Biology subjects

Del Giudice, P.

Publications and source records attributed to Del Giudice, P..

3 recordsLinked to original sources

bioRxiv2021

Signal neutrality, scalar property, and collapsing boundaries as consequences of a learned multi-time scale strategy

We postulate that three fundamental elements underlie a decision making process: perception of time passing, information processing in multiple timescales and reward maximisation. We build a simple reinforcement learning agent upon these principles that we train on a random dot-like task. Our results, similar to the experimental data, demonstrate three emerging signatures. (1) Signal neutrality: insensitivity to the signal coherence in the interval preceding the decision. (2) Scalar property: the mean of the response times varies widely for different signal coherences, yet the shape of the distributions stays almost unchanged. (3) Collapsing boundaries: the "effective" decision-making boundary changes over time in a manner reminiscent of the theoretical optimal. Removing the perception of time or the multiple timescales from the model does not preserve the distinguishing signatures. Our results suggest an alternative explanation for signal neutrality. We propose that it is not part of motor planning. It is part of the decision-making process and emerges from information processing on multiple timescales.

neuroscience↗

bioRxiv2018

Spontaneous activity emerging from an inferred network model captures complex temporal dynamics of spiking data

Inference methods are widely used to recover effective models from observed data. However, few studies attempted to investigate the dynamics of inferred models in neuroscience, and none, to our knowledge, at the network level. We introduce a principled modification of a widely used generalized linear model (GLM), and learn its structural and dynamic parameters from in-vitro spike data. The spontaneous activity of the new model captures prominent features of the non-stationary and non-linear dynamics displayed by the biological network, where the reference GLM largely fails, and also reflects fine-grained spatio-temporal dynamical features. Two ingredients were key for success. The first is a saturating transfer function: beyond its biological plausibility, it limits the neurons information transfer, improving robustness against endogenous and external noise. The second is a super-Poisson spikes generative mechanism; it accounts for the undersampling of the network, and allows the model neuron to flexibly incorporate the observed activity fluctuations.

neuroscience↗

bioRxiv2016

Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis

Simultaneous recordings from N electrodes generate N-dimensional time series that call for efficient representations to expose relevant aspects of the underlying dynamics.\n\nBinning the time series defines a sequence of neural activity vectors that populate the N-dimensional space as a density distribution, especially informative when the neural dynamics proceeds as a noisy path through metastable states (often a case of interest in neuroscience); this makes clustering in the N-dimensional space a natural choice.\n\nWe apply a variant of the mean-shift algorithm to perform such clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memory attractors.\n\nThe neural states identified as clusters centroids are then used to define a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities.\n\nWe next consider the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects; we develop a procedure, inspired by Boltzmann learning but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations.\n\nAfter clustering the activity generated by such multi-modular spiking networks, we cast their multi-dimensional dynamics in the form of the symbolic sequence of the clusters centroids; this representation naturally lends itself to complexity estimates that provide compact information on memory effects like those induced by spike-frequency adaptation. Specifically, to obtain a relative complexity measure we compare the Lempel-Ziv complexity of the actual centroid sequence to the one of Markov processes sharing the same transition probabilities between centroids; as an illustration, we show that the dependence of such relative complexity on the characteristic time scale of spike-frequency adaptation.

neuroscience↗