Biology subjects

Koren, V.

Publications and source records attributed to Koren, V..

6 recordsLinked to original sources

bioRxiv2024

Spiking networks that efficiently process dynamic sensory features explain receptor information mixing in somatosensory cortex

How do biological neural systems efficiently encode, transform and propagate information between the sensory periphery and the sensory cortex about sensory features evolving at different time scales? Are these computations efficient in normative information processing terms? While previous work has suggested that biologically plausible models of of such neural information processing may be implemented efficiently within a single processing layer, how such computations extend across several processing layers is less clear. Here, we model propagation of multiple time-varying sensory features across a sensory pathway, by extending the theory of efficient coding with spikes to efficient encoding, transformation and transmission of sensory signals. These computations are optimally realized by a multilayer spiking network with feedforward networks of spiking neurons (receptor layer) and recurrent excitatory-inhibitory networks of generalized leaky integrate-and-fire neurons (recurrent layers). Our model efficiently realizes a broad class of feature transformations, including positive and negative interaction across features, through specific and biologically plausible structures of feedforward connectivity. We find that mixing of sensory features in the activity of single neurons is beneficial because it lowers the metabolic cost at the network level. We apply the model to the somatosensory pathway by constraining it with parameters measured empirically and include in its last node, analogous to the primary somatosensory cortex (S1), two types of inhibitory neurons: parvalbumin-positive neurons realizing lateral inhibition, and somatostatin-positive neurons realizing winner-take-all inhibition. By implementing a negative interaction across stimulus features, this model captures several intriguing empirical observations from the somatosensory system of the mouse, including a decrease of sustained responses from subcortical networks to S1, a non-linear effect of the knock-out of receptor neuron types on the activity in S1, and amplification of weak signals from sensory neurons across the pathway.

neuroscience↗

bioRxiv2024

Structure, dynamics, coding and optimal biophysical parameters of efficient excitatory-inhibitory spiking networks

The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically-plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.

neuroscience↗

bioRxiv2020

The intersection between the representation of the stimuli and the choice by neural ensembles in the primary visual cortex of the macaque.

Animals frequently make decisions based on sensory cues. In such a setting, the overlap in the information on the stimulus and on the choice is crucial for the formation of informed behavioral decisions. Yet, how the information on the stimulus and on the choice interact in the brain is poorly understood. Here, we study the representation of a binary decision variable in the primary visual cortex (V1) while macaque monkeys perform delayed match-to-sample task on naturalistic visual stimuli close to psychophysical threshold. Using population vectors, we demonstrate the overlap in decoding spaces on binary stimulus classes "match/non-match" and binary choices "same /different" of the animal. Leveraging this overlap, we use learning from the invariant information across the two classification problems to predict the choice of the animal as a time-dependent population signal. We show the importance of the across-neuron organization and the temporal structure of spike trains for the decision signal and suggest how noise correlations between neurons with similar decoding selectivity are helpful for the accumulation of the decision signal. Finally, we show that decision signal is primarily carried by bursting neurons in the superficial layers of the cortex. Author summaryV1 is necessary for normal visual processing and is known to process features of visual stimuli such as orientation, but whether V1 also encodes behavioral decisions is an unresolved issue, with conflicting evidence. Here, we demonstrate that V1 encodes a mixed variable that contains the information about the stimulus as well as about the choice. We learn the structure of population responses in trials pertaining to the variable "stimulus+choice", and apply the resulting population vectors to trials that differ only about the choice of the animal, but not about the stimulus class. Moreover, we learn structure of population responses on time-averaged data and then apply it on time-dependent (spiking) data. During the late phase of the trial, this procedure allows to predict the upcoming choice of the animal with a time-dependent population signal. The spiking signal of small neural population is sparse, and we hypothesize that positive correlations between neurons in the same decoding pool help the transmission of the decision-related information downstream. We find that noise correlations in the same decoding pool are significantly stronger than across coding pools, which corroborates our hypothesis on the benefit of noise correlations for the read-out of a time-dependent population signal.

neuroscience↗

bioRxiv2019

The structure of the population code in neural ensembles of V4 shapes pair-wise interactions on different time scales.

In visual areas of primates, neurons activate in parallel while the animal is engaged in a behavioral task. In this study, we examine the structure of the population code while the animal performs delayed match to sample task on complex natural images. The macaque monkeys visualized two consecutive stimuli that were either the same or different, while recorded with laminar arrays across the cortical depth in cortical areas V1 and V4. We decoded correct choice behavior from neural populations of simultaneously recorded units. Utilizing decoding weights, we divide neurons in most informative and less informative, and show that most informative neurons in V4, but not in V1, are more strongly synchronized, coupled and correlated than less informative neurons. As neurons are divided in two coding pools according to their coding preference, in V4, but not in V1, spiking synchrony, coupling and correlations within the coding pool are stronger than across coding pools. HighlightsO_LIIn a match-to-sample visual task, responses of neural populations in V1 and in V4 predict the stimulus class better than chance. C_LIO_LIIn V4, informative neurons are more strongly coupled, correlated and synchronized than less informative neurons. C_LIO_LIIn V4, neurons are more strongly coupled, correlated and synchronized within coding pools compared to across coding pools. C_LIO_LICorrelations within coding pools harm the performance of the classifier in both V1 and V4. C_LI

neuroscience↗

bioRxiv2019

Reading-out task variables as a low-dimensional reconstruction of neural spike trains in single trials.

We propose a new model of the read-out of spike trains that exploits the multivariate structure of responses of neural ensembles. Assuming the point of view of a read-out neuron that receives synaptic inputs from a population of projecting neurons, synaptic inputs are weighted with a heterogeneous set of weights. We propose that synaptic weights reflect the role of each neuron within the population for the computational task that the network has to solve. In our case, the computational task is discrimination of binary classes of stimuli, and weights are such as to maximize the discrimination capacity of the network. We compute synaptic weights as the feature weights of an optimal linear classifier. Once weights have been learned, they weight spike trains and allow to compute the post-synaptic current that modulates the spiking probability of the read-out unit in real time. We apply the model on parallel spike trains from V1 and V4 areas in the behaving monkey macaca mulatta, while the animal is engaged in a visual discrimination task with binary classes of stimuli. The read-out of spike trains with our model allows to discriminate the two classes of stimuli, while population PSTH entirely fails to do so. Splitting neurons in two subpopulations according to the sign of the weight, we show that population signals of the two functional subnetworks are negatively correlated. Disentangling the superficial, the middle and the deep layer of the cortex, we show that in both V1 and V4, superficial layers are the most important in discriminating binary classes of stimuli.

neuroscience↗

bioRxiv2018

Minimally invasive deep-brain imaging through a 50 μm-core multimode fibre.

Achieving optical access to deep-brain structures represents an important step towards the goal of understanding the mammalian central nervous system. The complex refractive index distribution within brain tissue introduces severe aberrations to long-distance light propagation thereby prohibiting image reconstruction using currently available non-invasive techniques. In an attempt to overcome this challenge endoscopic approaches have been adopted, principally in the form of fibre bundles or GRIN-lens based endoscopes. Unfortunately, these approaches create substantial mechanical lesions of the tissue precipitating neuropathological responses that include inflammation and gliosis. Together, lesions and the associated neuropathology may compromise neural circuit performance. By replacing Fourier-based image relay with a holographic approach, we have been able to reduce the volume of tissue lesion by more than 100-fold, while preserving diffraction-limited imaging performance. Here we demonstrate high-resolution fluorescence imaging of neuronal structures, dendrites and synaptic specialisations, in deep-brain regions of living mice. These results represent a major breakthrough in the compromise between high-resolution imaging and tissue damage, heralding new possibilities for deep-brain imaging in vivo.

neuroscience↗