Veronika Koren: Coding of low-dimensional variables with spiking neural networks

BCCN Berlin / GRK 1589 / TU Berlin

Abstract

Spikes, extremely precise temporal signals, are believed to be the main mean of communication between neurons. However, it is at present unclear how can be the information, contained in spike timing, utilized for encoding of low-dimensional variables, that presumably guide animal's behavior. Based on work by Boerlin, Machens and Deneve (Boerlin et al. 2013), we derive a functional model of spiking neural activity that exploits information in spike timing. The model represents an arbitrary low-dimensional variable by tracking its inputs with its spiking activity, and a spike is produced whenever this improves the estimation of the input signal. Precise spike timing is a build-in feature of such a model, and is an alternative to bottom-up descriptions of neural dynamics. Coding functionality is based on a geometric description, where each neuron is attributed a coding weight that determines neuron's role for representation, computed at the network level. Coding weight determines how does the neuron weight its inputs, what is the effect of neuron's spike on connected neurons, as well as on the read-out of spiking activity. Even if many neurons share the same coding weight, and are therefore redundant in their coding function, the design of the network ensures that spiking activity is nevertheless efficient. We show that maximally efficient regime for coding coincides with asynchronous spiking, interspersed with occasional synchronized bursts, and show how recurrent and lateral connections generate these bursts.

In the rest of the thesis, we study decoding models on parallel spike trains in behaving monkey, performing a visual discrimination task on binary stimulus classes. While decision-making is traditionally studied with respect to the neural activity in high-level, decision-making areas, we instead decode correct choice behavior from the spiking activity in sensory areas V1 and V4. We show that a linear classifier on parallel spike counts predicts animal's behavior better than chance. From the classification model, we compute decoding weights, that tell what is the role of each neuron within the population for the classification task. We show that, in particular in V4, decoding weights allow various insights into the structure of pair-wise interactions and coupling of the activity of single neurons with the population. First, we show that in V4, neurons with strong weights are more strongly coupled, synchronized and correlated than uninformative neurons. Second, we show that coupling, synchrony and correlations are stronger between neurons with the same sign of the weight compared to others. Finally, we show that correlations between neurons with the same sign of the weight decrease the performance of the decoder.

We proceed by building a biologically interpretable model of the read-out of parallel spike trains in single trials. We compute the synaptic current of a read-out neuron that receives synaptic inputs from a population of projecting neurons. We assume that spikes are weighted by a vector of decoding weights, where decoding weights reflect the role of each neuron for the computation at the network level. Resulting signal allows to predict the choice behavior of the animal, while simpler methods as the population PSTH entirely fail to do so. Disentangling superficial, middle and deep layer of the cortex, we show that in both V1 and V4, superficial layers are the most important for discrimination. We also show that the read-out signal of neurons with positive and negative weights is negatively correlated.

During the experiment, the animal is rewarded for correct behavior. The representation of the behavioral choice, however, must also take place when the choice is incorrect. We ask whether the decoding model, trained in the presence of the information on both stimulus and choice (e.g., correct choice), generalizes to decoding in the context of choice alone. We show that such generalization takes place in V1, but fails in V4. In V1, in particular, the choice signal can be discriminated during the second half of the trial. Similarly to decoding stimulus and choice, the choice signal is the strongest in the superficial layer of the cortex and the read-out of neurons with positive and negative weights is negatively correlated. In contrast to decoding of stimulus and choice, decoding of choice requires the information on spike timing. In general, these results show the similarity of representation of stimulus classes and corresponding behavioral choices in the primary visual cortex of the macaque.

 

Keywords: Coding; stimulus; choice; behavior; computation; representation; classification; information; parallel spike trains; spiking neural network; population code; visual cortex; macaque; V1; V4; transfer learning; read-out; prediction

Additional Information

PhD defense in the research and training group GRK 1589, 'Sensory Computation in Neural Systems'.

Organized by

Klaus Obermayer / Lisa Velenosi

Location: TU Berlin, room MAR 6.004, Marchstr. 23, 10587 Berlin

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