBB-B, pers. obs). Although it cannot be considered as a proof of absence, this lack of record despite active search suggests that the genus is not distributed in Palawan. This could be related to the geological history of the region where a landbridge may have existed between the Borneo and Palawan during the late middle Pleistocene [START_REF] Heaney | Biogeography of mammals in SE Asia: estimates of rates of colonization, extinction and speciation[END_REF][START_REF] Hall | Cenozoic geological and plate tectonic evolution of SE Asia and the SW Pacific: computer-based reconstructions, model and animations[END_REF]. This is particularly relevant to understand the biogeographic history of the tribe Nisitrini, since Palawan is located between the areas of distributions of Paranisitra (mostly in Philippines) and Nisitrus (Borneo, Sumatra, and the Malay Peninsula). At the estimated time of divergence between Paranisitra and Nisitrus ca. 55 Ma, the region was drastically different from today [START_REF] Hall | Cenozoic geological and plate tectonic evolution of SE Asia and the SW Pacific: computer-based reconstructions, model and animations[END_REF]. In particular, Palawan was still related to the Sunda Shelf. It is only recently that this island moved in its current distribution, while the Nisitrini were already diversified. Phylogenetic and biogeographic studies involving a more complete sampling of the Nisitrini will be necessary to analyze more precisely the detailed routes of colonization by this clade in this region of Southeast Asia.

Apart from these biogeographical questions, Paranisitra is relevant to address questions related to the evolution of communication systems in crickets. This genus became mute as a consequence of the complete loss of its wings [START_REF] Robillard | [END_REF]. The causes and circumstances of this loss of acoustic communication represent a fascinating topic for future research. It could involve comparisons between Nisitrus and Paranisitra, to assess if they share other modalities of communication (i.e., visual, vibrational) and what aspects of their ecology and behavior could explain the loss of the wings and acoustic communication in Paranisitra.

Tables:

Table 1. Measurements of female Paranisitra septentria. Abbreviations, see materials and methods.

  1. Face with white or yellow transverse band ........................................................................ 2 -Face without white or yellow transverse band ................................................................... 4 2. Body spotted; male genitalia with lateral endophallic sclerites and almost not hooked at apex ............................................................................................... P. maculata Gorochov, 2009 -Body not spotted ................................................................................................................ 3 3. Body coloration usually yellow brown with black and yellow patterns; male genitalia with rounded and wide pseudepiphallic lophi ................................... P. longipes Chopard, 1925 -Body coloration lighter than other species; male genitalia with smaller and triangular pseudepiphallic lophi ............................................................... P. leytensis Robillard, 2015 4. Face entirely black; male genitalia with membranous lophi and ectophallic fold have strong leaf-like lateral sclerites .............................. P. septentria Baroga, Yap, & Robillard, 2016 -Face entirely yellow ........................................................................................................... 5 5. Presence of dark longitudinal band on hind femora; male genitalia pseudepiphallic lophi longer than wide, endophallic sclerite with lateral arms curved anteriorly ......................... ............................................................................................................... P. flavofacia n. sp.

  Paranisitra longipes Chopard, 1925, by original monotypy Emended diagnosis from Robillard & Yap (2015). Size average for the subfamily.

(

  JBB194, JBB183, JBB190), 24.v.2014, coll. N.M. Barbecho (UPLBMNH ORT-00819-00821); night, 1♀ (JBB195), 7♂ (JBB188, JBB184, JBB187, no code, JBB191, JBB185, JBB193), 26.v.2014, coll. N.M. Barbecho (UPLBMNH ORT-00863-00867, 00872-00874); 1♂ (JBB186), 1♂ juvenile (JBB199), 27.v.2014, coll. N.M. Barbecho (UPLBMNH ORT-00890-00891). Majayjay, 1♂ juvenile (JBB235), 01.iii.2009, coll. N.R. Barnacha (IWEP Taxo Lab-02982). [Quezon Province]: [Real], NBG [National Botanic Garden]: 10.ii.1985 1♂ (JBB278), coll. C.L. Dayanon (UPLBMNH ORT-01121), 1♂ (JBB234), 15.ii.1985 (IWEP Taxo Lab-02990); 2♂ (JBB276-277), 18.ii.1984, coll. N. Hasan (UPLBMNH ORT-01119-01120); 2000 ft., 1♂ (JBB279), 2.xii.1976, C.R. Baltazar (UPLBMNH ORT-01122); grasses, 1♂ (JBB280), 22.iii.1976, P.L. Bugayong (UPLBMNH ORT-01123). Polillo Island: 1♂ (JBB232), 17.iii.2009, coll. R.E. Bayot (IWEP Taxo Lab-02988); 1♀ (JBB241), 08.iv.2016, coll. P.C.T. Salazar (IWEP Taxo Lab-30234), Pinaglubayan: 2♂ (JBB031-032), 4-6.vi.2014, coll. J.B. Baroga, F. Signabon & S.A. Yap (UPLBMNH). Real, UPLB Land Grant, 1♀ (JBB364), 1♂ (JBB365), 20-24.i.2016, coll. K.O. Abenis (UPLBMNH). [Camarines Sur]: Mt. Iriga, 500-600 m: 1♂ (JBB367), 10.iv.1962, coll. H.M. Torrevillas (BPBM); 1♂ (JBB369), 19.iv.1962, coll. H.M. Torrevillas (BPBM); 1♂ (JBB370), 23.iv.1962, coll. H.M. Torrevillas (BPBM). Mt. Isarog: 20 km of Naga, 500-600 m, light trap, 1♂ (JBB371), 6.iv.1963, coll. H.M. Torrevillas, (BPBM); 500-600 m, 1♂ (JBB373), 11.iv.1963, coll. H.M. Torrevillas (BPBM); 750-900 m, 2♂ (JBB377-378), 4-7.iv.1963, coll. H.M. Torrevillas (BPBM); 750-850 m, 1♂ (JBB379), 8-12.v.1963, coll. H.M. Torrevillas (BPBM); 750-850 m, 1♂ (JBB380), 15-17.v.1963, coll. H.M. Torrevillas (BPBM); 800 m, light trap, 1♂ (JBB383), 28.iv.1965, coll. H.M. Torrevillas (BPBM). [Albay]: Mt. Malinao: 1♂ (JBB025), 1♀ (JBB175), 1-3.iii.2014, coll. J.B. Baroga (UPLBMNH); 1♂ (JBB007), 24-25.x.2013, coll. S.A. Yap (UPLBMNH); 1♂ (JBB233), 24.x.2013, coll. A.K. Amarga & J.M. Briz (IWEP Taxo Lab-02983); no data, 1♂ (JBB284) (UPLBMNH ORT-01127). Type locality. Philippines, Luzon Island, Laguna, Mount Makiling. Distribution. Philippines, Luzon Island: Laguna Province: Mt. Makiling, Makiling Botanic Garden, Dampalit Falls (new record), Majayjay (new record), Cavinti (new record). Quezon Province: Real (new record), Polillo Island (new record). Camarines Sur Province: Mt. Iriga [formerly known as Mt. Asog] (new record); Mt. Isarog (new record); Albay Province: Mt. Malinao (new record).

  , Gorochov A. (MNHN-EO-ENSIF3682), 1♀, , molecular sample N8Pma (MNHN-EO-ENSIF3683) [examined]. 8♂, 3♀, same information (ZIN) [examined] Other material examined. [Philippines]. Mindora [Mindoro], coll. Br. v. W., 19.024, 1♀, (MNHN-ENSIF646); Philippinen [Philippines], Mindoro, 1♀, (MNHN-ENSIF647). Type locality. Philippines. Luzon, Mindoro Island, northern coast, environs of Puerto Galera. Distribution. Philippines: Mindoro Island.

  sample N16Pdi2 (MNHN ENSIF3681)[examined]. Indonesia. Northern coast of Biak I. (not far from N. coast of W. New Guinea), on hills near vill. Corem[Korim], primary forest,1♀, 14.xi.20041♀, 14.xi. - 15.xi.2004, Gorochov, A., molecular , Gorochov, A., molecular sample N9Pdi (MNHN ENSIF3680) [examined]. 5♂, 5♀, same information (ZIN) [examined]. Type locality. Indonesia. Papua Province, Supiori Island, environs of Korido village. Distribution. Indonesia: Supiori Island and Biak Island. Diagnosis. Size average, body less spotted than P. longipes and P. maculata and without dark longitudinal band on hind femora. Face almost completely yellow. Absence of large dark spots and bands on dorsal disc and lateral lobes of pronotum. Male genitalia with short and rounded pseudepiphallic lophi; lateral arms of endophallic sclerite narrower than in P. maculata. Female ovipositor shorter than in P. longipes. Paranisitra leytensis Robillard, 2015 (Fig. 1) Paranisitra leytensis Robillard & Yap, 2015: 85; Baroga et al. 2016: 94 (taxonomic key). Type material. Holotype male. Philippines. [Visayas]: Leyte [Island], Burauen, Barangay Villa Corazon, zone herbacée [herbaceous area] (GPS Bar1), 10°57'50.5"N 124°46' 35.3"E, 282 m (TR14), iii.2013, coll. T. Robillard (UPLBMNH). Allotype female. Philippines. [Visayas]: Leyte [Island]: Buo, zone secondaire près de la route [secondary area near road], (TR34), iii.2013, coll. T. Robillard (UPLBMNH). Paratypes (1 male, 5 females). Philippines. same information as HT and AT (MNHN-EO-ENSIF-3162-3166). [examined] Other material examined. Philippines. [Mindanao]: [Surigao del Norte]: Siargao Island, Del Carmen, Brgy. Esperanza, 1♂ (JBB139), 01-05.x.2016, coll. S.A. Yap (UPLBMNH), right middle leg for molecular analysis (N40); Del Carmen, Brgy. Katipunan, 2♀, 07.iv.2018, rainy, coll. J.B. Baroga-Barbecho, S.A. Yap, M.K. Tan, & H. Yeo (UPLBMNH, ZRC); 2♀ (Siargao18_44, 84), 14-17.x.2018, coll. M.K.Tan, J.B. Baroga-Barbecho & S.A. Yap (ZRC). [Mindanao Island: Agusan del Sur]: Casawangan Lake, 1♂ (JBB363), 22.vii.2015, coll. N.M. Barbecho (NMP 13579), left middle leg for molecular analysis (N39). Type locality. Philippines. Visayas, Leyte Island, Burauen, Barangay Villa Corazon, 10°57'50.5"N 124°46' 35.3"E, 282 m. Distribution. Philippines. Visayas: Leyte Island; Mindanao: Siargao Island (new record); Mindanao Island: Agusan del Sur (new record).

  Paranisitra septentriaBaroga, Yap & Robillard, 2016: 100. Type material. Holotype male. Philippines. [Luzon Island]: [Ilocos Norte]: Adams, Mt. Pau, secondary forest, N18.45, E120.866, day, rainy, col. J. B. Baroga, S. A. Yap, V. Yngente & W. Bulalacao (JBB011), 30.xi-2.xii.2013, (UPLBMNH ORT-01097). [examined] Additional material examined. Philippines. [Luzon Island]: [Ifugao]: Mayoyao, Mt.[Mountain] Province, 1♂ (JBB384), 10.viii.1966, coll. H.M. Torrevillas (BPBM). Bunhian, Jacmal, 24 km E Mayoyao, 800-1000 m, 1♀ (JBB385), 4-6.v.1967, coll. H.M. Torrevillas (BPBM). Type locality. Philippines, Luzon Island, Ilocos Norte, Adams, Mt. Pau. Distribution. Philippines. Luzon Island: Ilocos Norte: Mt. Pau; Mountain Province, Ifugao (new records).

  Holotype male. Philippines. [Mindanao Island]: [Zamboanga del Sur]: 32 km NW of Milbuk, rain forest, light trap, 900 m, 6.viii.1958 (JBB386), coll. H.E. Milliron (BPBM). Allotype female. Philippines. [Mindanao Island]: [Misamis Occidental]: Don Victoriano, Lake Duminagat, 1395 asl, Agro-ecosystem, grass dominated, 17.xi.2003 (JBB243), coll. M.C. Espallardo, left fore leg for molecular analysis (N38) (IWEP-Taxo. Lab-02989). Paratypes. Philippines. [Mindanao Island]: [Misamis Oriental]: Mt. Kibungol, 20 km SE of Gingoog, 700-800 m, 1♀ (JBB390), 2♀(JBB392-JBB393),9-18.iv.1960, coll. W. Torrevillas (BPBM). Minalwang, 1050 m, 1♀ (JBB394),24.iii.4.iv.1961, coll. W Torrevillas (BPBM). Minubatan, 1050-1200 m, 1♀ (JBB395),5-9.iv.1961, coll. H Torrevillas (BPBM). Mt. Empagatao, 1050-1200 m, 3♀ (JBB396-JBB398),19-30.iv.1961, coll. H Torrevillas (BPBM). [Zamboanga del Sur]: 24 km NW of Milbuk nr. Lebak, 450-900 m, 1♀ (JBB387), 6-7.viii.1958, coll. H. E. Milliron (BPBM). [Agusan]: San Francisco 10 km, SE, 1♀ (JBB388), 15.xi.1959, coll. C.M. Yoshimoto (BPBM). Dos Arcos, 1♀ (JBB389) 19-23.xi.1959, coll. C. M. Yoshimoto (BPBM). Mindanao, 1♀, coll. Br.v.W. [Brunner von Wattenwyl], 21.181, identified Paranisitra longipes by T. Robillard (MNHN-EO-ENSIF648). Other material examined. Philippines. [Mindanao]: [Surigao del Norte]: Siargao Island, Del Carmen: Brgy. Esperanza, 01-05.x.2016, 2 ♀ (JBB140,142), coll. S.A. Yap; left middle leg of JBB140 for molecular analysis (N41) (UPLBMNH), 1♀ (JBB141) (MNHN-EO-ENSIF1548), 2♂ 6♀ (UPLBMNH). Brgy. Katipunan N9.87637 E126.00936, 34.5±4.7 m.a.s.l, 07.iv.2018, rainy, coll. J.B. Baroga-Barbecho, S.A. Yap, M.K. Tan, & H. Yeo, 1 ♂ (UPLBMNH), 1♂ (Siargao18_04), 1♀ (Siargao18_21) (ZRC), 1♀, molecular sample N50 (MNHN-EO-ENSIF4123). Brgy. Sayak N9.86026 E126.03296, 51.1 ± 6.4 m.a.s.l, 08.iv.2018, rainy, coll. J.B. Baroga-Barbecho, S.A. Yap, M.K. Tan, & H. Yeo, 3 ♂, 1 ♀ (UPLBMNH), 1 ♂, molecular sample N51 (MNHN-EO-ENSIF4121), 1♀ (MNHN), 1♀ (Siargao18_29) (ZRC). [Mindanao Island]: [Misamis Oriental]: Mt. Kibungol, 20 km SE of Gingoog, 700-800 m, 1 juvenile (JBB391), 9-18.iv.1960, coll. W. Torrevillas (BPBM). Type locality. Philippines, Mindanao Island: Zamboanga del Sur. Distribution. Philippines, Mindanao: Mindanao Island, Zamboanga Peninsula,Siargao Island.

Figures:Figure 1 .

 1 Figures:

Figure 2 .

 2 Figure 2. Paranisitra septentria Baroga, Yap & Robillard, 2016. Female in dorsal (A) and lateral (B)views. Female copulatory papilla in ventral (C) and lateral (D) views. Scale bars: A-B, 1 cm; C-D, 0.5 mm.

Figure 3 .

 3 Figure 3. Paranisitra flavofacia n. sp. Male holotype in dorsal (A) and lateral (B) views; fFemale allotype in dorsal (C) and lateral (D) views. h. Scale bar: 1 cm.

Figure 4 .

 4 Figure 4. Paranisitra flavofacia n. sp. head in dorsal (A), facial (B) and lateral (C) views. Scale bar: 1 mm.

Figure 5 .

 5 Figure 5. Paranisitra flavofacia n. sp. Male genitalia in dorsal (A), ventral (B), and lateral (C) views. Female female copulatory papilla in ventral (D) and lateral (E) views. Scale bars: A-C, 1 mm; D-E, 0.5 mm.

Figure 6 .

 6 Figure 6. Paranisitra flavofacia n. sp. Male (A) and female (B) in natural habitat at night on vegetation along the trail in Del Carmen, Siargao. Photo by M-K Tan.

Figure 7 .

 7 Figure 7. Paranisitra flavofacia n. sp. Male and female mating during night on vegetation along the trail in Del Carmen, Siargao. Photo by M-K Tan.

Figure 8 .

 8 Figure 8. A Paranisitra found in Siargao with unique colour patterns different from that of the adult Paranisitra flavofacia n. sp. and Paranisitra leytensis Robillard, 2015. As the specimen was not collected and we could not have a view of the face, the species could not be identified.

Fig. 2

 2 Fig.2

Fig. 3 Fig

 3 Fig.3

Fig. 5 Fig

 5 Fig.5

Other material examined. Philippines. Luzon [Island]: Laguna: Los Baños, Mt.

	(JBB244), 02.iv.2016, coll. J.E. Umerez (IWEP Taxo Lab-30231); 1♂ (JBB274),
	26.xii.1985, coll. E. Oratai (UPLBMNH ORT-01117). UPLB, Pook ni Mariang Makiling,
	1♀ (JBB178), 18.iii.2014, coll. I.J. Llanto (UPLBMNH). Los Baños: 50 m, 1♀
	(JBB287), 12.v.1964, 1♂ (JBB282), 13.v.1964, coll. R.R. Reyes (UPLBMNH ORT-
	01130, 01125); 1♂ juvenile (JBB288), 28.ii.1976, coll. H. Baguinon (UPLBMNH ORT-
	01131); 1♂ (JBB236), 25.i.2014, coll. Saranya S. (IWEP Taxo Lab-02981). Dampalit
	Falls, 1♀ (JBB179), 11.i.2014, coll. J.B. Baroga (UPLBMNH). Cavinti: cave, 3♂
	description). Gorochov 2009: 436
	(redescription); Robillard & Desutter-Grandcolas 2004: 658 (morphological phylogeny),
	2006 (molecular phylogeny), 2008: 67 (classification); Nattier et al. 2011 (molecular
	phylogeny); Robillard & Yap 2015: 85; Baroga et al. 2016: 94 (taxonomic key); Vicente
	et al. 2017: 2203 (biogeography).
	Type material. Holotype male. Philippines. [Luzon Island, Laguna], Mt. Makiling,
	Baker, coll. Karny, 1266 (MNHN-EO-ENSIF1192) [examined]. Allotype female.
	Philippines. same information as HT (MNHN-EO-ENSIF446) [examined].
	Makiling: 1♂ (JBB238), 15.ii.2014, coll. A.K. Amarga & E.C. Rubico (IWEP Taxo Lab-
	02978); 2♀ (JBB019-020), 3♂ (JBB021-023), 15-16.ii.2014, coll. N.M. Barbecho
	(UPLBMNH); 1♀ (JBB014), 06.i.2014, coll. J.B. Baroga (UPLBMNH); 1♀ (JBB109),
	15.viii.2014, coll. J.B. Baroga (UPLBMNH); Plot 3, 1♀ (JBB144), 20.v.2015, coll. N.M.
	Barbecho (UPLBMNH); 300 m, 1♂ (JBB281), 19.v.1904, coll. S. Rosario (UPLBMNH
	ORT-01124); 3500 m, 1♂ (JBB283), 3500 ft, 1♀ (JBB285), 26.iv.1958, coll. C.E.
	Mendoza (UPLBMNH ORT-01126, 01128), 1500 ft, 1♀ (JBB286), 26.iv.1958, coll.
	B.D. Morallo (UPLBMNH ORT-01129). Mount Makiling, base, 14°09'12,9''N
	121°14'05,0''E, 168 m, 2011-2014, T. Robillard, 5♀, 7♂, 9 juveniles (MNHN); Los
	Banos, Laguna, Mount Makiling, base, Aguila base, to summit #2, 14°08'15,2"N
	121°11'47,7"E, 2011-2014, 1♂, 4♀, 1 juvenile (MNHN). Makiling Botanic Garden: 1♀

Table 2 .

 2 Measurements of Paranisitra flavofacia n. sp. Abbreviations, see materials and methods.

Table 3 .

 3 Checklist of Paranisitra species.

Table 1 .

 1 Measurement of female Paranisitra septentria. Abbreviations, see materials and methods.

		PronL PronW FWL	FWW	HWT	FIIIL	FIIIW	TIIIL
	Female (n=1)	19	42		-	-	-		128	27	129
	(mean)	19	42		-	-	-		128	27	129
		Ias	Ibs	TIIIs	Oas	Obs	Int	TaIIIs	Ext	ST (n= )	OL
	Female (n=1)	14	11		19	9	1			6	-	113
	(mean)	14	11		19	9	1			6	-	113

Table 2 .

 2 Measurements of Paranisitra flavofacia n. sp. Abbreviations, see materials and methods.

		PronL PronW FWL	FWW	HWT	FIIIL	FIIIW	TIIIL
	Male holotype	21	39		-	-	-		148	30	149
	Female allotype	22	31		-	-	-		149	30	145
	Females (n=6)	19-28	31-49	-	-	-		133-168	30-39	145-171
	(mean)	21	40		-	-	-		157	34	162
		Ias	Ibs	TIIIs	Oas	Obs	Int	TaIIIs	Ext	ST (n= )	OL
	Male holotype	18	10		27	10	0			0	-	-
	Female allotype	15	10		24	9	1			7	-	126
	Females (n=6)	11-16	7-14	22-28	7-11	0-1		5-10	-	98-139
	(mean)	13	10		25	9	1			6	-	124

Table 3 .

 3 Checklist of Paranisitra species.

	TRIBE	GENUS AND SPECIES	TYPE LOCALITY (TL) AND	REPOSITORY
			DISTRIBUTION

Acknowledgements

We are grateful to the Department of Science and Technology-Accelerated Science and Technology Human Resource Development Program (DOST-ASTHRDP) for J.B. Baroga-Barbecho's MS scholarship and to UP-Continuous Operational and Outcomes-based Partnership for Excellence in Research and Academic Training Enhancement (COOP-ERATE) Program for the travel grant, which allow us to further examine the specimens at MNHN, Paris France. We also thank the MODECERA project, funded by the DOST-Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development (PCAARRD) for allowing us to study the collected specimens from Siargao, Mr. Roel Ruzol, the SSCT-Del Carmen Campus especially Ms. Deny Comon, Mr. Jonathan Litang, Mr. Carlos Donoso, and their students for accommodating and accompanying us during fieldworks in Siargao Island. To the Lady Yuen Peng McNeice Graduate Fellowship and Orthoptera Species File Grant 2018 for the research grants that allow M.K. Tan to conduct fieldwork in the Philippines. To Mr. Normandy M. Barbecho of the National Museum of the Philippines for collecting the specimens in Agusan, and for the use of museum equipment and facilities, Ms. Huiqing Yeo for assistance in the collection in Siargao, and Simon Poulain (MNHN, France) for taking photos of the specimens.

P. Bishop Museum (BPBM), Muséum national