<figure><img src="image_111.png" /></figure>

<figure><img src="image_112.png" /></figure>

The model describes the neural activity within a cortical area that receives top-down modulatory and bottom-up sensory inputs from higher-order areas and thalamic projections, respectively. The activity of excitatory neurons is simulated using the leaky integrate-and-fire model. Excitatory synaptic connections are modified on shorter and longer time-scales by short-term depression and spike-timing dependent plasticity, respectively. Sequential patterns are stored within the recurrent connections of the middle area by repeated presentation of the external stimuli.

Figure 103A1–C1, A2–C2 show that only the first and second stored sequences are replayed when the first and second top-down input is active, respectively, although the bottom-up inputs trigger the starting neurons of both sequential patterns. When there is no top-down input, the spontaneous activity is composed of the time-compressed superposition of both sequential patterns (see Fig. 103A4–C4).

**Acknowledgements:** This research was supported by ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan).

## References

1. Luczak A, Bartho P, Harris KD. Gating of sensory input by spontaneous cortical activity. J Neurosci. 2013;33(4):1684–95.

2. Leleu T, Aihara K. Unambiguous reconstruction of network structure using avalanche dynamics. Phys Rev E. 2015;91:022804.

## Q28

**An auto-encoder network realizes sparse features under the influence of desynchronized vascular dynamics**

Ryan T. Philips¹, Karishma Chhabria¹, V.Srinivasa Chakravarthy¹
¹Department of Biotechnology, Indian Institute of Technology, Madras, Chennai 600036, India

**Correspondence:** V. Srinivasa Chakravarthy - schakra@iitm.ac.in

BMC Neuroscience 2016, 17(Suppl 1):Q28

Please note that this abstract was presented at the previous year's 24th Annual Computational Neuroscience Meeting: CNS-2015.

Cerebral vascular dynamics are generally thought to be controlled by neural activity in a unidirectional fashion. However, both computational modeling and experimental evidence points to the feedback effects of vascular activity on neural dynamics [1, 2]. Vascular feedback in the form of glucose and oxygen controls neuronal ATP, which in turn can control the threshold of neural firing. We present a computational model of a neuro-vascular system in which a network of 'vascular units' supply 'energy' to a neural network (NN), which reduces neural firing threshold. The vascular network (VN) is modeled by a network of oscillators as in [3]. Neuronal pools fed by the complex dynamics of VN are turned ON and OFF randomly. We show that such a feedback mechanism results in sparse weight matrix, thereby enhancing the performance of an auto-encoder NN.

In the proposed NN model, the hidden layer is coupled to a vascular network in a one-to-one fashion (Fig. 104A) and is trained using backpropagation. The cross-entropy (ce) error measure is used to update the energy demand parameter ($M_d$) which is fed back to the VN. $M_d$ in turn governs the state of the vascular units, which determines if the neuron should be turned ON/OFF. This paradigm of randomly turning neural units ON/OFF is adapted from [4]. High $M_d$ results in an increase in ce as the neuronal dropout level is too low; similarly low $M_d$ results in an increase in ce due to high dropout. The time scale of vascular dynamics ($M_d$) is much longer than that of neural dynamics (ce), reflecting physiology. The network was trained on two datasets: overlapping bar patterns and MNIST data. The settled weight matrices corresponding to both the synchronized (Fig. 104B) and desynchronized vascular dynamics (Fig. 104C) are shown in the Fig. 104B1, B2, C1, C2, respectively. Our earlier modeling study highlighted the link between desynchronized vascular dynamics and efficient energy delivery in skeletal muscle [3]. We now show that desynchronized vascular dynamics leads to efficient training in an auto-encoder NN.

## References

1. Chander BS, Chakravarthy VS. A computational model of neuro-gliovascular loop interactions. PloS One. 2012;7(11):e48802.

2. Moore CI, Cao R: The hemo-neural hypothesis: on the role of blood flow in information processing. J Neurophysiol. 2008;99(5):2035.

3. Pradhan RK, Chakravarthy V, Prabhakar A. Effect of chaotic vasomotion in skeletal muscle on tissue oxygenation. Microvasc Res. 2007;74(1):51–64.

4. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15(1):1929–58.